Energy Efficient MANET Protocol Using Cross Layer Design for Military Applications

In military applications mobile adhoc network plays very important role because it is specifically designed network for on demand requirement and in situations where set up of physical network is not possible. This special type of network which takes control in infrastructure less communication handles serious challenges tactfully such as highly robust and dynamic military work stations, devices and smaller sub-networks in the battle field. Therefore there is a high demand of designing efficient routing protocols ensuring security and reliability for successful transmission of highly sensitive and confidential military information in defence networks. With this objective, a power efficient network layer routing protocol in the network for military application is designed and simulated using a new cross layer approach of design to increase reliability and network lifetime up to a greater extent.

IoT-Fog-Edge-Cloud Computing Simulation Tools, A Systematic Review

The Internet of Things (IoT) perspective promises substantial advancements in sectors such as smart homes and infrastructure, smart health, smart environmental conditions, smart cities, energy, transportation and mobility, manufacturing and retail, farming, and so on. Cloud computing (CC) offers appealing computational and storage options; nevertheless, cloud-based explanations are frequently conveyed by downsides and constraints, such as energy consumption, latency, privacy, and bandwidth. To address the shortcomings related to CC, the advancements like Fog Computing (FC) and Edge Computing (EC) are introduced later on. FC is a novel and developing technology that connects the cloud to the network edges, allowing for decentrali zed computation. EC, in which processing and storage are performed nearer to where data is created, may be able to assist address these issues by satisfying particular needs such as low latency or lower energy use. This study provides a comprehensive overview and analysis of IoT-Fog-Edge-Cloud Computing simulation tools to assist researchers and developers in selecting the appropriate device for research studies while working through various scenarios and addressing current reality challenges. This study also takes a close look at various modeling tools, which are examined and contrasted to improve the future

FOHC: Firefly Optimizer Enabled Hybrid approach for Cancer Classification

Early detection and prediction of cancer, a group of chronic diseases responsible for a large number of deaths each year and a serious public health hazard, can lead to more effective treatment at an earlier stage in the disease's progression. In the current era, machine learning (ML) has widely been used to develop predictive models for incurable diseases such as cancer, heart disease, and diabetes, among others, taking into account both existing datasets and personally collected datasets, more research is still being conducted in this area. Using recursive feature elimination (RFE), principal component analysis (PCA), the Firefly Algorithm (FA), and a support vector machine (SVM) classifier, this study proposed a Firefly Optimizer-enabled Hybrid approach for Cancer classification (FOHC). This study considers feature selection and dimensionality reduction techniques RFE and PCA, and FA is used as the optimization algorithm. In the last stage, the SVM is applied to the pre-processed dataset as the classifier. To evaluate the proposed model, empirical analysis has been carried out on three different kinds of cancer disease datasets including Brain, Breast, and Lung cancer obtained from the UCI-ML warehouse. Based on the various performance parameters like accuracy, error rate, precision, recall, f-measure, etc., some experiments are carried out on the Jupyter platform using Python codes. This proposed model, FOHC, surpasses previous methods and other considered state-of-the-art works, with 98.94% accuracy for Breast cancer, 95.58% accuracy for Lung cancer, and 96.34% accuracy for Brain cancer. The outcomes of these experiments represent the effectiveness of the proposed work

Real-time validation of a novel IAOA technique-based offset hysteresis band current controller for grid-tied photovoltaic system

Renewable energy sources have power quality and stability issues despite having vast benefits when integrated with the utility grid. High currents and voltages are introduced during the disconnection or injection from or into the power system. Due to excessive inverter switching frequencies, distorted voltage waveforms and high distortions in the output current may be observed. Hence, advancing intelligent and robust optimization techniques along with advanced controllers is the need of the hour. Therefore, this article presents an improved arithmetic optimization algorithm and an offset hysteresis band current controller. Conventional hysteresis band current controllers (CHCCs) offer substantial advantages such as fast dynamic response, over-current, and robustness in response to impedance variations, but they suffer from variable switching frequency. The offset hysteresis band current controller utilizes the zero-crossing time of the current error for calculating the lower/upper hysteresis bands after the measurement of half of the error current period. The duty cycle and hysteresis bands are considered as design variables and are optimally designed by minimizing the current error and the switching frequency. It is observed that the proposed controller yields a minimum average switching frequency of 2.33 kHz and minimum average switching losses of 9.07 W in comparison to other suggested controllers. Results are validated using MATLAB/Simulink environment followed by real-time simulator OPAL-RT 4510.Web of Science1523art. no. 879

Early detection of Alzheimer's diseases through IoT

In this article, we discuss a biological explanation of Alzheimer's disease by using IoT-enabled devices. Alzheimer's disease has different stages depending on risk factors, and it has no current cure. Today, Alzheimer's disease is a prominent issue among researchers. In order to provide better treatment, the investigation is updated for improved understanding of Alzheimer's disease (AD). In this research, we classify IoT implemented data to recognize and identify stages of Alzheimer's patients. Wearable assistive IoT with complicated embedded artificial perception utilizing deep learning is being developed in this paper and also represents the largest comprehensive study of AD approaches with helping the neurologist to make a better diagnosis

A novel optimally tuned super twisting sliding mode controller for active and reactive power control in grid‐interfaced photovoltaic system

Abstract In photovoltaic (PV) systems, inverters play a crucial role for supplying electricity to meet the demand while maintaining power quality. For a local load connected to a grid‐interfaced photovoltaic (GIPV) system, active and reactive power control is necessary at the distribution level. Thus, the foremost purpose of this article is to get the best optimally designed robust controller for control of active and reactive power. A GIPV system with Improved Arithmetic Optimisation Algorithm (IAOA)‐based Super Twisting Sliding Mode Controller (ST‐SMC) methodology has been proposed in this article for active and reactive power management. The conventional PI controller in the GIPV system that is most frequently used has considerable undershoot and a long settling period. PI controller tuning parameters were also changed to account for the wide change in the reference pattern. Therefore, STSMC and SMC are used for ensuring robustness against external disturbances. The conventional SMC comes out to have a chattering issue. Furthermore, the proposed IAOA technique is validated through some benchmark functions. The proposed IAOA technique outperforms Particle Swarm Optimisation (PSO), Forensic Based Investigation (FBI), and Traditional Arithmetic Optimisation Algorithm (TAOA) in terms of the number of iterations and accurately achieving optimal solutions for active and reactive power control. The results show that the proposed IAOA‐based STSMC technique has an improved performance of settling time and undershoot for active and reactive power control. This article also presents stability analysis and robustness test of the above mentioned controllers to illustrate the effectiveness of each optimally designed controller. A 40 kW GIPV system performance is evaluated using the MATLAB environment, and the results are validated in a real‐time simulator platform OPAL‐RT 4510

CanDiag: Fog Empowered Transfer Deep Learning Based Approach for Cancer Diagnosis

Breast cancer poses the greatest long-term health risk to women worldwide, in both industrialized and developing nations. Early detection of breast cancer allows for treatment to begin before the disease has a chance to spread to other parts of the body. The Internet of Things (IoT) allows for automated analysis and classification of medical pictures, allowing for quicker and more effective data processing. Nevertheless, Fog computing principles should be used instead of Cloud computing concepts alone to provide rapid responses while still meeting the requirements for low latency, energy consumption, security, and privacy. In this paper, we present CanDiag, an approach to cancer diagnosis based on Transfer Deep Learning (TDL) that makes use of Fog computing. This paper details an automated, real-time approach to diagnosing breast cancer using deep learning (DL) and mammography pictures from the Mammographic Image Analysis Society (MIAS) library. To obtain better prediction results, transfer learning (TL) techniques such as GoogleNet, ResNet50, ResNet101, InceptionV3, AlexNet, VGG16, and VGG19 were combined with the well-known DL approach of the convolutional neural network (CNN). The feature reduction technique principal component analysis (PCA) and the classifier support vector machine (SVM) were also applied with these TDLs. Detailed simulations were run to assess seven performance and seven network metrics to prove the viability of the proposed approach. This study on an enormous dataset of mammography images categorized as normal and abnormal, respectively, achieved an accuracy, MCR, precision, sensitivity, specificity, f1-score, and MCC of 99.01%, 0.99%, 98.89%, 99.86%, 95.85%, 99.37%, and 97.02%, outperforming some previous studies based on mammography images. It can be shown from the trials that the inclusion of the Fog computing concepts empowers the system by reducing the load on centralized servers, increasing productivity, and maintaining the security and integrity of patient data

Excretion of SARS-CoV-2 in breast milk: a single-centre observational study

Background Breast feeding by SARS-CoV-2-infected mothers has been a concern because of the possibility of excretion of virus in breast milk.Objective To detect SARS-CoV-2 in expressed breast milk (EBM) of mothers infected with SARS-CoV-2 and clinical outcome of neonates delivered and breast fed by them.Design A single-centre, prospective observational study involving 50 SARS-CoV-2-infected mothers and their 51 neonates.Setting A tertiary care hospital in Eastern India.Participants SARS-CoV-2-infected mothers and neonates delivered by them.Main outcome measures We investigated the presence of SARS-CoV-2 in the breast milk of mothers, who tested positive for this virus in their nasopharyngeal swab (NPS). Clinical outcome was assessed in neonates breast fed by these mothers after 1 month of the postnatal period.Results 50 SARS-CoV-2-positive expectant mothers were enrolled for the study. One out of 51 neonates, who delivered through lower segment caesarean section at term gestation and tested SARS-CoV-2 negative, died due to severe birth asphyxia. One sample of EBM was collected from each of the 49 mothers within 4 days of delivery. All EBM samples tested negative for SARS-CoV-2 through real-time reverse transcriptase-PCR (RT-PCR). All the newborns were screened twice for presence of SARS-CoV-2 RNA in their NPS, by RT-PCR. 2 of 51 neonates had COVID-19 infection after 24 hours of life. Caregivers of 37 of 50 alive neonates responded to follow-up via telephone. Except for minor feed intolerance in one (1 of 37) neonate, all neonates were reported well after 1 month of their age.Conclusion All the samples of breast milk were negative for SARS-CoV-2. Most of the neonates remained asymptomatic on breast feeding, whose mothers had SARS-CoV-2 infection before delivery

Fungal Keratitis Due to Fusarium lichenicola: A Case Report and Global Review of Fusarium lichenicola Keratitis

Fusarium species are among the most commonly isolated causes of fungal keratitis. Most species of the genus Fusarium belong to Fusarium solani species complex (FSSC). Fusarium lichenicola, a member of the FSSC complex, is a well-established plant and human pathogen. However, reports of fungal keratitis due to Fusarium lichenicola have not been frequently reported. To the best of our knowledge, only twelve cases of Fusarium lichenicola keratitis have been reported in the past fifty years. Clinical cases of Fusarium lichenicola may have most likely been misidentified because of the lack of clinical and microbiological suspicion, as well as inadequate diagnostic facilities in many tropical countries where the burden of the disease may be the highest. We report a case of fungal keratitis caused by Fusarium lichenicola and present a global review of the literature of all cases of fungal keratitis caused by this potentially blinding fungus