Emperor Penguin Optimizer (EPO) is a metaheuristic algorithm which is recently developed and illustrates the emperor penguin’s huddling behaviour. However, the original version of the EPO will fix ...issues that are continuing in fact but not discrete. The eight separate EPO variants have been provided in this article. Four transfer features, s-shaped and v-shaped, that are used in order to map the search space into a separate research space are considered in the proposed algorithm. The output of the proposed algorithm is validated using 25 standard benchmark functions. It also analyses the statistical sense of the proposed algorithm. Experimental findings and comparisons suggest that the proposed algorithm performs better than other algorithms. The solution also applies to the issue of feature selection. The findings reveal the supremacy of the binary emperor penguin optimization algorithm.
A BSTRACT Objective: The aim of study’s goal was to look into the anticancer efficacy of a methanolic extract of Justicia gendarussa against a lung cancer cell line. Materials and Methods: Cell ...viability assays and cell and nuclear morphology examinations were used to evaluate the anticancer efficacy against methanolic extract of Justicia gendarussa on lung cancer cell lines. The IC50 doses were calculated using different concentrations of Justicia gendarussa extract (0, 10, 20, 40, 60, and 80 μg/mL). Results: The results of MTT (3-4,5-dimethylthiazol-2-yl-2,5-diphenyl-tetrazolium bromide) assay revealed that the percentage of viability in treated cells was significantly lower as compared with untreated control groups, which represented as 100%, and an inhibitory concentration of 40 μg/mL was observed. Under a phase-contrast microscope, morphological changes revealed cell shrinkage and cytoplasmic membrane blebbing. The apoptotic nuclei (intensely colored, broken nuclei, and compacted chromatin) were examined under a fluorescence microscope. Conclusions: The outcome of the research work on Justicia gendarussa was investigated for anticancer properties. The results revealed the proapoptotic and cytotoxic effects of Justicia gendarussa extract on lung cancer cell lines. From the above results and findings, it could be concluded that the Justicia gendarussa methanolic leaf extract exhibited potent anticancer activity against a lung cancer cell line. Further study needs to be conducted to investigate the active chemicals in the extract as well as the molecular mechanisms underlying its anticancer benefits.
Since the coronavirus (COVID-19) outbreak keeps on spreading all through the world, scientists have been crafting varied technologies mainly focusing on AI for an approach to acknowledge the ...difficulties of the epidemic. In this current worldwide emergency, the clinical business is searching for new advancements to screen and combat COVID-19 contamination. Strategies used by artificial intelligence can stretch screen the spread of the infection, distinguish highly infected patients, and be compelling in supervising the illness continuously. The artificial intelligence anticipation can further be used for passing dangers by sufficiently dissecting information from past sufferers. International patient support with recommendations for population testing, medical care, notification, and infection control can help fight this deadly virus. We proposed the hybrid deep learning method to diagnose COVID-19. The layered approach is used here to measure the symptom level of the patients and to analyze the patient image data whether he/she is positive with COVID-19. This work utilizes smart AI techniques to predict and diagnose the coronavirus rapidly by the Oura smart ring within 24 h. In the laboratory, a coronavirus rapid test is prepared with the help of a deep learning model using the RNN and CNN algorithms to diagnose the coronavirus rapidly and accurately. The result shows the value 0 or 1. The result 1 indicates the person is affected with coronavirus and the result 0 indicates the person is not affected with coronavirus. X-Ray and CT image classifications are considered here so that the threshold value is utilized for identifying an individual's health condition from the initial stage to a severe stage. Threshold value 0.5 is used to identify coronavirus initial stage condition and 1 is used to identify the coronavirus severe condition of the patient. The proposed methods are utilized for four weighting parameters to reduce both false positive and false negative image classification results for rapid and accurate diagnosis of COVID-19.
Hyperspectral images (HSI) are adjacent band images commonly used in remote sensing environment; the deep learning methodologies have the important feature for classification process. Additionally, ...the highest dimensionality of HSI enhances the computational complexity which affects the overall performance. Hence, the dimensionality reduction plays a vital role to enhance the performance while processing the Hyperspectral images. The HSI is initially segmented into the pixels, it belongs to the similar correlation and it is optimized using the neural network framework. Auto Encoder based dimensionality reduction is proposed for performance enhancement that denoising removed. The reconstructed pixel using vectors and also identifying the reconstructing loss enhances the overall accuracy. The Convolutional Neural network framework implements the classification process for Hyperspectral images. The performance analysis results on the proposed technique have improved accuracy and performance compared to the related techniques.
Healthcare practices include collecting all kinds of patient data which would help the doctor correctly diagnose the health condition of the patient. These data could be simple symptoms observed by ...the subject, initial diagnosis by a physician or a detailed test result from a laboratory. Thus, these data are only utilized for analysis by a doctor who then ascertains the disease using his/her personal medical expertise. The artificial intelligence has been used with Naive Bayes classification and random forest classification algorithm to classify many disease datasets like diabetes, heart disease, and cancer to check whether the patient is affected by that disease or not. A performance analysis of the disease data for both algorithms is calculated and compared. The results of the simulations show the effectiveness of the classification techniques on a dataset, as well as the nature and complexity of the dataset used.
From the past few decades many nature inspired algorithms have been developed and gaining more popularity because of their effectiveness in solving problems of distinct application domains. ...Undoubtedly, Particle swarm optimization (PSO) algorithm is the most successful optimization algorithm among the available nature inspired algorithms such as simulated annealing, genetic algorithm, differential evolution, firefly, cuckoo etc., because of its high efficiency and capability to adjust in different dynamic environments. This year marks its 25th anniversary of PSO, one of the base inspirations for many modern-day metaheuristics development. Because of its simple structure and few number of algorithmic parameters, PSO from its origin has acquired widespread popularity amongst researchers, technocrats and practitioners and has been proven to provide better performance in various functional areas such as networking, robotics, image segmentation, power generation and controlling, fuzzy systems and so on. PSO is a population based global heuristic optimization approach motivated by the social behavior of animals chasing for food such as flock of birds, schools of fish. PSO attempts to stabilize exploration and exploitation by combining local search capabilities with global search capabilities. In this article, an in-depth analysis of PSO with its developments from 1995 to 2020 has been presented. Mainly, the improved variants of PSO along with solvable application areas are discussed in detail to provide a scope for the further development. At the end of the paper, the growth of the PSO in various application areas has been presented with factual representation. The main motive of this survey is to inspire the researchers, practitioners and technocrats to develop improved and innovative solutions for solving complex problems in various domains using PSO.
The Internet of Medical Things (IoMT) is a kind of associated smart-medical device infrastructure with applications, health services, and systems. These medical devices and applications are linked ...via the Internet to healthcare systems. The privacy and security for patient data, scalability, and data accessibility are the most complex IoT challenges (particularly in IoMT) and need to be considered. Blockchain can disrupt the current modes of patient data access, exchange, accumulation, control, and contribution. Hence, in this study, blockchain-assisted secure data management framework (BSDMF) has been suggested for health information based on the Internet of Medical Things to securely exchange patient data and enhance scalability and data accessibility healthcare environment. The proposed BSDMF provides secure data management between personal servers and implantable medical devices and between cloud servers and personal servers. The IoMT-based security framework utilizes blockchain to guarantee data transmission security and data management between linked nodes. The experimental results show that the suggested BSDMF method achieves a high accuracy ratio of 97.2%, a precision ratio of 97.9%, an average trust value of 98.3%, and less response time of 11.2%, and a latency ratio of 15.6% when compared to other popular methods.
Endocrine disrupting chemicals or carcinogens have been known for decades for their endocrine signal disruption. Endocrine disrupting chemicals are a serious concern and they have been included in ...the top priority toxicants and persistent organic pollutants. Therefore, researchers have been working for a long time to understand their mechanisms of interaction in different human organs. Several reports are available about the carcinogen potential of these chemicals. The presented review is an endeavor to understand the hazard identification associated with endocrine disrupting carcinogens in relation to the human body. The paper discusses the major endocrine disrupting carcinogens and their potency for carcinogenesis. It discusses human exposure, route of entry, carcinogenicity and mechanisms. In addition, the paper discusses the research gaps and bottlenecks associated with the research. Moreover, it discusses the limitations associated with the analytical techniques for detection of endocrine disrupting carcinogens.
Display omitted
•Types of endocrine disrupting chemicals (EDCs) related to cancer were analyzed.•EDCs associated cancers and their mechanisms were discussed.•Human exposure to EDCs was explored.•Assessment of EDCs carcinogenicity was performed.•Research gaps and future studies were suggested.
SUMMARY
Establishment of energy efficient and reliable data routing in wireless sensor network (WSN) is one of the most critical and challenging task in the recent days. Also, the overall performance ...and lifetime of WSN is highly depends on the energy level of sensor nodes, hence it is most essential to save the energy of network. For this purpose, the different types of clustering and data aggregation mechanisms are developed in the conventional works, which are focusing on improving both the energy conservation and lifetime of network. Yet, it facing the challenges of increased computational complexity, inefficient routing of data, high controlling overhead, and reduced reliability. Thus, the proposed work objects to develop a novel energy efficient mechanism by integrating the functionalities of advanced clustering, path selection, and data aggregation methodologies. Here, the spider monkey optimization based energy efficient routing protocol is developed for optimally selecting the cluster head (CH) based on certain parameters of energy, distance, and weight value. In this framework, the data transmission is performed between the source to destination nodes through the relay nodes and CHs, which helps to minimize the energy consumption of network. Then, the Classy Bellman‐Ford algorithm is deployed for identifying the best paths having shortest distance with the sink nodes. Consequently, an anticipated data aggregation mechanism is utilized for ensuring the security and reliability of data transmission in WSN. For evaluation assessment, various performance metrics have been utilized to validate the results of proposed methodology, and also the obtained values are compared with some other recent state‐of‐the‐art models for proving the betterment of proposed mechanism.
In this research, pure deterministic system has been established by a new Distributed Energy Efficient Clustering Protocol with Enhanced Threshold (DEECET) by clustering sensor nodes to originate the ...wireless sensor network. The DEECET is very dynamic, highly distributive, self-confessed and much energy efficient as compared to most of the other existing protocols. The MATLAB simulation provides aim proved result by means of energy dissipation being emulated in the networks lifespan for homogeneous as well as heterogeneous sensor network, which when contrasted for other traditional protocols. An enhanced result has been obtained for equitable energy dissipation for systematized networks using DEECET.
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