In this study, the Exponential-type lifetime distribution which is often used in reliability testing as a continuous probability distribution for the elapsed time of software failure occurrence was applied to the NHPP model, and the reliability performance of the proposed model was newly compared and analyzed. For this, a solution for analyzing reliability performance was developed according to the order of the analysis algorithm presented in this study by utilizing the failure time data collected during the operation of the software system. In this process, the parameter estimation was solved by applying the maximum likelihood estimation (MLE), and the nonlinear equation was solved using the bisection method. In the criteria analysis for judging by comparing efficient models, the Exponential-exponential and Exponential-based models showed relatively good results. As a result of analyzing reliability performance, first, in the estimation power analysis on the real value using the mean value function, the Exponential-exponential model showed a slight overestimation error but showed excellent performance with the smallest error value. Second, in the analysis of the failure strength using the intensity function, the Exponential-exponential model was evaluated as an efficient model because the failure rate showed a large decrease with the smallest value. Third, in the reliability analysis, the Exponential-exponential model was evaluated as showing a higher and more stable reliability trend than other models whose reliability decreased as the mission time passed. In conclusion, it can be seen that the Exponential-exponential model is an efficient model with the best performance among the proposed models. Through this study, the Exponential-type distribution model was newly analyzed, and the related data is expected to be utilized as basic design data required for software developers.

In this study, laboratory model tests were performed to investigate the bearing strength of a circular footing resting on the foundation bed. Four types of foundation beds were used: homogeneous sand, homogeneous clay, layered soil (clay-sand), and layered soil with a geogrid reinforcement (clay-geogrid-sand). The geogrid was placed at the interface between the sand and clay layer. The undrained shear strength of clay varied from 7 kPa to 56 kPa. The relative density and the depth of the sand layer were kept constant throughout the tests. The laboratory model test findings show that the unreinforced layered soil improves footing pressure 3.23 times, while a geogrid reinforced layered soil improves footing pressure by 3.88 times as compared to homogenous clay beds. This study also proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS) model for predicting the footing pressure by utilizing the laboratory model test datasets. The number of membership functions and their types were changed to find the best model. Results show that the proposed ANFIS model can predict the footing strength of a geogrid reinforced foundation bed with greater accuracy (R² = 0.99855, RMSE = 0.0085). The optimal ANFIS model was found when three Gaussian membership functions were assigned to each input parameter.

In Ayurveda, the ancient medicinal plant identification system is based on the morphological comparison of leaf, fruit, flower, root, stem etc. Botanists use morphometrics for aquatic and semi-aquatic medicinal plants classification. However, deep learning networks provide the highest image classification result in digital image processing. Existing deep learning algorithms generate feature maps for pixel-wise image classification. In the feature map of deep learning output, most of the morphological features are missing. This issue leads to the Catastrophic forgetting issue of deep learning. To generate a traditional morphological feature-based medicinal plant identification system, we are introducing morphometrics and morphological feature-based deep learning networks for aquatic and semi-aquatic plant classification. This article contains: (a) A detailed morphological features database of aquatic and semi-aquatic medicinal plants, (b) a summary of the importance of the morphological features-based leaf classification, (c) a morphological features extraction algorithm and (d) the morphological features-based deep learning approach for aquatic and semi-aquatic plant classification. This human brain-like procedure achieved 97% classification accuracy and reduced the Catastrophic forgetting issue of continual learning.

There has been a substantial amount of online discussion regarding the COVID - 19 pandemic. This perpetual restoration transition due to coronavirus disease has led to discussions on communal, fiscal, emotional and mental gratification of human beings. The disease has transformed the physical mode of communication among people to an online mode completely, involving greater use of emojis. Emojis are used extensively with text to express health conditions, prayers, ambulance, soaps, police, danger, facial expressions, sentiments, etc. To the best of our familiarity, the proposed work focuses on the importance of emojis for expressing sentiments. In this paper, the online document of 650 personages consisting of 1, 68,548 (what is this number?) tweets without COVID - 19 terminologies and 67,819 tweets dataset sample of COVID - 19 terminologies have been explored. The portrayal of emotions via emojis plays an important role. This promoted us to contribute a framework to categorize the numerous social media documents in to major categories: ‘Online Document Content Class, ODCC’ i.e., categorizing the different social media documents as text only, emoji only, and both text and emoji. This classification has been strengthened by analyzing the tweets of three major regions across the world. The hypothesis testing identifies the significance of emojis in the sentiment analysis during COVID along with superior productive way of tweets categorization. The results illustrate that online users of European region used the emojis most. The proposed approach presents precision of social media expression sentiment score and polarity evaluation is completely accomplished by considering text with emojis as well.

In the medical field, the intensifying use of Artificial Intelligence and Machine Learning tends to lower the time and increase accuracy in detecting illnesses in their early stages. AI will have a 10.4 percent influence on the Indian economy in 2030, amounting to 0.9 trillion dollars. More than 80 anomalies in a child's fetal development have been discovered today. As it takes more time to manually identify the lesion tissues in the fetal brain, this work proposes an automatic detection and classification of fetal brain abnormalities using a cloud environment. The art of finding the abnormalities in the fetal brain is the core objective of the system, which eradicates or reduces the time and cost and improves the accuracy. The Machine Learning algorithm is introduced in fetal MRI scans to discover and pinpoint fetal brain anomalies. The Precise Epic Localization Algorithm is adopted in YOLO v4 architecture to detect and classify the healthy fetal brain with its orientation and unhealthy brain with their abnormalities from the given input of MRI Images. In this proposed work, the detection and classification of Encephalocele and Arteriovenous Malformation from a fetal brain MRI are obtained and evaluated using a machine learning algorithm to determine the abnormalities with the accuracy of 97.27%, which outperforms the public tools, BET and ROBEX. As Tesla P100 GPU is employed in the cloud environment, the output is more convenient and accessible than the existing methods.

In consideration of high-precision machining, Tool Condition Monitoring (TCM) is very influential to retain the resolution and accuracy of the machined component. TCM is considered to be always challenging due to diversified operating conditions. In an era of big data analytics, Deep Learning-based networks are gaining significant consideration in the manufacturing industry in the direction of dealing with data gathered from these diversified conditions in a heavy noise environment. In order to address these problems, the Convolutional Neural Network (CNN) based deep learning approach is advocated herein for condition monitoring of a turning tool. After acquiring real-time vibration data corresponding to tool faults, the CNN architecture was designed to assign decimal probabilities to every category in a multi-class classification of tool faults followed by hyperparameters tuning. A rigorous analysis was undertaken through different datasets gathered from diversified machining conditions. The test and validation results demonstrated that the proposed network outperforms the conventional machine learning classifiers.

Recently, an IoT device has taken over as the primary platform for botnet operations. Further research is required to build the proper detection techniques based on the new aspects of botnet assaults since they are not entirely safe. This study aims to develop a parametric analysis of the suggested MAOA hybrid optimization model for intelligent botnet attack detection. The model consists of the extraction of particular features, Improved Information Gain based feature selection, and a hybrid classification-based attack detection model "(Bi-directional Gated Recurrent Unit (BI-GRU)) and Recurrent Neural Network (RNN)," where the training weights of BI-GRU are tuned optimally by MAOA algorithm. Finally, parametric and non-parametric analysis is done to evaluate the performance of the proposed work.

Modeling the reliability of complex repairable systems (CRS) is often intended to be used as a decision support tool for planning the best periodicity of preventive maintenance (PM) of systems. However, modeling with the renewal process (RP) and the non-homogeneous Poisson process (NHPP) considering respectively the case of perfect (PR) and minimal (MR) repair can put the intervention on the system either too soon or too late by deviating from its best relevant periodicity. In this article, we consider the intensity reduction model (IRM) for an approach to optimize the frequency of preventive maintenance (PM) of an air compressor that has operated for nearly three years in the LPG (liquefied petroleum gas) company of NAFTAL (subsidiary of the SONATRACH Group). The estimation of the parameters of this model, making it possible to highlight the effect of preventive maintenance actions (PM), is carried out by the maximum likelihood approach (MLE) which allows to develop, on the programming language MATLAB, an algorithm for simulating the future performance of air compressors. The objective is to propose, over a fixed time horizon, the best frequency of preventive maintenance based on the economic criterion representing the average cost generated by each recommended PM interval. This realistic approach will ensure a balance between preventive and corrective maintenance actions.