Summary
The sensor nodes, which are available in the wireless sensor networks (WSN), are equipped with sensing abilities, and communication. Several domains require the sensor nodes to be arranged in ...aggressive surroundings, in which observing malicious activities within the sensor network. Therefore, the present research proposes the border‐hunting optimization‐based deep CNN (BHO‐DCNN) for the mobile agent (MA)‐based intrusion detection in WSN. The importance of the research relies on the BHO‐DCNN model for identifying the intrusion available in the network is established by integrating the optimization with its features through a deep classifier for detection in a precise manner. The algorithm follows the communal hierarchy, surrounding, group hunting, and prey attacking, which provides an enhanced rate of convergence in the method of detection. The analysis is achieved through the database IDS 2018 Intrusion CSVs depending on the performance like delay, alive nodes, normalized energy, as well as throughput. The obtained number of alive nodes through the developed BHO‐DCNN algorithm is 45, end‐to‐end delay is 0.2572 ms, normalized energy is 0.1622 J, as well as throughput, is 0.3125% for nodes 50 at the populate rate of 100, respectively.
Physical Classification of ripe fruits is an expensive affair in the agriculture industry and human error can lead to inaccurate results. This paper introduces the concept of an intelligent AI-based ...system using spectrophotometry and computer vision for automated fruit segregation based on their grade. When the fruit is fed into the proposed system, the fruit is identified with 95% accuracy, using a cloud-computing platform provided by Microsoft Azure. After that, using spectroscopy and ensemble machine learning approaches, fruit grade is predicted. This ensemble model is trained using 1366 apple readings taken from Unitec's Apple Sorting and Grading Machine from an industrial plant. With the help of H2O's Driverless.AI, the proposed ensemble provides an overall approximate validation accuracy of 82%. The model is also tested on an unseen test dataset containing real-life spectral values and the accuracy of fruit segregation into different classes peaked at 72%.
To propose and implement an automated machine learning (ML) based methodology to predict the overall survival of glioblastoma multiforme (GBM) patients. In the proposed methodology, we used deep ...learning (DL) based 3D U‐shaped Convolutional Neural Network inspired encoder‐decoder architecture to segment the brain tumor. Further, feature extraction was performed on these segmented and raw magnetic resonance imaging (MRI) scans using a pre‐trained 2D residual neural network. The dimension‐reduced principal components were integrated with clinical data and the handcrafted features of tumor subregions to compare the performance of regression‐based automated ML techniques. Through the proposed methodology, we achieved the mean squared error (MSE) of 87 067.328, median squared error of 30 915.66, and a SpearmanR correlation of 0.326 for survival prediction (SP) with the validation set of Multimodal Brain Tumor Segmentation 2020 dataset. These results made the MSE far better than the existing automated techniques for the same patients. Automated SP of GBM patients is a crucial topic with its relevance in clinical use. The results proved that DL‐based feature extraction using 2D pre‐trained networks is better than many heavily trained 3D and 2D prediction models from scratch. The ensembled approach has produced better results than single models. The most crucial feature affecting GBM patients' survival is the patient's age, as per the feature importance plots presented in this work. The most critical MRI modality for SP of GBM patients is the T2 fluid attenuated inversion recovery, as evident from the feature importance plots.
Smart Grids (SG) generate extensive data sets regarding the system variables, viz., and demand and supply. These extremely large data sets are known as big data. Hence, preprocessing of this vast ...data and integration become critical steps in the load forecasting process. The precise prediction of the load is the primary concern while balancing the demand and supply in SG. Many techniques were devised for load forecasting using machine learning methods such as Deep-learning Models. However, in the case of large data sets, only a few models provide good performance, viz. Autoregressive Integrated Moving Average (ARIMA). However, this approach is complex, as it takes a minimum of 50 observations to make an evaluation. In this paper, the Prophet technique is used in the prediction of future demand response based on the past data, which is in the form of a time series. This technique is valid even if a few values in the time series are not available. Furthermore, the procedure is not affected by fluctuations, trends, and abnormal variations. The automatic model fitting approach is adopted for its effective performance. Further, ARIMA and Prophet model have been used to forecast and the approach is verified using various evaluation metrics. The demand response management was achieved and is being validated with two data sets. The results show the effectiveness of the Prophet model in the demand response management scheme involving large data sets.
The purpose of this study was to determine electromyographically if there are significant differences in the movement associated with the knee muscle, gait, leg extension from a sitting position and ...flexion of the leg upwards for regular and abnormal sEMG data. Surface electromyography (sEMG) data were obtained from the lower limbs of 22 people during three different exercises: sitting, standing, and walking (11 with and 11 without knee abnormality). Participants with a knee deformity took longer to finish the task than the healthy subjects. The sEMG signal duration of patients with abnormalities was longer than that of healthy patients, resulting in an imbalance in the obtained sEMG signal data. As a result of the data’s bias towards the majority class, developing a classification model for automated analysis of such sEMG signals is arduous. The sEMG collected data were denoised and filtered, followed by the extraction of time-domain characteristics. Machine learning methods were then used for predicting the three distinct movements (sitting, standing, and walking) associated with electrical impulses for normal and abnormal sets. Different anomaly detection techniques were also used for detecting occurrences in the sEMG signals that differed considerably from the majority of data and were hence used for enhancing the performance of our model. The iforest anomaly detection technique presented in this work can achieve 98.5% accuracy on the light gradient boosting machine algorithm, surpassing the previous results which claimed a maximum accuracy of 92.5% and 91%, improving accuracy by 6–7% for classification of knee abnormality using machine learning.
Big data has been a topic of interest for many researchers and industries for the past few decades. Due to the exponential growth of technology, a tremendous amount of data is generated every minute. ...This article provides a strategic review of big data in the healthcare sector. In particular, this article highlights various applications and issues faced by the healthcare industry using big data by evaluating various journal articles between 2016 and 2021. Multiple issues related to data mining, storing, analyzing, and sharing of big data in healthcare, briefly summarizing deep-learning-based tools available for big data analytics, have been covered in this article. This article aims to benefit the research community by summarizing various research tools and processes available today to manage big data in healthcare.
In recent years, the increased population has led to an increase in the demand for various industrially processed edibles and other consumable products. These industries regularly alter the proteins ...found in raw materials to generate more commercially viable end-products in order to keep up with consumer demand. These modifications result in a substance that may cause allergic reactions in consumers, thereby creating a protein allergen. The detection of such proteins in various substances is essential for the prevention, diagnosis and treatment of allergic conditions. Bioinformatics and computational methods can be used to analyze the information contained in amino-acid sequences to detect possible allergens. The article presents a deep learning based ensemble approach to identify protein allergens using Extra Tree, Deep Belief Network (DBN), and CatBoost models. The proposed ensemble model achieves higher detection accuracy by combining the prediction results of the three models using majority voting. The evaluation of the proposed model was carried out on the benchmark protein allergen dataset, and the performance analysis revealed that the proposed model outperforms the other state-of-the-art literature techniques with a protein allergen detection accuracy of 89.16%.
This paper is a case study of visiting an external audit company to explore the usefulness of machine learning algorithms for improving the quality of an audit work. Annual data of 777 firms from 14 ...different sectors are collected. The Particle Swarm Optimization (PSO) algorithm is used as a feature selection method. Ten different state-of-the-art classification models are compared in terms of their accuracy, error rate, sensitivity, specificity, F measures, Mathew's Correlation Coefficient (MCC), Type-I error, Type-II error, and Area Under the Curve (AUC) using Multi-Criteria Decision-Making methods like Simple Additive Weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The results of Bayes Net and J48 demonstrate an accuracy of 93% for suspicious firm classification. With the appearance of tremendous growth of financial fraud cases, machine learning will play a big part in improving the quality of an audit field work in the future.
Transactions through the web are now a progressive mechanism to access an ever-increasing range of services over more and more diverse environments. The internet provides many opportunities for ...companies to provide personalized online services to their customers, but the quality and novelty of some web services may adversely affect the appeal and user gratification. In the future, prediction of the consumer intention needs to be a main focus for selecting the web services for an application. The aim of this study is to predict online consumer repurchase intentions; to accomplish this objective a hybrid approach is chosen with a combination of machine learning techniques and artificial bee colony (ABC) algorithm being used. The study starts with identification of consumer characteristics for repurchase intention, followed by determining the feature selection of consumer characteristics and shopping mall attributes (with >0.1 threshold value) for the prediction model using ABC. Finally, validation using
k
-fold cross has been employed to measure the best classification model robustness. The classification models, viz. decision trees (C5.0), AdaBoost, random forest, support vector machine and neural network, are utilized to predict consumer purchase intention. Performance evaluation of identified models on training–testing partitions (70–30%) of the data set shows that the AdaBoost method outperforms other classification models, with sensitivity and accuracy of 0.95 and 97.58%, respectively, on testing the data set. Examining the consumer repurchase intentions by considering both shopping mall and consumer characteristics makes this study unique.
Graphene oxide (GO) has attracted much attention in the past few years because of its interesting and promising electrical, thermal, mechanical, and structural properties. These properties can be ...altered, as GO can be readily functionalized. Brodie synthesized the GO in 1859 by reacting graphite with KClO3 in the presence of fuming HNO3; the reaction took 3–4 days to complete at 333 K. Since then, various schemes have been developed to reduce the reaction time, increase the yield, and minimize the release of toxic byproducts (NO2 and N2O4). The modified Hummers method has been widely accepted to produce GO in bulk. Due to its versatile characteristics, GO has a wide range of applications in different fields like tissue engineering, photocatalysis, catalysis, and biomedical applications. Its porous structure is considered appropriate for tissue and organ regeneration. Various branches of tissue engineering are being extensively explored, such as bone, neural, dentistry, cartilage, and skin tissue engineering. The band gap of GO can be easily tuned, and therefore it has a wide range of photocatalytic applications as well: the degradation of organic contaminants, hydrogen generation, and CO2 reduction, etc. GO could be a potential nanocarrier in drug delivery systems, gene delivery, biological sensing, and antibacterial nanocomposites due to its large surface area and high density, as it is highly functionalized with oxygen-containing functional groups. GO or its composites are found to be toxic to various biological species and as also discussed in this review. It has been observed that superoxide dismutase (SOD) and reactive oxygen species (ROS) levels gradually increase over a period after GO is introduced in the biological systems. Hence, GO at specific concentrations is toxic for various species like earthworms, Chironomus riparius, Zebrafish, etc.
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