Development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

Cloud computing is the most recent smart city advancement, made possible by the increasing volume of heterogeneous data produced by apps. More storage capacity and processing power are required to process this volume of data. Data analytics is used to examine various datasets, both structured and unstructured. Nonetheless, as the complexity of data in the healthcare and biomedical communities grows, obtaining more precise results from analyses of medical datasets presents a number of challenges. In the cloud environment, big data is abundant, necessitating proper classification that can be effectively divided using machine language. Machine learning is used to investigate algorithms for learning and data prediction. The Cleveland database is frequently used by machine learning researchers. Among the performance metrics used to compare the proposed and existing methodologies are execution time, defect detection rate, and accuracy. In this study, two supervised learning-based classifiers, SVM and Novel KNN, were proposed and used to analyses data from a benchmark database obtained from the UCI repository. Initially, intrusions were detected using the SVM classification method. The proposed study demonstrated how the novel KNN used for distance capacity outperformed previous studies. The accuracy of the results of both approaches is evaluated. The results show that the intrusion detection system (IDS) with a 98.98% accuracy rate produces the best results when using the suggested system

Evaluating air quality and criteria pollutants prediction disparities by data mining along a stretch of urban-rural agglomeration includes coal-mine belts and thermal power plants

Air pollution has become a threat to human life around the world since researchers have demonstrated several effects of air pollution to the environment, climate, and society. The proposed research was organized in terms of National Air Quality Index (NAQI) and air pollutants prediction using data mining algorithms for particular timeframe dataset (01 January 2019, to 01 June 2021) in the industrial eastern coastal state of India. Over half of the study period, concentrations of PM2.5, PM10 and CO were several times higher than the NAQI standard limit. NAQI, in terms of consistency and frequency analysis, revealed that moderate level (ranges 101–200) has the maximum frequency of occurrence (26–158 days), and consistency was 36%–73% throughout the study period. The satisfactory level NAQI (ranges 51–100) frequency occurrence was 4–43 days with a consistency of 13%–67%. Poor to very poor level of air quality was found 13–50 days of the year, with a consistency of 9%–25%. Random Forest (RF), Support Vector Machine (SVM), Bagged Multivariate Adaptive Regression Splines (MARS) and Bayesian Regularized Neural Networks (BRNN) are the data mining algorithms, that showed higher efficiency for the prediction of PM2.5, PM10, NO2 and SO2 except for CO and O3 at Talcher and CO at Brajrajnagar. The Root Mean Square Error (RMSE) between observed and predicted values of PM2.5 (ranges 12.40–17.90) and correlation coefficient (r) (ranges 0.83–0.92) for training and testing data indicate about slightly better prediction of PM2.5 by RF, SVM, bagged MARS, and BRNN models at Talcher in comparison to PM2.5 RMSE (ranges 13.06–21.66) and r (ranges 0.64–0.91) at Brajrajnagar. However, PM10 (RMSE: 25.80–43.41; r: 0.57–0.90), NO2 (RMSE: 3.00–4.95; r: 0.42–0.88) and SO2 (RMSE: 2.78–5.46; r: 0.31–0.88) at Brajrajnagar are better than PM10 (RMSE: 35.40–55.33; r: 0.68–0.91), NO2 (RMSE: 4.99–9.11; r: 0.48–0.92), and SO2 (RMSE: 4.91–9.47; r: 0.20–0.93) between observed and predicted values of training and testing data at Talcher using RF, SVM, bagged MARS and BRNN models, respectively. Taylor plots demonstrated that these algorithms showed promising accuracy for predicting air quality. The findings will help scientific community and policymakers to understand the distribution of air pollutants to strategize reduction in air pollution and enhance air quality in the study region

Role of Fibrin Glue as a Sealant to Esophageal Anastomosis in Cases of Congenital Esophageal Atresia with Tracheoesophageal Fistula

Abstract Objective The aim of this study was to characterize a successful approach for the management of infants with long-gap esophageal atresia (EA) with tracheoesophageal fistula (TEF). The goal was to preserve the native esophagus and minimize the incidence of esophageal anastomotic leaks using fibrin glue as a sealant over the esophageal anastomosis. Method A total of 52 patients were evaluated in this study. Only patients in whom, gap between the two ends of the esophagus was ‡ 2 cm were selected durin

Classification of Electrocardiogram Signals Based on Hybrid Deep Learning Models

According to the analysis of the World Health Organization (WHO), the diagnosis and treatment of heart diseases is the most difficult task. Several algorithms for the classification of arrhythmic heartbeats from electrocardiogram (ECG) signals have been developed over the past few decades, using computer-aided diagnosis systems. Deep learning architecture adaption is a recent effective advancement of deep learning techniques in the field of artificial intelligence. In this study, we developed a new deep convolutional neural network (CNN) and bidirectional long-term short-term memory network (BLSTM) model to automatically classify ECG heartbeats into five different groups based on the ANSI-AAMI standard. End-to-end learning (feature extraction and classification work together) is done in this hybrid model without extracting manual features. The experiment is performed on the publicly accessible PhysioNet MIT-BIH arrhythmia database, and the findings are compared with results from the other two hybrid deep learning models, which are a combination of CNN and LSTM and CNN and Gated Recurrent Unit (GRU). The performance of the model is also compared with existing works cited in the literature. Using the SMOTE approach, this database was artificially oversampled to address the class imbalance problem. This new hybrid model was trained on the oversampled ECG database and validated using tenfold cross-validation on the actual test dataset. According to experimental observations, the developed hybrid model outperforms in terms of recall, precision, accuracy and F-score performance of the hybrid model are 94.36%, 89.4%, 98.36% and 91.67%, respectively, which is better than the existing methods

Mobile robot navigation in unknown static environments using ANFIS controller

Navigation and obstacle avoidance are the most important task for any mobile robots. This article presents the Adaptive Neuro-Fuzzy Inference System (ANFIS) controller for mobile robot navigation and obstacle avoidance in the unknown static environments. The different sensors such as ultrasonic range finder sensor and sharp infrared range sensor are used to detect the forward obstacles in the environments. The inputs of the ANFIS controller are obstacle distances obtained from the sensors, and the controller output is a robot steering angle. The primary objective of the present work is to use ANFIS controller to guide the mobile robot in the given environments. Computer simulations are conducted through MATLAB software and implemented in real time by using C/C++ language running Arduino microcontroller based mobile robot. Moreover, the successful experimental results on the actual mobile robot demonstrate the effectiveness and efficiency of the proposed controller

Significance of fractal correlation dimension and seismic b-value variation due to 15th July 2009, New Zealand earthquake of Mw 7.8

New Zealand earthquake that occurred on 15th July 2009 (Mw 7.8) was analysed using fractal correlation dimension (Dc) and seismic b-VALUE. We have analysed the earthquakes catalog of thirty- ve years with a magnitude (mb ≥3.7), in order to observe a crucial in- formation in terms of Dc value uctuation for the event. The event is preceded by fall and anomalous change in Dc value in the year 2007 about two years prior to the mainshock. A sudden decrease in Dc value with highly clustered events is observed before the main- shock. The low value of Dc is an indicator of clustering and it shows how intermediate size events correlate with one another in the pre- paration process of this event. Here the low Dc value may be the indicator for high stress developer along the fault to produce large size earthquake. Moreover, we also observed abnormal uctuation in b-VALUE from 2003. The fractal clustering and scaling of earthquakes are indicated by b-VALUE change prior to strong earthquake as a harbinger of stress correlation in various scales. The event is also mar- ked for that occurred in the periphery of the positive Coulomb stress development, as obtained from three low Dc time windows' events. The drop in Dc value is not a single observation prior to this large event, but such pattern is also seen for other strong events in the study zone. One such well identi ed strong event is Mw 7.2 (2003) along with low Dc value prior to the event. Thus, stress correlation mea- sured along with these indirect statistical tools gives the clue of self-organization of long wavelength of stress, which was not measu- red earlier with classical approaches. This type of study may provide a very useful information for hazard mitigation

Brain tumor detection using CNN, AlexNet & GoogLeNet ensembling learning approaches

The detection of neurological disorders and diseases is aided by automatically identifying brain tumors from brain magnetic resonance imaging (MRI) images. A brain tumor is a potentially fatal disease that affects humans. Convolutional neural networks (CNNs) are the most common and widely used deep learning techniques for brain tumor analysis and classification. In this study, we proposed a deep CNN model for automatically detecting brain tumor cells in MRI brain images. First, we preprocess the 2D brain image MRI image to generate convolutional features. The CNN network is trained on the training dataset using the GoogleNet and AlexNet architecture, and the data model's performance is evaluated on the test data set. The model's performance is measured in terms of accuracy, sensitivity, specificity, and AUC. The algorithm performance matrices of both AlexNet and GoogLeNet are compared, the accuracy of AlexNet is 98.95, GoogLeNet is 99.45 sensitivity of AlexNet is 98.4, and GoogLeNet is 99.75, so from these values, we can infer that the GooGleNet is highly accurate and parameters that GoogLeNet consumes is significantly less; that is, the depth of AlexNet is 8, and it takes 60 million parameters, and the image input size is 227 × 227. Because of its high specificity and speed, the proposed CNN model can be a competent alternative support tool for radiologists in clinical diagnosis

Real-Time Survivor Detection System in SaR Missions Using Robots

This paper considers the issue of the search and rescue operation of humans after natural or man-made disasters. This problem arises after several calamities, such as earthquakes, hurricanes, and explosions. It usually takes hours to locate the survivors in the debris. In most cases, it is dangerous for the rescue workers to visit and explore the whole area by themselves. Hence, there is a need for speeding up the whole process of locating survivors accurately and with less damage to human life. To tackle this challenge, we present a scalable solution. We plan to introduce the usage of robots for the initial exploration of the calamity site. The robots will explore the site and identify the location of human survivors by examining the video feed (with audio) captured by them. They will then stream the detected location of the survivor to a centralized cloud server. It will also monitor the associated air quality of the selected area to determine whether it is safe for rescue workers to enter the region or not. The human detection model for images that we have used has a mAP (mean average precision) of 70.2%. The proposed approach uses a speech detection technique which has an F1 score of 0.9186 and the overall accuracy of the architecture is 95.83%. To improve the detection accuracy, we have combined audio detection and image detection techniques