Performance Analysis of Intelligent Neural-Based Deep Learning System on Rank Images Classification

The use of the internet is increasing all over the world on a daily basis in the last two decades.The increase in the internet causes many sexual crimes,such as sexual misuse,domestic violence,and child pornography.Various research has been done for pornographic image detection and classification.Most of the used models used machine learning techniques and deep learning models which show less accuracy,while the deep learning model ware used for classification and detection performed better as compared to machine learning.Therefore,this research evaluates the performance analysis of intelligent neural-based deep learning models which are based on Convolution neural network(CNN),Visual geometry group(VGG-16),VGG-14,and Residual Network(ResNet-50)with the expanded dataset,trained using transfer learning approaches applied in the fully connected layer for datasets to classify rank(Pornographic vs.Nonpornographic)classification in images.The simulation result shows that VGG-16 performed better than the used model in this study without augmented data.The VGG-16 model with augmented data reached a training and validation accuracy of 0.97,0.94 with a loss of 0.070,0.16.The precision,recall,and f-measure values for explicit and non-explicit images are(0.94,0.94,0.94)and(0.94,0.94,0.94).Similarly,The VGG-14 model with augmented data reached a training and validation accuracy of 0.98,0.96 with a loss of 0.059,0.11.The f-measure,recall,and precision values for explicit and non-explicit images are(0.98,0.98,0.98)and(0.98,0.98,0.98).The CNN model with augmented data reached a training and validation accuracy of 0.776&0.78 with losses of 0.48&0.46.The f-measure,recall,and precision values for explicit and non-explicit images are(0.80,0.80,0.80)and(0.78,0.79,0.78).The ResNet-50 model with expanded data reached with training accuracy of 0.89 with a loss of 0.389 and 0.86 of validation accuracy and a loss of 0.47.The f-measure,recall,and precision values for explicit and non-explicit images are(0.86,0.97,0.91)and(0.86,0.93,0.89).Where else without augmented data the VGG-16 model reached a training and validation accuracy of 0.997,0.986 with a loss of 0.008,0.056.The f-measure,recall,and precision values for explicit and non-explicit images are(0.94,0.99,0.97)and(0.99,0.93,0.96)which outperforms the used models with the augmented dataset in this study.

An Adaptive Edge Detection Algorithm for Weed Image Analysis

Weeds are one of the utmost damaging agricultural annoyers that have a major influence on crops.Weeds have the responsibility to get higher production costs due to the waste of crops and also have a major influence on the worldwide agricultural economy.The significance of such concern got motivation in the research community to explore the usage of technology for the detection of weeds at early stages that support farmers in agricultural fields.Some weed methods have been proposed for these fields;however,these algorithms still have challenges as they were implemented against controlled environments.Therefore,in this paper,a weed image analysis approach has been proposed for the system of weed classification.In this system,for preprocessing,a Homomorphic filter is exploited to diminish the environmental factors.While,for feature extraction,an adaptive feature extraction method is proposed that exploited edge detection.The proposed technique estimates the directions of the edges while accounting for non-maximum suppression.This method has several benefits,including its ease of use and ability to extend to other types of features.Typically,low-level details in the formof features are extracted to identify weeds,and additional techniques for detecting cultured weeds are utilized if necessary.In the processing of weed images,certain edges may be verified as a footstep function,and our technique may outperform other operators such as gradient operators.The relevant details are extracted to generate a feature vector that is further given to a classifier for weed identification.Finally,the features have been used in logistic regression for weed classification.The model was assessed against logistic regression that accurately identified different kinds of weed images in naturalistic domains.The proposed approach attained weighted average recognition of 98.5%against the weed images dataset.Hence,it is assumed that the proposed approach might help in the weed classification system to accurately identify narrow and broad weeds taken captured in real environments.

A Health Monitoring System Using IoT-Based Android Mobile Application

Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate,ECG,nasal/oral airflow,temperature,light sensor,and fall detection sensor.Different researchers have done different work in the field of health monitoring with sensor networks.Different researchers used built-in apps,such as some used a small number of parameters,while some other studies used more than one microcontroller and used senders and receivers among the microcontrollers to communicate,and outdated tools for study development.While no efficient,cheap,and updated work is proposed in the field of sensor-based health monitoring systems.Therefore,this study developed an android-based mobile system that can remotely monitor electrocardiograms(ECGs),pulse oximetry,heart rate,and body temperature.The microcontroller’s Wi-Fi device is used to manage wireless data transport.The findings of the patient are saved on the Firebase server for further usage in the mobile app.The performance of the proposed device is tested on ten numbers of different patients age-wise in terms of beats per minute(BPM),ECG,Temperature,and SpO2.This system uses temperature,pulse,ECG,blood pressure,and eye blink sensors.This device makes the usage of a tiny pulse sensor that has been designed to provide an accurate and optimal readout of the pulse rate and a temperature sensor is also included.With the help of an MCU,our system measures the pulse rate in beats per minute(bpm),blood oxygen level temperature measurements,and ECG readings and communicates this information to the Firebase server.To check the performance of the proposed system first,the BPM parameter was checked on the cardiac monitor.Then,the proposed model is tested on different patients age-wise.The simulation result shows that the BPM reading is not much different than the BPM of the cardiac monitor.According to the simulation findings,the proposed model achieved the best performance as compared to commercially available devices.

A Template Matching Based Feature Extraction for Activity Recognition

Human activity recognition(HAR)can play a vital role in the monitoring of human activities,particularly for healthcare conscious individuals.The accuracy of HAR systems is completely reliant on the extraction of prominent features.Existing methods find it very challenging to extract optimal features due to the dynamic nature of activities,thereby reducing recognition performance.In this paper,we propose a robust feature extraction method for HAR systems based on template matching.Essentially,in this method,we want to associate a template of an activity frame or sub-frame comprising the corresponding silhouette.In this regard,the template is placed on the frame pixels to calculate the equivalent number of pixels in the template correspondent those in the frame.This process is replicated for the whole frame,and the pixel is directed to the optimum match.The best count is estimated to be the pixel where the silhouette(provided via the template)presented inside the frame.In this way,the feature vector is generated.After feature vector generation,the hiddenMarkovmodel(HMM)has been utilized to label the incoming activity.We utilized different publicly available standard datasets for experiments.The proposed method achieved the best accuracy against existing state-of-the-art systems.