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.

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.

Updates on therapies for chronic prostatitis/chronic pelvic pain syndrome

Prostatitis comprises of a group of syndromes that affect almost 50% of men at least once in their lifetime and makeup the majority of visits to the Urology Clinics.After much debate, it has been divided into four distinct categories by National Institutes of Health namely(1) acute bacterial prostatitis;(2) chronic bacterial prostatitis;(3) chronic prostatitis/chronic pelvic pain syndrome(CP/CPPS) which is further divided into inflammatory and non-inflammatory CP/CPPS; and(4)asymptomatic inflammatory prostatitis. CP/CPPS has been a cause of great concern for both patients and physicians because of the lack of presence of thoroughinformation about the etiological factors along with the difficult-to-treat nature of the syndrome. For the presented manuscript an extensive search on PubM ed was conducted for CP/CPPS aimed to present an updated review on the evaluation and treatment options available for patients with CP/CPPS. Several diagnostic criteria's have been established to diagnose CP/CPPS, with prostatic/pelvic pain for at least 3 mo being the major classifying symptom along with the presence of lower urinary tract symptoms and/or ejaculatory pain. Diagnostic tests can help differentiate CP/CPPS from other syndromes that come under the heading of prostatitis by ruling out active urinary tract infection and/or prostatic infection with uropathogen by performing urine cultures, Meares-Stamey Four Glass Test, Preand Post-Massage Two Glass Test. Asymptomatic inflammatory prostatitis is confirmed through prostate biopsy done for elevated serum prostate-specific antigen levels or abnormal digital rectal examination. Researchers have been unable to link a single etiological factor to the pathogenesis of CP/CPPS, instead a cluster of potential etiologies including atypical bacterial or nanobacterial infection, autoimmunity, neurological dysfunction and pelvic floor muscle dysfunction are most commonly implicated. Initially monotherapy with anti-biotics and alpha adrenergic-blockers can be tried, but its success has only been observed in treatment nave population. Other pharmacotherapies including phytotherapy, neuromodulatory drugs and anti-inflammatories achieved limited success in trials. Complementary and interventional therapies including acupuncture, myofascial trigger point release and pelvic floor biofeedback have been employed. This review points towards the fact that treatment should be tailored individually for patients based on their symptoms. Patients can be stratified phenotypically based on the UPOINT system constituting of Urinary, Psychosocial, Organ-specific, Infectious, Neurologic/Systemic and symptoms of muscular Tenderness and the treatment algorithm should be proposed accordingly. Treatment of CP/CPPS should be aimed towards treating local aswell as central factors causing the symptoms. Surgical intervention can cause significant morbidity and should only be reserved for treatment-refractory patients that have previously failed to respond to multiple drug therapies.

An Energy Efficient Color Based Topology Control Algorithm for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are universally being used and deployed to monitor the surrounding physical environments and detail events of interest. In wireless Sensor Networks energy is one of the primary issues and requires energy conservation of the sensor nodes, so that network lifetime can be maximized. To minimize the energy loss in dense WSNs a Color Based Topology Control (CBTC) algorithm is introduced and implemented in Visual Studio 6.0. The results are compared with Traditional dense WSNs. In the evaluation process it was observed that the numbers of CPU ticks required in traditional WSNs are much more than that’s of CBTC Algorithm, both in Normal and Random deployments. So by using CBTC, delay in network can be minimized. Using CBTC algorithm, the energy conservation and removal of coverage holes was also achieved in the present study.

Channel aware receiver for next generation wireless communication system

We consider an iterative phase synchronization scheme based on maximum a posteriori probability algorithm.In classical approaches,the phase noise estimation model considers one sample per symbol at the channel and receiver.However,information theoretic studies suggested use of more than one sample per symbol at the channel and receiver for achieving higher performance.In this article,a soft-information aided iterative receiver is derived,which uses off-the-shelf blocks for detection and demodulation by keeping the complexity of the receiver acceptable.We consider here two samples per symbols at the channel and receiver in a pragmatic paradigm.It is shown that phase noise estimation can be significantly improved at the expense of modest processing overhead.Simulation results are presented for low-density parity check coded quadrature amplitude modulations.Our results show a significant performance improvement for strong phase noise values compared to classical receiver approaches.