Smart Artificial Intelligence techniques using embedded band for diagnosis and combating COVID-19

Abstract

Recently, COVID-19 virus spread to create a major impact in human body worldwide. The Corona virus, initiated by the SARS-CoV-2 virus, was known in China, December 2019 and affirmed a worldwide epidemic by the World Health Organization on 11 March 2020. The core aim of this research is to detect the spreading of COVID-19 virus and solve the problems in human lungs infection quickly. An Artificial Intelligence (AI) technique is a possibly controlling device in the battle against the corona virus epidemic. Recently, AI with computational techniques are utilized for COVID-19 virus with the building blocks of Deep Learning method using Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) is used to classify and identify the lung images affected region. These two algorithms used to diagnose COVID-19 infections rapidly. The AI applications against COVID-19 are Medical Imaging for Diagnosis, Lung delineation, Lesion measurement, Non-Invasive Measurements for Disease Tracking, Patient Outcome Prediction, Molecular Scale: from Proteins to Drug Development and Societal Scale: Epidemiology and Infodemiology.

1. Introduction

AI-empowered image acquisition utilized CT-scan and X-Ray images to identify the corona virus result rapidly [1]. The radiologists using smartphone enabled sensor using AI techniques to detect corona virus rapidly [2]. The artificial intelligence tool to tackle COVID-19 crisis with different molecular and epidemiological applications [3]. The convolution neural network (CNN) and modified pre- trained AlexNet model are used to prepared X-rays and CT scan images dataset to detect COVID-19 rapidly [4]. The deep neural network is automatically detecting the COVID-19 symptoms from CXR images. It provides positive predictive value up to 89.61% [5]. Multi-level threshold and SVM classifiers are used to find corona virus lung infection with the help of X-Ray images [6]. Deep leaning with AI techniques assist radiologists using chest X-Ray images to detect COVId-19 virus up to 96% accurately [7].

Deep learning method is used to identify the corona virus patient using X-Ray images. SVM with ResNet50 provides better results compare to other classification models [8]. The survey result shows that AI plays an important role to fight against COVID-19 [9]. Corona virus is a virus initiated by the “SARS-CoV-2 virus” which may cause illness from animals or humans. The primary symptoms for Corona virus are Fever, Dry cough, Breathing difficulty, headache and body pain, runny nose, nasal blocking, throat pain [10]. The COVID-19 infected person, when he is coughing or sneezing the virus can easily spread through small drops from one person to another person easily. So, COVID-19 virus spread in two ways: Direct contact and indirect contact. In direct close contact the person does not cover his face can get the infection from COVID-19 patients when he is coughing or sneezing. In indirect contact the person is touching any COVID-19 virus infected place and then he is touching his eyes, nose or mouth it can spread the virus easily [11]. Up to November 23, 2020, 58,425,681 Confirmed cases of corona virus in over 220 countries and territories and 1385,218 Confirmed deaths [12].

The COVID-19 virus can be transferred through two modes of droplets with different particles. If the particle sizes are >5–10 µm they are called as respiratory droplets, otherwise it is called as droplet nuclei whose particle size is <5 µm. According to World Health Organization current evidence respiratory droplets are spreading easily through direct contact compare to droplet nuclei. The droplet transmission occurs within 1 m direct contact with COVID-19 infected people [13]. As of now there are no vaccines or antivirals to prevent or treat the COVID-19 patients, although worldwide there are 44 potential corona virus vaccines are in development [14]. The scientists cautioned the people to guard their eyes, hands and mouth for spreading COVID-19 virus very slowly. The corona virus infected people when they cough or talks, the virus particle can easily spread into another person's face [15].

United States Polymerase Chain Reaction (PCR) testing trusted source primarily used for COVID- 19 diagnostic testing. In 2002 the same type of test used to detect for SARS virus. The Reverse Transcription PCR (RT-PCR) technique is used to collect larger sample for viral comparison. Finally, the RT-PCR technique comparison result found two genes in SARS-CoV-2 genome. The test result shows if both genes are found the result is positive, if only one gene is found then the result is inconclusive, if neither of the genes is found then the result is negative. Finally, the doctor order to take chest CT scan which also to help diagnose COVID-19 or it clearly shows lung infected with virus. As a result, the CT scan identified virus infection in 98 percent of patients, but RT-PCR test detect it only 71 of the time correctly. The RT-PCR test can take day or longer time to get the results. The Food and Drug Administration (FDA) ratified Point-of-Care (POC) testing devices produce test results within 45 min [16]. Recently COVID-19 Diagnostics Resource Centre started online training course for laboratory testing and diagnosis for COVID-19 [17]. The possible symptoms for COVID-19 viruses are as shown in Fig. 1 . The primary symptoms for Corona virus are mild fever, Fatigue, Aching muscles, Breathing problem, Dry cough along with less typical symptoms of Headache, Diarrhea, Phlegm buildup and Hemoptysis. The person is having all above symptoms then the person affected with COVID-19 virus. The virus gets into human lungs and it affect lung functionality with the impact increases up to 14 days.

Fig. 1.

Possible symptoms of COVID-19 [18].

2. Smart AI prediction for Covid-19

In 2015, active neural network was produced to forecast Zika-virus and its spread. The Carnegie Mellon University now utilizes the same algorithm to be re-utilized on new data from corona virus to predict its spread. The GLEAMviz epidemiological model developed by the Oxford University were provides the prediction for spreading of corona virus. Metabiota, a San Fransciso company developed Eppidemic tracker to track the virus spread based upon predictions. The epidemiological SIR model is utilized in Robert Koch Institute with the purpose of quarantines and social distance maintenance. Finally, Robert Kock institute's used extended SIR model to analyse that the containment is the success of reducing the corona virus slowly [19].

The corona virus infected people sometimes they are not realizing their symptoms up to five days. In that situation the virus can easily spread to new people, without realizing their symptoms. Rezai and his team developed the Oura smart ring which is used to measure the person body temperature, heart beat rate, activity and sleep time of the person. The Oura ring contains different sensors which include temperature sensor, gyroscope, accelerometer and infrared LEDs as shown in Fig. 2 . Finally, Rezai says this smart ring AI model predicts the people infection within 24 h whether they are having COVID-19 symptoms or not [19].

Fig. 2.

The Oura smart ring [20].

Andrew NG's startup Landing AI has developed social distance detector to detect the people walking around the road street whether they are maintained six feet distance from one person to another person or not. The result shows on the video screen with the colors of red and green. The red color indicates the peoples they are not maintaining the six feet social distance, so the probability of spreading the virus from one person another person is very high. The green color indicates the peoples they are maintained above six feet distance, so the probability of spreading virus from one person another person is very low as shown in Fig. 3 [21]

Fig. 3.

Social distancing detector [21].

3. Smart AI techniques for diagnosis Covid-19

The smart AI techniques can diagnosis the COVID-19 viruses quickly and accurately. So, this smart AI technique can safeguard the persons, minimize the corona virus spread, and it can produce accurate information to train the AI techniques. The researchers working with UN Global Pulse to diagnosis COVID-19 virus using smart AI techniques, the result shows that smart AI techniques provide accurate result compare to humans and also diagnosis the corona virus faster and cheaper than standard COVID-19 tests. Both Computed Tomography (CT) scans and X-rays plays important role to diagnosis the corona virus. The chest X-ray image data are applied to machine learning to diagnose COVID-19 virus. The smart AI technique called as COVID-19 Net were used chest X-ray images data to identify the various lung conditions, including COVID-19. To identify Corona virus from CT-Scans, the researchers from Renmin University, China printed an AI with deep learning methods gives better performance compare to radiologist which control the epidemic, improve early diagnosis, isolation and treatment. Another researcher from Dutch University established an AI method called “CAD4COVID” for diagnose corona virus from X-rays. The CAD4COVID is an AI software that tries corona virus suspects on chest X-ray images. So, based upon this survey result the doctors can take either CT scan or X-ray from corona virus infected person with direct contact of high risk. In future COVID-19 diagnosis by an AI doctor plays an important role [22].

Linking Med, company said, Pneumonia is a communal problem of corona virus. The AI based CT scan model analyzed CT images within sixty seconds with an accuracy of above 90% accurate results. The AI model not only identifies the lung infected region but also quantifies the number, volume and proposition. The AI based multisensory model repeatedly detects the fever of individuals, movement monitoring, and finally it detects the person is covered his face or not [23].

Nowadays every smartphone having sensor technologies like camera sensor, Inertial sensor, Microphone sensor and Temperature fingerprint sensor to capture the data from COVID-19 patients. The captured data processed though deep algorithms such as Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). The CNN algorithm is used for image recognition and the output of each layer is applied to input of next layer with the help of RNN algorithm. Finally, these two algorithms combined to produce the COVID-19 patient result as positive or negative [2].

The neural networks combine two deep learning algorithms such as “RestNet-50″ and “UNet++”. The “RestNet50” is used for image recognition and the purpose of “UNet++” is processing the chest CT scans. The Chinese Academy of Sciences in Beijing developed Corona virus diagnosis using fully automatic deep learning model with CT- images [24]. The researchers designed decision-based computer model to predict COVID-19 virus accurately from the patients based upon the decision point [25]. Many deep learning methods battle against COVID-19 diagnosis using radiology images [9]. YITU developed AI-equipped diagnostic assistant to identify the COVID-19 analysis within 2–3 s using chest CT-images in Table 1 [26].

Table 1.

Summary of laboratory testing and medical imaging-based methods for COVID-19 applications [26].

S. No	Data	Modality	Results
1	The patients RNA samples are collected from throat swab and a specific enzyme.	RT-PCR	The Patient both DNA results are positive, then the person affected with COVID-19. The result ranges are varying from 60–70% to 95–97%.
2	Sputum Sample	Molecular Point-of-Care	The test results produced within 30 min automatically.
3	Chest image	CT	CT-based COVID-19 diagnosis results better than RT-PCR which has 80–90%. But RT-PCR test result is 60–70%. The problem with CT- Scan is radiology person has to cleaning scanners in between patients with high risk of COVID-19 epidemic.
4	Chest image	X-Ray	X-Ray results are insensitive compare to CT-Scan. But compare to CT-scan, X-Ray machines are easier to clean.
5	Chest image	Ultra Sound CT	The ultra sound CT scan result is better than X-Ray image. But ultrasound has high risk with close contact between the patient and physician.
6	Chest image	PET-CT	This technique takes more time to diagnose COVID-19 results compare to other methods.
7	Chest image	CT	AI based CT assessment using Deep knowledge principles to detect Corona virus on chest CT scans. According to Gozes et al., reports sensitivity is 98.2%, and specificity is 92.2% using deep learning-based CT algorithm. But the problem is some persons are infected with pneumonia with COVID-19. Alibaba, used segmentation and quantification of lung infection regions to distinguish between COVID-19 with pneumonia and other pneumonia with an accuracy of 96%.

Generally, doctors spend more time to answer the basic question from COVID-19 infected patients. In this situation chat bots are helpful for the doctors to screen and diagnosis the patient's condition [27]. The deep learning applications like NLP, Computer Vision, Life Sciences and Epidemiology to fight against COVID-19 [28].

The hybrid deep learning methods are used to diagnose COVID-19 as shown in Fig. 4 . The diagram consists of five layers. They are Input Layer, Configuration Layer, Prediction and Processing Layer, Testing Layer and Result Layer.

Fig. 4.

Proposed deep learning methods for diagnose COVID-19.

Input Layer: The input layer is responsible to read the pre-processing image data from dataset. Also it used to read cough, temperature and blood sample data from the patients. The X-Ray and CT-Scan is utilized for pre-processing the lung images. Cropping and resizing the lung images prepared through pre-processing steps. The lung images are taken from medical devices like CT-Scan and X-ray having letters, crafts and medical symbols and the lung medical images also taken from different sources with different sizes. Because of these problems, pre-processing is important for image processing. Here, the input image size is changed to 224-by-224-by-3 which is width-by-height-by-channel number. Finally, the cropped input image does not contain any writing as much as possible, and the sample images are shown in Fig. 4.

Configuration Layer: This layer is responsible for reading image size, image resolution and buffer size etc. The camera sensor was used to read X-Ray and CT-Scan Data. The microphone sensor is used to measure the cough voice samples and laser-based sensor is used to predict blood count measures. Further reading and configuration data as considered for the symptoms of deep learning algorithm process.

Prediction and Processing Layer: The prediction layer is used to measure the symptoms level of the patients and processing layer is used to analyse the patient image data whether he/she is having COVID-19 or not. This layer is crucial layer for our proposed model in which most of the computation will be performed using RNN and CNN algorithm. The prediction and process layer internal process to diagnose COVID-19 as shown in Fig. 5 . The CT-Scan and X-Ray image input is fed into to convolution neural network process. The input image is processed and produces with n classification output with different threshold value. The RNN is a type of neural network algorithm which takes the input from the previous step output. The RNN process is continued throughout the process of convolutional neural network algorithm. A CNN is algorithm which takes input image and allocates weight and biases to numerous objects in the image to differentiate from one another. The core function this layer is to retrieve the features from image data set and to maintain the spatial relationship between image pixels. The features are retrieved from set of 16 filters and each filter construction is based upon 5 × 5 filter size. The CNN calculate dot product function which learns the convolved features which is same size of input image during training process and convolved images. The convolved images are trained to normalize the image data which reduce the training process and stabilizing the learning process. After normalization the convolved features are replacing with negative pixel value by zero. Further it is applied for extracting dominant features from the training model. It is done with pooling layer which are two types: Max pooling and average pooling. Here max pooling is used, because max polling performance is better than average pooling. Maximum value is returned from the part of the image covered by the filter using max pooling. This max pooling value is connected to all the activation function of fully connected layer. The responsibility of fully connected layer is to classify the convolved features from image data set into different classes.

Fig. 5.

Prediction and process layer internal process for diagnose COVID-19.

Testing Layer: Testing layer is used to test the different classes of data retrieved from prediction and processing layer. The major functionality of this layer is used to detect the corona virus condition which is normal, average or severe condition.

Result Layer: This is the final layer of the CNN model which produces the output value 0 or 1. The value 1 indicates COVID-19 positive and the value 0 indicates COVID-19 negative.

3.1. Materials and methods

3.1.1. Datasets

The datasets are collected from different sources to utilized for this work includes 106 lung X-Ray images acquired on 75 Corona Virus confirmed patients, and 1200 lung X-Ray images are diagnosed as Non-COVID-19 from 1002 patients. Similarly, 112 CT images are acquired from 80 COVID-19 confirmed patients, and 1354 CT images are diagnosed as Non-COVID-19 from 1094 patients. Further, the COVID-19 and pneumonia datasets are available in the Git-hub repository [29]. The screening performance is evaluated by the sensitivity, specificity and accuracy. The accuracy was used to measure the COVID-19 positive and negative results. The sensitivity and specificity was used to measure proportion of correctly identified positive and negative results.

3.1.2. Results and discussion

The data are spitted twice and conduct the experiment for evaluation. First X-Ray image split contains 50 images of Corona virus patients and 700 images for pneumonia patients. The second split contains 56 images for corona virus patients and 600 images for pneumonia patients. Similarly, CT Scan first split contains 60 images for Corona virus patients and 800 images for pneumonia patients. CT-Scan second split contains 52 images for Corona virus patients and 554 images for pneumonia patients. The average performance of two split considered as final performance.

The performance of COVID-19 is diagnosed by setting different threshold value from 0 to 1. The result layer indicates the value 0 becomes COVID-19 negative and value 1 becomes COVID-19 positive. Initially, the threshold value 0.5 is fixed for all the patient diagnosis. The final decision is based upon the following Eq. (1).

$$Diagnosis=\{\begin{array}{c}COVID-19if0.5\ge T\\ Non-COVIDif0.5<T\end{array}$$ (1)

Where T is threshold controls the trade-off between sensitivity and specificity. The following formula's which is used to measure the accuracy, sensitivity and specificity [4].

$$Accuracy=\frac{TruePositive\left(TP\right)+TrueNegative\left(TN\right)}{Totalnumberoftestedimages}$$ (20)

Where, true positive and true negative is the numbers were truly identified Corona virus and Non-Corona virus patients.

$$Sensitivity=\frac{TP}{TP+FalseNegative\left(FN\right)}$$ (3)

Where, FN is a false negative classification which incorrectly classified as Non-COVID 19 for CT-Scan and X-Ray images.

$$Specificity=\frac{TN}{TN+FalsePositive\left(FP\right)}$$ (4)

Where, FP is a false positive classification which misclassified as COVID 19 for CT-Scan and X-Ray images. So, the proposed method aims to reduce both false negative and false positive classification results. The following Eqs. (5) & 6 is utilized some weighting factors to minimize both false positive and false negative results. Further, Corona virus diagnosis for CT-Scan and X-Ray images has its basis parameter value with weighting coefficient values [W(CT), W(C), W(B), W(T)] or [W(X), W(C), W(B), W(T)] Where

• W(CT) → Coefficient weight of computed tomography image classification result.

• W(X) → Coefficient weight of X − Ray image classification result.

• W(C) → Coefficient weight of cough sample result.

• W(B) → Coefficient weight of blood sample count result.

• W(T) → Coefficient weight of body temperature result.

So, the COVID-19 result is assessed accurately based upon the above parameters values. The COVID-19 CT diagnosis is assessed by the following Eq. (5)

$$COVID-19CTDiagnosis=W_1\left(CT\right)+W_2\left(C\right)+W_3\left(B\right)+W_4\left(T\right)$$ (5)

Where W ₁ + W ₂ + W ₃ + W ₄ = 1. If computed diagnosis value is greater than its relative threshold (Eg.: 0.5), then the patient result is assumed to be positive, otherwise it is negative. Likewise, X-Ray diagnosis is assessed by the following equation.

$$COVID-19X-RayDiagnosis=W_1\left(X\right)+W_2\left(C\right)+W_3\left(B\right)+W_4\left(T\right)$$ (6)

The results in Tables 2 and 3 shows the X-Ray and CT-Scan image classification for Corona virus detection using RNN and CNN algorithm. The CT-Scan result is better performance compare to X-Ray result. The threshold value from 0.5 to 1 gives the different classification result from initial stage to final stage of COVID-19.

Table 2.

X-Ray image classification result using RNN and CNN for COVID-19 detection.

Threshold	True positive (COVID-19)	False positive (Non COVID-19)	True negative (COVID-19)	False negative (Non COVID-19)	Total number of tested images	Sensitivity (%)	Specificity (%)	Accuracy (%)
0.5	65	41	1115	85	1306	43.33	96.45	90.35
0.6	70	36	1124	76		47.94	96.89	91.42
0.7	75	31	1135	65		53.57	97.34	92.64
0.8	79	27	1148	52		60.30	97.70	93.95
0.9	85	21	1160	40		68.00	98.22	95.32
1	96	10	1180	20		82.75	99.15	97.70

Table 3.

CT-scan image classification result using RNN and CNN for COVID-19 detection.

Threshold	True positive (COVID-19)	False positive (Non COVID-19)	True negative (COVID-19)	False negative (Non COVID-19)	Total number of tested images	Sensitivity (%)	Specificity (%)	Accuracy (%)
0.5	88	24	1320	34	1466	72.13	98.21	96.04
0.6	91	21	1325	29		75.83	98.43	96.58
0.7	96	16	1330	24		80.00	98.81	97.27
0.8	100	12	1338	16		86.20	99.11	98.09
0.9	104	8	1342	12		89.65	99.40	98.63
1	108	4	1348	6		94.73	99.70	99.31

The X-Ray and CT-Scan image COVID-19 detection accuracy (%) versus threshold as shown in Fig. 6, Fig. 7 .

Fig. 6.

X-Ray image COVID-19detection accuracy (%) with respect to threshold.

Fig. 7.

CT- scan imageCOVID-19 detection accuracy (%) with respect to threshold.

4. Smart AI techniques for combating Covid-19

To find disease surveillance the BlueDot Company uses ML and NLP algorithm to predict the new hotspots and to inform the health officials and government through their health care reports. The predictive algorithms use the aviation data available, for predicting the risk that some hubs may face due to either arrival or departure of infected patients. The Canada based Stallion company uses NLP algorithm to build a Virtual Healthcare Assistant (ChatBots) can provide reliable information related to corona virus, answer the questions related corona virus, regularly monitor and check the symptoms of COVID-19 infected patients, and finally advice the patients to take rest at their home or whether they need to take hospital screening. The intelligent drones and robots play an important role for combating COVID-19. The drones are used to monitor whether the individuals they are using face mask or not, broadcast the precaution information about COVID-19 to publics and disinfect public spaces. Catering-industry centred Pudu Technology utilizes robots for food and medication delivery for COVID-19 infected patients in 40 hospitals. The robots are also utilized for room cleaning and disinfection of isolation wards. Various companies like AlphaFold System, Google's AI and DeepMind utilizes smart AI techniques for the growth of antibodies and vaccines for the novel corona virus in future [23].

The structure of corona virus proteins is predicted by Google's DeepMind which is useful for researchers to rapidly develop new drugs [19]. AI helps to diagnose the corona virus, Clear the doubts, delivery services and assisting drug detection to tackle during outbreak [30].

The AI can help fight against COVID-19 through population screening, AI powered smartphone apps to monitor individual health and spreading of virus [22].

5. Future medicine and healthcare for Covid-19

Microsoft and Allen Institute for AI speeding up vaccine research which aims to access the COVID- 19 from CORD-19 resources for scientists freely. BarabasiLab is looking for new drug development to fight against novel corona virus using machine learning and network science technologies [31]. The experimental lab has list of drugs to be tested in human cell as shown in Fig. 8 .

Fig. 8.

COVID-19 drug development using machine learning model [32].

AI alone is not the solution for COVID-19, because AI professional can aid to develop algorithms to predict and diagnosis COVID-19 disease. Without implementing AI we can't able to tackle the next epidemic. So, in future AI automation plays important role in healthcare. Fig 9 shows the future healthcare will be treated with the help of AI robotics like Receptionist and Administrative support Robot, Nurse Robot, Body Scanning Robot, Data based clinical judgment Robot, AI assisted surgery and Managing medical records. The AI professional doctor monitors robot's activity for giving treatment to the patient and another AI professional to monitor the health condition of the patient [31].

Fig. 9.

AI automation in healthcare [Source: Spiros Margaris/Twitter].

6. Conclusion

This work utilizes smart AI techniques to predict and diagnose the corona virus rapidly. The Qura Smart Ring is used to predict corona virus symptoms rapidly, within 24 h. In laboratory, corona virus rapid test is prepared with the help of deep learning model using RNN and CNN algorithm to diagnose the corona virus rapidly and accurately. The result shows the value 0 or 1. The value 1 indicates the person is affected with corona virus and the result zero indicates the person not affected with corona virus. Here threshold value is utilized for CT or X-Ray images classification to identify the patient condition from initial stage to severe stage. Threshold value 0.5 is used to identify corona virus initial stage condition and 1 is used to identify the corona virus severe condition of the patient. The proposed methods were utilized for four weighting parameters to reduce both false positive and false negative image classification results for diagnosis COVID-19 rapidly and accurately.

Declaration

Author certifies that this material or similar material has not been and will not be submitted to or published in any other publication before. Furthermore, Author certify that they have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript.

Declaration of Competing Interest

The author declares that they no conflict of interest. The author of this research acknowledge that they are not involved in any financial interest.

Biographies

Dr. Aswin, Working as Associate professor, College of Computer Science, VIT Bhopal. His-research interest includes Network Security, Software Defined Networks, Block Chain Technology, Networking, Wireless networks and Mobile Computing, IoT, Cloud Computing, and Deep learning. He is a member of various Academic committees, External Bodies and Research Divisions. As expertise is less it continues in the various National and International Publications in reputed Journals and international conference. He is guiding the Research Students in the area of Information and Communication Engineering at Different Universities. He is also editor and reviewer of various national and international Journals. He is contributing the academic and research experience in national and International Countries. He is also an active member in various research societies related to his domain expertise and also having cordial contact with active researchers in those domains.

Dr. Abdulrahman Saad Alqahtani, Currently Working as Associate Professor, College of Computing and Information Technology, Department of Computer Science, Bisha University, Kingdom of Saudi Arabia. His-research interest includes Network Security, Software Defined Networks, Block Chain Technology, Networking, Wireless networks and Mobile Computing, IoT, Cloud Computing, and Deep learning. He is a member of various Academic committees, External Bodies and Research Divisions. As expertise is less it continues in the various National and International Publications in reputed Journals and international conference. He is guiding the Research Students in the area of Information and Communication Engineering at Different Universities. He is also editor and reviewer of various national and international Journals. He is contributing the academic and research experience in national and International Countries. He is also an active member in various research societies related to his domain expertise and also having cordial contact with active researchers in those domains. In last fifteen years he has played a pivotal role in various technical symposiums and project Expo. He is also serving as a reviewer of many reputed Journals and reviewed quality papers of peer researchers in unbiased manner. He has also completed 2 new patents. He is also working on various international project funding agencies proposals to boost his research findings. Apart from this he continues his career as passionate Teacher and stay updated with current needs of IT industry and keeping himself updated and also taking active part in Industry-Academia collaborations in academics and research front.

Dr. Azath Mubarakali ,Currently Working as Associate Professor, College of computer Science, king Khalid University, Kingdom of Saudi Arabia. His-research interest includes Network Security, Software Defined Networks, Block Chain Technology, Networking, Wireless networks and Mobile Computing, IoT, Cloud Computing, and Deep learning. He is a member of various Academic committees, External Bodies and Research Divisions. As expertise is less it continues in the various National and International Publications in reputed Journals and international conference. He is guiding the Research Students in the area of Information and Communication Engineering at Different Universities. He is also editor and reviewer of various national and international Journals. He is contributing the academic and research experience in national and International Countries. He is also an active member in various research societies related to his domain expertise and also having cordial contact with active researchers in those domains. In last fifteen years he has played a pivotal role in various technical symposiums and project Expo. He is also serving as a reviewer of many reputed Journals and reviewed quality papers of peer researchers in unbiased manner. He has also completed 2 new patents. He is also working on various international project funding agencies proposals to boost his research findings. Apart from this he continues his career as passionate Teacher and stay updated with current needs of IT industry and keeping himself updated and also taking active part in Industry-Academia collaborations in academics and research front.