Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
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Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
An Analysis of The Methods Employed for Breast Cancer Diagnosis IJORCS
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Breast cancer research over the last decade has been tremendous. The ground breaking innovations and novel methods help in the early detection, in setting the stages of the therapy and in assessing the response of the patient to the treatment. The prediction of the recurrent cancer is also crucial for the survival of the patient. This paper studies various techniques used for the diagnosis of breast cancer. Different methods are explored for their merits and de-merits for the diagnosis of breast lesion. Some of the methods are yet unproven but the studies look very encouraging. It was found that the recent use of the combination of Artificial Neural Networks in most of the instances gives accurate results for the diagnosis of breast cancer and their use can also be extended to other diseases.
Coronavirus disease (COVID-19) is a pandemic disease, which has already caused
thousands of causalities and infected several millions of people worldwide. Any technological tool
enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the
healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the
Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires
specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative
in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI)
in the rapid and accurate detection of COVID-19 from chest X-ray images
Detect COVID-19 with Deep Learning- A survey on Deep Learning for Pulmonary M...JumanaNadir
Â
Who knew Deep Learning can come so handy to us during this period of global crisis?
There has yet been no vaccine or any effective treatment for the 2019 novel Coronavirus (COVID-19), but generative deep learning is helping in detecting and monitoring coronavirus patients by chest CT screening.
Recognition of Corona virus disease (COVID-19) using deep learning network IJECEIAES
Â
Corona virus disease (COVID-19) has an incredible influence in the last few months. It causes thousands of deaths in round the world. This make a rapid research movement to deal with this new virus. As a computer science, many technical researches have been done to tackle with it by using image processing algorithms. In this work, we introduce a method based on deep learning networks to classify COVID-19 based on x-ray images. Our results are encouraging to rely on to classify the infected people from the normal. We conduct our experiments on recent dataset, Kaggle dataset of COVID-19 X-ray images and using ResNet50 deep learning network with 5 and 10 folds cross validation. The experiments results show that 5 folds gives effective results than 10 folds with accuracy rate 97.28%.
An Analysis of The Methods Employed for Breast Cancer Diagnosis IJORCS
Â
Breast cancer research over the last decade has been tremendous. The ground breaking innovations and novel methods help in the early detection, in setting the stages of the therapy and in assessing the response of the patient to the treatment. The prediction of the recurrent cancer is also crucial for the survival of the patient. This paper studies various techniques used for the diagnosis of breast cancer. Different methods are explored for their merits and de-merits for the diagnosis of breast lesion. Some of the methods are yet unproven but the studies look very encouraging. It was found that the recent use of the combination of Artificial Neural Networks in most of the instances gives accurate results for the diagnosis of breast cancer and their use can also be extended to other diseases.
Coronavirus disease (COVID-19) is a pandemic disease, which has already caused
thousands of causalities and infected several millions of people worldwide. Any technological tool
enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to the
healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the
Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires
specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative
in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI)
in the rapid and accurate detection of COVID-19 from chest X-ray images
Detect COVID-19 with Deep Learning- A survey on Deep Learning for Pulmonary M...JumanaNadir
Â
Who knew Deep Learning can come so handy to us during this period of global crisis?
There has yet been no vaccine or any effective treatment for the 2019 novel Coronavirus (COVID-19), but generative deep learning is helping in detecting and monitoring coronavirus patients by chest CT screening.
INSIGHT ABOUT DETECTION, PREDICTION AND WEATHER IMPACT OF CORONAVIRUS (COVID-...ijaia
Â
The world is facing a tough situation due to the catastrophic pandemic caused by novel coronavirus (COVID-19). The number people affected by this virus are increasing exponentially day by day and the number has already crossed 6.4 million. As no vaccine has been discovered yet, the early detection of patients and isolation is the only and most effective way to reduce the spread of the virus. Detecting infected persons from chest X-Ray by using Deep Neural Networks, can be applied as a time and laborsaving solution. In this study, we tried to detect Covid-19 by classification of Covid-19, pneumonia and normal chest X-Rays. We used five different Convolutional Pre-Trained Neural Network models (VGG16,
VGG19, Xception, InceptionV3 and Resnet50) and compared their performance. VGG16 and VGG19 shows precise performance in classification. Both models can classify between three kinds of X-Rays with an accuracy over 92%. Another part of our study was to find the impact of weather factors (temperature, humidity, sun hour and wind speed) on this pandemic using Decision Tree Regressor. We found that temperature, humidity and sun-hour jointly hold 85.88% impact on escalation of Covid-19 and 91.89% impact on death due to Covid-19 where humidity has 8.09% impact on death. We also tried to predict the death of an individual based on age, gender, country, and location due to COVID-19 using the Logistic Regression, which can predict death of an individual with a model accuracy of 94.40%.
Pneumonia Classification using Transfer LearningTushar Dalvi
Â
Pneumonia can be life-threatening for people with weak immune systems, in which the alveoli filled with fluid that makes it hard to pass oxygen throughout the bloodstream. Detecting pneumonia is from a chest X-ray is not only expansive but also time-consuming for normal people. Throughout this research introduced a machine learning technique to classify pneumonia from Chest X-ray Images. Most of the medical datasets having class imbalance issues in the dataset. The Data augmentation technique used to reduce the class imbalance from the dataset, Horizontal Flip, width shift and height shift techniques used to complete the augmentation technique. Used VGG19 as a base architecture and ImageNet weights added for the transfer learning approach, also Removing initial layers and adding
some more dense layers helped to discover new possibilities. After testing the proposed model on testing data, we are able to achieve 98% recall and 82% of precision. As compare with state of the art technique, the proposed method able to achieve high
recall but that compromises with Precision.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
Â
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Developed Project with 3 more colleagues for Pneumonia Detection from Chest X-ray images using Convolutional Neural Network. Used confusion matrix, Recall, Precision for check the model performance on testing Data
NCCR 2020: Conference Of Very Important Disease (COVID-19) | 24 - 26 August 2020
Young Investigator Awards Presentation
Kim-Ann Git1, Aida binti Abdul Aziz2, Lau Kiew Siong3, Lau Song Lung3, Preetvinder Singh a/l Dheer Singh4, Tan Ying Sern5, Eric Chung6
1-Selayang Hospital
2-Sungai Buloh Hospital
3-Sarawak General Hospital
4-Hospital Raja Permaisuri Bainun
5-Taiping Hospital
6-University of Malaya Medical Centre
https://doi.org/10.5281/zenodo.4004461
A Review on Brain Disorder Segmentation in MR ImagesIJMER
Â
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and AssessmentâŠ. And many more.
In this work, we describe the field research, design, and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described here are threefold: 1) The design of an advanced visual interface for multimodal diagnosis of breast cancer (BreastScreening); 2) Insights from the field comparison of Single-Modality vs Multi-Modality screening of breast cancer diagnosis with 31 clinicians and 566 images; and 3) The visualization of the two main types of breast lesions in the following image modalities: (i) MammoGraphy (MG) in both Craniocaudal (CC) and Mediolateral oblique (MLO) views; (ii) UltraSound (US); and (iii) Magnetic Resonance Imaging (MRI).
What is Deep Learning and how it helps to Healthcare Sector?Cogito Tech LLC
Â
To know what is Deep Learning and how it helps to Healthcare Sector check this presentation that shows the top use cases of deep learning process of this technology backed systems, applications or machines in the healthcare industry. The entire presentation shows the deep learning definition and how it is changing the healthcare industry. This PPT is represented by Cogito to get to know the role of deep learning in healthcare as Cogito is providing the training data sets for deep learning and machine learning with best accuracy.
Visit: http://bit.ly/2QRrSc2
Lung Cancer Detection using Machine Learningijtsrd
Â
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors thatâs why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
INSIGHT ABOUT DETECTION, PREDICTION AND WEATHER IMPACT OF CORONAVIRUS (COVID-...ijaia
Â
The world is facing a tough situation due to the catastrophic pandemic caused by novel coronavirus (COVID-19). The number people affected by this virus are increasing exponentially day by day and the number has already crossed 6.4 million. As no vaccine has been discovered yet, the early detection of patients and isolation is the only and most effective way to reduce the spread of the virus. Detecting infected persons from chest X-Ray by using Deep Neural Networks, can be applied as a time and laborsaving solution. In this study, we tried to detect Covid-19 by classification of Covid-19, pneumonia and normal chest X-Rays. We used five different Convolutional Pre-Trained Neural Network models (VGG16,
VGG19, Xception, InceptionV3 and Resnet50) and compared their performance. VGG16 and VGG19 shows precise performance in classification. Both models can classify between three kinds of X-Rays with an accuracy over 92%. Another part of our study was to find the impact of weather factors (temperature, humidity, sun hour and wind speed) on this pandemic using Decision Tree Regressor. We found that temperature, humidity and sun-hour jointly hold 85.88% impact on escalation of Covid-19 and 91.89% impact on death due to Covid-19 where humidity has 8.09% impact on death. We also tried to predict the death of an individual based on age, gender, country, and location due to COVID-19 using the Logistic Regression, which can predict death of an individual with a model accuracy of 94.40%.
Pneumonia Classification using Transfer LearningTushar Dalvi
Â
Pneumonia can be life-threatening for people with weak immune systems, in which the alveoli filled with fluid that makes it hard to pass oxygen throughout the bloodstream. Detecting pneumonia is from a chest X-ray is not only expansive but also time-consuming for normal people. Throughout this research introduced a machine learning technique to classify pneumonia from Chest X-ray Images. Most of the medical datasets having class imbalance issues in the dataset. The Data augmentation technique used to reduce the class imbalance from the dataset, Horizontal Flip, width shift and height shift techniques used to complete the augmentation technique. Used VGG19 as a base architecture and ImageNet weights added for the transfer learning approach, also Removing initial layers and adding
some more dense layers helped to discover new possibilities. After testing the proposed model on testing data, we are able to achieve 98% recall and 82% of precision. As compare with state of the art technique, the proposed method able to achieve high
recall but that compromises with Precision.
Early detection of breast cancer using mammography images and software engine...TELKOMNIKA JOURNAL
Â
The breast cancer has affected a wide region of women as a particular case. Therefore, different researchers have focused on the early detection of this disease to overcome it in efficient way. In this paper, an early breast cancer detection system has been proposed based on mammography images. The proposed system adopts deep-learning technique to increase the accuracy of detection. The convolutional neural network (CNN) model is considered for preparing the datasets of training and test. It is important to note that the software engineering process model has been adopted in constructing the proposed algorithm. This is to increase the reliably, flexibility and extendibility of the system. The user interfaces of the system are designed as a website used at country side general purpose (GP) health centers for early detection to the disease under lacking in specialist medical staff. The obtained results show the efficiency of the proposed system in terms of accuracy up to more than 90% and decrease the efforts of medical staff as well as helping the patients. As a conclusion, the proposed system can help patients by early detecting the breast cancer at far places from hospital and referring them to nearest specialist center.
Developed Project with 3 more colleagues for Pneumonia Detection from Chest X-ray images using Convolutional Neural Network. Used confusion matrix, Recall, Precision for check the model performance on testing Data
NCCR 2020: Conference Of Very Important Disease (COVID-19) | 24 - 26 August 2020
Young Investigator Awards Presentation
Kim-Ann Git1, Aida binti Abdul Aziz2, Lau Kiew Siong3, Lau Song Lung3, Preetvinder Singh a/l Dheer Singh4, Tan Ying Sern5, Eric Chung6
1-Selayang Hospital
2-Sungai Buloh Hospital
3-Sarawak General Hospital
4-Hospital Raja Permaisuri Bainun
5-Taiping Hospital
6-University of Malaya Medical Centre
https://doi.org/10.5281/zenodo.4004461
A Review on Brain Disorder Segmentation in MR ImagesIJMER
Â
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and AssessmentâŠ. And many more.
In this work, we describe the field research, design, and comparative deployment of a multimodal medical imaging user interface for breast screening. The main contributions described here are threefold: 1) The design of an advanced visual interface for multimodal diagnosis of breast cancer (BreastScreening); 2) Insights from the field comparison of Single-Modality vs Multi-Modality screening of breast cancer diagnosis with 31 clinicians and 566 images; and 3) The visualization of the two main types of breast lesions in the following image modalities: (i) MammoGraphy (MG) in both Craniocaudal (CC) and Mediolateral oblique (MLO) views; (ii) UltraSound (US); and (iii) Magnetic Resonance Imaging (MRI).
What is Deep Learning and how it helps to Healthcare Sector?Cogito Tech LLC
Â
To know what is Deep Learning and how it helps to Healthcare Sector check this presentation that shows the top use cases of deep learning process of this technology backed systems, applications or machines in the healthcare industry. The entire presentation shows the deep learning definition and how it is changing the healthcare industry. This PPT is represented by Cogito to get to know the role of deep learning in healthcare as Cogito is providing the training data sets for deep learning and machine learning with best accuracy.
Visit: http://bit.ly/2QRrSc2
Lung Cancer Detection using Machine Learningijtsrd
Â
Modern three dimensional 3 D medical imaging offers the potential and promise for major advances in science and medicine as higher fidelity images are produced. Due to advances in computer aided diagnosis and continuous progress in the field of computerized medical image visualization, there is need to develop one of the most important fields within scientific imaging. From the early basis report on cancer patients it has been seen that a greater number of people die of lung cancer than from other cancers such as colon, breast and prostate cancers combined. Lung cancer are related to smoking or secondhand smoke , or less often to exposure to radon or other environmental factors thatâs why this can be prevented. But still it is not yet clear if these cancers can be prevented or not. In this research work, approach of segmentation, feature extraction and Convolution Neural Network CNN will be applied for locating, characterizing cancer portion. Harpreet Singh | Er. Ravneet Kaur | "Lung Cancer Detection using Machine Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-6 , October 2020, URL: https://www.ijtsrd.com/papers/ijtsrd33659.pdf Paper Url: https://www.ijtsrd.com/computer-science/computer-architecture/33659/lung-cancer-detection-using-machine-learning/harpreet-singh
Validation of Clinical Artificial Intelligence: Where We Are and Where We Are...Sean Manion PhD
Â
This is the deck from a presentation I gave to the Pittsburgh Industrial Statisticians Association (PISA) for their PISA23 event in a session on Artificial Intelligence and Machine Learning.
The deck itself is not intended to be stand alone without the accompanying verbal presentation, however many of the slides contain key elements with references, and my contact information is available at the end if anyone has questions.
[DSC Croatia 22] Writing scientific papers about data science projects - Mirj...DataScienceConferenc1
Â
Data science is not only about numbers and how to crunch them; it is also about how to communicate project results with the various audience. Scientific journals and conferences are an excellent venue for getting a wider audience reach and gathering valuable comments. The talk will answer the questions: How to structure a scientific paper in data science? What are relevant venues for showcasing your work to gain the most relevant reach? To demystify the process of scientific writing, the case study will be presented: Messy process: Story of the birth of one data science paper.
Service innovation and performance-based evaluation of university libraries i...Muhammad Yousuf Ali
Â
This presentation was presented
PhD Open Defense presentation at The Islamia University Bahawalpur on 31 July 2023. The title PhD study was "Service innovation and performance-based evaluation of university libraries in the
age of Artificial Intelligence". The PhD scholar successfully defended his dissertation.
Top 5 most viewed articles from academia in 2019 - gerogepatton
Â
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications
April 2023-Top Cited Articles in ACII-24.pdfaciijournal
Â
Easy Home or Home automation plays a very important role in modern era because of its flexibility in
using it at different places with high precision which will save money and time by decreasing human
hard work. Prime focus of this technology is to control the household equipmentâs like light, fan, door,
AC etc. automatically. This research paper has detailed information on Home Automation and Security
System using Arduino, GSM and how we can control home appliances using Android application.
Whenever a person will enter into the house then the count of the number of persons entering in the
house will be incremented, in Home Automation mode applicances will be turned on whereas in
security light will be turned on along with the alarm. The count of the number of persons entering the
house is also displayed on the LCD screen. In Home Automation mode when the room will become
empty i.e. the count of persons reduces to zero then the applicances will be turned off making the
system power efficient. Moreover a person can control his home appliances by using an android
application present in his mobile phone which will reduce the human hard work. At the same time if
anyone enters while security mode is on a SMS will be sent to house ownerâs mobile phone which will
indicate the presence of a person inside the house.The alarm can be turned of using SMS or Android
application.
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SafeAssign Originality Report
Digital Forensics Tools & Tchq - 202040 - CRN127 - Rucker âą Week Eight Assignment
%74Total Score: High riskVenkatesh Bodhupally
Submission UUID: 680cd83f-65c1-b609-7c13-c42c95f8db1c
Total Number of Reports
1
Highest Match
74 %
forensictools.docx
Average Match
74 %
Submitted on
04/30/20
05:27 PM EDT
Average Word Count
564
Highest:Â forensictools.docx
%74Attachment 1
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Student paper Student paper
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View Originality Report - Old Design
Word Count: 564
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https://blackboard.nec.edu/webapps/mdb-sa-BB5b75a0e7334a9/originalityReport?attemptId=4b21db19-c753-4a4c-bf5f-5fa5c168286f&course_id=_47023_1&download=true&includeDeleted=true&print=true&force=true
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Running Head: INVESTIGATIONS AND FORENSICS 1
INVESTIGATIONS AND FORENSICS 4
Tools in Memory Forensics
Venkatesh Bodhupally
NEC.
Some of the tools applicable in the collection of live memory images in media include; volatility suite (Htun, Thwin & San, 2018). This tool or program analyzes the
RAM and has support from different operating systems such as Linux and windows. RAW and VMWare are also analyzable by this tool, with no issues arising. Rekall is
a tool used by investigators and responders since it features in analyzing other tools and acquiescing them. It's not a single application but a forensic framework
(SocaĆa & Cohen, 2016). Helix ISO, a live disk that helps in capturing of memory images in a system and memory dumping. This type of tool has some risks associated
with it that make it not able to run directly into a system such as acquisition footprint Other tools include; process hacker which is an application that monitors
application, and it can be run when the machine that is on target is on use. The tool makes an investigator understand the issue affecting the system before a
snapshot of the memory is taken (Eden, Pontypridd, Cherdantseva, & Stoddart, 2016). The tool can also help in uncovering processes that are malicious and in
identifying terminated processes in a set period. Investigators also use or can use Belk soft RAM capture, which allows capturing of the volatile section of system
memory into a file. Belksoft RAM capture is a criminology device that has a free unpredictable memory, and it is used in catching the live RAM. Belksoft RAM capture
has drivers worth 32-bit and 64-bit; that's why this tool is used in overcoming anti-debugging as well as anti-dumping systems. Ftk Imager is a tool that catches the live
RAM. At a time picture, this type of tool makes a tiny bit alongside slack space. This type of tool is not capable of dividing or dissecting the memory dump that is
caught (Venkateswara Rao, & Ch.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Â
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
Â
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Â
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
How to Add Chatter in the odoo 17 ERP ModuleCeline George
Â
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
1. Research Data Session 2020-21
List of published articles in journals:
1. Tiwari, Shamik. "Dermatoscopy Using Multi-Layer Perceptron, Convolution Neural
Network, and Capsule Network to Differentiate Malignant Melanoma From Benign
Nevus." International Journal of Healthcare Information Systems and Informatics
(IJHISI) 16.3 (2021): 58-73.
Indexed In: Web of Science Emerging Sources Citation Index (ESCI), SCOPUS.
2. Tiwari, Shamik. "An Ensemble Deep Neural Network Model for Onion-Routed Traffic
Detection to Boost Cloud Security." International Journal of Grid and High Performance
Computing (IJGHPC) 13.1 (2021): 1-17.
Indexed In: Web of Science Emerging Sources Citation Index (ESCI), SCOPUS.
3. Tiwari, Shamik. "Multiclass Content-based Visual Information Retrieval using Multi Layer
Perceptron and Convolution Neural Network: A Comparative Study." Journal of Artificial
Intelligence Research & Advances 7.1 (2020): 1-9.
4. Tiwari, Shamik. "A Comparative Study of Deep Learning Models With Handcraft Features
and Non-Handcraft Features for Automatic Plant Species Identification." International Journal
of Agricultural and Environmental Information Systems (IJAEIS) 11.2 (2020): 44-57.
Indexed In: Web of Science Emerging Sources Citation Index (ESCI), SCOPUS.
5. Tiwari, Shamik. "A Blur Classification Approach Using Deep Convolution Neural
Network." International Journal of Information System Modeling and Design (IJISMD) 11.1
(2020): 93-111.
Indexed In: Web of Science Emerging Sources Citation Index (ESCI), SCOPUS.
Book chapter
1. Tiwari, Shamik, Varun Sapra, and Anurag Jain. "Heartbeat sound classification using Mel-
frequency cepstral coefficients and deep convolutional neural network." Advances in
Computational Techniques for Biomedical Image Analysis. Academic Press, 2020. 115-
131.
https://www.sciencedirect.com/science/article/pii/B9780128200247000062
List of accepted articles in journals:
1. Anurag Jain, Shamik Tiwari, Tanupriya Choudhury, Bhupesh Kumar Dewangan. âGradient
and statistical features based prediction system for COVID-19 using chest X-ray imagesâ.
International Journal of Computer Applications in Technology. Inder Science.
Indexed In: Web of Science Emerging Sources Citation Index (ESCI), SCOPUS.
2. Shamik Tiwari, Anurag Jain. âConvolutional Capsule Network for Covid-19 Detection
Using Radiography Imagesâ. International Journal of Imaging Systems and Technology,
Willey.
Indexed In: Web of Science Citation Index (SCI).
2. Academic Excellence
1. Coordinating academic monitoring in department
2. Curriculum development
3. Member of core committee for NAAC/NBA documentation
4. Coordinated first year orientation program for UG and PG bacthes
5. Served as member of Technical Program Committee (TPC) and session chair in
MIDAS-2020 conference.
6. Delivered a lecture entitled âComputer Vision in Healthcare Applicationsâ at Unifacvest
International Global Talks Webinar: Artificial Intelligence and Health promoted by
Unifacvest University, Brazil on August 29, 2020, through its EdTech, with duration
of four hours.
Successfully completed and achieved certification for following MOOC courses
through Coursera
1. MOOC and Blended Learning (authorized by Tsinghua University)
2. Teaching Character and Creating Positive Classrooms (authorized by Relay
Graduate School of Education)
3. Image Denoising Using AutoEncoders in Keras and Python (authorized by Rhyme)
4. Python for Genomic Data Science (authorized by Johns Hopkins University)
5. Cloud Computing Basics (Cloud 101) (authorized by LearnQuest)
6. Image Classification with CNNs using Keras (authorized by Rhyme)
7. Foundations of Virtual Instruction (authorized by University of California, Irvine)
8. Advanced Instructional Strategies in the Virtual Classroom (authorized by
University of California, Irvine)
9. Performance Assessment in the Virtual Classroom (authorized by University of
California, Irvine)
10. Assessment in Higher Education: Professional Development for Teachers
(authorized by Erasmus University Rotterdam)
11. Virtual Teacher Final Project (authorized by University of California, Irvine)
12. COVID-19: What You Need to Know (CME Eligible) (authorized by Osmosis)
13. Emerging Trends & Technologies in the Virtual K-12 Classroom (authorized by
University of California, Irvine)
14. Linear Regression with NumPy and Python (authorized by Rhyme)
15. Logistic Regression with Python and Numpy (authorized by Rhyme)
16. Anomaly Detection in Time Series Data with Keras (authorized by Rhyme)
17. Traffic Sign Classification Using Deep Learning in Python/Keras (authorized by
Rhyme)
18. Classification with Transfer Learning in Keras (authorized by Rhyme)
Reviewer:
1. Computers in Biology and Medicine (Elsevier)
2. ISA Transactions (Elsevier)
3. Indian Journal of Science and Technology
4. J. for International Business and Entrepreneurship Development (JIBED).
5. Journal of Applied Research on Public Health Management (IJARPHM).
3. 6. International Journal of Service Science, Management, Engineering, and
Technology (IJSSMET).
7. International Journal of Healthcare Information Systems and Informatics (IJHISI).
8. First International Conference on Artificial Intelligence and Sustainable Computing
for Smart Cities (AIS2C2)
List of published articles in journals:
1. Tiwari, Shamik. "Dermatoscopy Using Multi-Layer Perceptron, Convolution Neural
Network, and Capsule Network to Differentiate Malignant Melanoma From Benign Nevus."
International Journal of Healthcare Information Systems and Informatics (IJHISI) 16.3 (2021):
58-73.
Indexed In: Web of Science Emerging Sources Citation Index (ESCI), SCOPUS.
2. Tiwari, Shamik. "An Ensemble Deep Neural Network Model for Onion-Routed Traffic
Detection to Boost Cloud Security." International Journal of Grid and High Performance
Computing (IJGHPC) 13.1 (2021): 1-17.
Indexed In: Web of Science Emerging Sources Citation Index (ESCI), SCOPUS.
3. Tiwari, Shamik. "Multiclass Content-based Visual Information Retrieval using Multi Layer
Perceptron and Convolution Neural Network: A Comparative Study." Journal of Artificial
Intelligence Research & Advances 7.1 (2020): 1-9.
4. Tiwari, Shamik. "A Comparative Study of Deep Learning Models With Handcraft Features
and Non-Handcraft Features for Automatic Plant Species Identification." International Journal
of Agricultural and Environmental Information Systems (IJAEIS) 11.2 (2020): 44-57.
Indexed In: Web of Science Emerging Sources Citation Index (ESCI), SCOPUS.
5. Tiwari, Shamik. "A Blur Classification Approach Using Deep Convolution Neural
Network." International Journal of Information System Modeling and Design (IJISMD) 11.1
(2020): 93-111.
Indexed In: Web of Science Emerging Sources Citation Index (ESCI), SCOPUS.
Book chapter
1. Tiwari, Shamik, Varun Sapra, and Anurag Jain. "Heartbeat sound classification using
Mel-frequency cepstral coefficients and deep convolutional neural network." Advances in
Computational Techniques for Biomedical Image Analysis. Academic Press, 2020. 115-131.
https://www.sciencedirect.com/science/article/pii/B9780128200247000062
4. List of accepted articles in journals:
1. Anurag Jain, Shamik Tiwari, Tanupriya Choudhury, Bhupesh Kumar Dewangan. âGradient
and statistical features based prediction system for COVID-19 using chest X-ray imagesâ.
International Journal of Computer Applications in Technology. InderScience.
Indexed In: Web of Science Emerging Sources Citation Index (ESCI), SCOPUS.
2. Shamik Tiwari, Anurag Jain. âConvolutional Capsule Network for Covid-19 Detection
Using Radiography Imagesâ. International Journal of Imaging Systems and Technology,
Willey.
Indexed In: Web of Science Citation Index (SCI).
Guiding Ph.D. students
Reviewer:
1. Computers in Biology and Medicine (Elsevier)
2. ISA Transactions (Elsevier)
3. Indian Journal of Science and Technology
4. J. for International Business and Entrepreneurship Development (JIBED).
5. Journal of Applied Research on Public Health Management (IJARPHM).
6. International Journal of Service Science, Management, Engineering, and Technology
(IJSSMET).
7. International Journal of Healthcare Information Systems and Informatics (IJHISI).
8. First International Conference on Artificial Intelligence and Sustainable Computing for
Smart Cities (AIS2C2)
5. 1. Coordinating academic monitoring in department
2. Curriculum development
3. Member of core committee for NAAC/NBA documentation
4. Coordinated first year orientation program for UG and PG batches
5. Served as member of Technical Program Committee (TPC) and session chair in
MIDAS-2020 conference.
6. Delivered a lecture entitled âComputer Vision in Healthcare Applicationsâ at Unifacvest
International Global Talks Webinar: Artificial Intelligence and Health promoted by Unifacvest
University, Brazil on August 29, 2020, through its EdTech, with duration of four hours.
7. Efficiently adopted and utilized online teaching through blackboard
8. Achieved 4+ score as student feedback during academic year for all courses
9. Activity coordinator for GG batch
10. Mentoring students
11. Supporting higher studies for students by issuing LOR
12. Guiding Ph.D. students
