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The Agadir seismic database is fed by a local seismic network of five stations. The latter belongs to the national seismic network of Morocco. Three types of seismic events are recorded on daily basis: earthquakes, quarry blasts, and other undesired seismic events which are considered as noise. A quantity of data is currently available. The aim of this study is to highlight the degree of similarity that may exist between these different seismic events. This similarity could help in many studies including classification of these seismic events. The cross-correlation function, commonly used in signal theory, is used to quantify this similarity and compare the obtained results. The cross-correlation function is firstly applied to synthetic signals to clearly demonstrate its behavior versus signal parameter variation, and then to real seismic signals. The obtained results show that quarry blast signals are more correlated than those of earthquakes. This is explained by different factors. This relative correlation that exists between quarry blast signals may be exploited to develop an identification task.

Cross-correlation, Seismic signal, Similarity, Classification

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E.H. Ait Laasri, A. Atmani, D. Agliz, E. Akhouayri, "Application of Cross-Correlation to Seismic Signal Database of Agadir," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.1-8, 2021.

Joni and Erwin, Abstract, Artificial Intelligence (AI) has been widely used in analyzing objects, such as text, image, etc. There are many things that can be analyzed from images for the needs of identifying and classifying objects into certain types. One of the identifiable data is color. To identify the main color of an object, a vehicle image (car) requires a very complex analysis process. In this study, the identification process was carried out using an image center area analysis approach. This is based on the perception that the main color is in the middle of the object area. All color pixels in the analyzed area are converted to color names using the KDTree library. The segmentation process will produce several groups of color values. From the color matrix that has been through the segmentation process, the color identity of the object is obtained, which is determined by the mode value of the color matrix.

Color Segmentation, Color Identification, KDTree Library, Car Color

[1] Astha Pathak, Avinash Dhole, "Image classification Method in detecting Lungs Cancer using CT images: A Review," International Journal of Computer Sciences and Engineering, Vol.9, Issue.5, pp.37-42, 2021.
[2] Yash Baid, Avinash Dhole, "Food Image Classification Using Machine Learning Techniques: A Review," International Journal of Computer Sciences and Engineering, Vol.9, Issue.5, pp.31-36, 2021.
[3] M. Lalitha, M. Kiruthiga, and C. Loganathan, “A survey on image segmentation through clustering algorithm,” International Journal of Science and Research, vol. 2, no. 2, pp. 348–358, 2013.
[4] N. Sharma, M. Mishra, and M. Shrivastava, “Colour image segmentaion techniques and issues: an approach,” International Journal of Scientific & Technology Research, vol. 1, no. 4, pp. 9–12, 2012.
[5] L. Busin, N. Vandenbroucke, and L. Macaire, “Color spaces and image segmentation,” Advances in Imaging and Electron Physics, vol. 151, pp. 65–168, 2008.
[6] R. Rulaningtyas, A. B. Suksmono, T. L. R. Mengko, G. A. P. Saptawati, “Segmentasi Citra Berwarna dengan Menggunakan Metode Clustering Berbasis Patch untuk Identifikasi Mycobacterium Tuberculosis” (Color Image Segmentation Using Patch-Based Clustering Method for Identification of Mycobacterium Tuberculosis), Jurnal Biosains Pascasarjana, Vol. 17, No. 1, pp. 19-25, August 2015.
[7] M. G. Alfianto, R. N. Whidhiasih, Maimunah, “Identifikasi Beras Berdasarkan Warna Menggunakan Adaptive Neuro Fuzzy Inference System” (Rice Identification Based on Color Using Adaptive Neuro Fuzzy Inference System), Jurnal Penelitian Ilmu Komputer, Sistem Embedded & Logic, Vol. 5, No. 2, pp. 51-59, 2017.

Joni, Erwin, "Image Color Segmentation With Kdtree Library For Car Color Identity Classification," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.9-12, 2021.

The logs in a network are not bound to be perfect perpetually. The behavior of the network traffic is bound to deviate from the expected one sometimes and when that occurs, the traffic is said to be anomalous. Anomalous traffic can be problematic for various reasons, be it external attacks, or transfer of outdated data or even serving customers for networking companies. When the network size is at a large scale, this becomes an even bigger problem to tackle. The anomaly detection systems currently in place are either trained with aging datasets or are not able to handle large loads efficiently. Hence arises the need for a scalable solution which can provide security to a network by detecting anomalies in it and alerting with quick response when an anomaly occurs by learning from its past behavior. The paper offers an end-to-end solution for the introduction of an anomaly detection system using machine learning into an enterprise environment, right from the collection of logs to the generation of alerts, effectively. This is implemented with an infrastructure that includes Elasticsearch, Logstash and Kibana along with the added feature of Machine Learning.

Alerting, anomaly detection, machine learning, networks

[1] S. N. Hussain, N. R. Singha., “A Survey on Cyber Security Threats and their Solutions”, International Journal for Research in Applied Science and Engineering Technology, Vol. 8, Issue. 7, pp. 1141-1146, 2020.
[2] M. Zamani, “Machine Learning Techniques for Intrusion Detection”, arXiv:1312.2177, 2013.
[3] O. Kononenko, O. Baysal, R. Holmes and M. W. Godfrey, “Mining modern repositories with Elasticsearch”, In the Proceedings of the 2014 Conference on Mining Software Repositories, India, pp. 328–331, 2014.
[4] N. Shah, D. Willick and V. Mago, “A framework for social media data analytics using Elasticsearch and Kibana”, Wireless Networks, Vol. 24, Issue.8, pp. 1-9, 2018.
[5] Sharma, Chalsi and Maurya, Satish, “A Review: Importance of Cyber Security and its challenges to various domains”, International Journal of Technical Research & Science Special(Issue.3), pp. 46-54, 2020.
[6] X. Shu, K. Tian, A. Ciambrone and D. Yao, “Breaking the Target: An Analysis of Target Data Breach and Lessons Learned”, arXiv:1701.04940, 2017.
[7] F. Salo, M. Injadat, A. B. Nassif, A. Shami and A. Essex, “Data Mining Techniques in Intrusion Detection Systems: A Systematic Literature Review”, in IEEE Access, Vol. 6, pp. 56046-56058, 2018.
[8] S. D. Bhattacharjee, Y. Junsong, Z. Jiaqi, Y. Tan, “Context-Aware Graph-Based Analysis for Detecting Anomalous Activities”, In the Proceedings of the 2017 IEEE International Conference on Multimedia and Expo (ICME), China, pp. 1021-1026, 2017.
[9] L. Cheng, F. Liu and D.Yao, “Enterprise data breach: causes, challenges, prevention, and future directions”, WIREs: Data Mining and Knowledge Discovery, Vol. 7, Issue.5, 2017.
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[11] P. P. Bavaskar, O. Kemker and A. Sinha, “A SURVEY ON: "LOG ANALYSIS WITH ELK STACK TOOL", International Journal of Research and Analytical Reviews (IJRAR), Vol. 6, Issue.4, pp. 965-968, 2019.
[12] S. J. Son and Y. Kwon, "Performance of ELK stack and commercial system in security log analysis", ”, In the Proceedings of the 2017 IEEE 13th Malaysia International Conference on Communications (MICC), Malaysia, pp. 187-190, 2017.

Ruchita R Biradar, Nagaraja G.S., "Anomalous Traffic Detection System for Enterprise using Elastic stack with Machine Learning," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.13-18, 2021.

Panchang is a traditional Indian Almanac that has been in practice for over 5 millennia. Important meteorological predictions have been fossilised by this method. The Panchang predictions are approximated for a small area, based on astrological occurrences like effects on weather when different celestial bodies come close to one another etc. Panchang consists of 5 components, namely Tithi, Vaara, Nakshatra, Yoga and Karana, along with many different terminologies. Panchang predictions are carried out using some observed rules. Extensive study of these rules has been performed and then an attempt has been made to write a program which incorporates a set of rules and regulations. The main aim was to predict what type of rainfall will be observed on a particular day based on Planetary and Nakshatra positions. A basic implementation of two Panchang types (In python language) has been done. One being Traditional Panchang and the other- Tamil Panchangam. Both the methods mostly use similar rules and logic, but Traditional Panchang allows prediction for a shorter time period accurately and Tamil Panchangam provides accurate predictions for the yearly time period.

Panchang, Conjunctions, Nakshatras, Ecliptic Longitude, Right Ascension

[1] Kaluvagunta, Vanadeep & Moorthy, Sadasiva & Musali, Krishnaiah. “Meteorological predictions preserved in the Panchangam versus real-time observations – a case study over Tirupati region – a semi-arid tropical site in India.” Indian Journal of Science and Technology. pp.2491-2509, 2012.
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[7] Kanani PR and Pastakia Astad “Everything is written in the sky! Participatory meteorological assessment and prediction based on traditional beliefs and indicators in Saurashtra”. J. Asian Int. Bioethics. Vol.9, pp.170, 1999.
[8] Satguru Sivaya Subramuniyaswami, “Vedic Calendar – The Kadavul Hindu Panchangam”, The Saivite Series, Himalayan Academy, Kapaa, Hawaii.
[9] De US, Joshi UR & Prakasa Rao GS, “Nakshatra based rainfall climatology”, Mausam, Vol.55, Issue.2, pp 305, 2004.
[10] Vaidya, V.B., Kedar, D., and Vyas, P., Report on Validation of rainfall forecast given by AAU Monsoon Research Almanac-2011”. International Society for Agrometeorology (INSAM) Accounts of operational agro meteorology”, pp.1- 4, 2011.
[11] Mishra, S.K. V.K. Dubey and R.C. Pandey.“Rain Forecasting in Indian Almanacs (Panchangs): a case for making Krishi-Panchang”. Asian Agri-History, Vol.6 Issue.1, pp.29-42, 2002.

Kartik Jawanjal, Mayank Modi, Aditya Panchwagh, Nagesh Jadhav, "Weather Prediction Using Indian Almanac Rules," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.19-24, 2021.

Induction heating is widely used now a day because of its high efficiency and clean operation. Induction heating system utilizes electricity for the generation of heat even though solar energy is the largely available energy source. The present work of combining solar energy with induction heat generation technique is the efficient solution for heat generation application. It is derived from the principle of electromagnetic induction. The solar energy is the main source used to produce heat but one cannot rely on solar energy throughout the year. This project helps to develop a model called HYBRID STOVE in which cooking can be switched to biogas during cloudy days or rainy seasons. The other thing which one needs to take care of while using gas is the possibility of gas leakage. The aim of this project is to build a stove with the energy coming from both solar source and biogas and if there will be any gas leakage, it will be detected by MQ5 sensor and notified to the user when the threshold limit is crossed using Arduino.

Solar energy; biogas; MQ5 gas sensor; Arduino

[1].Vikash Kumar Singh, Md. Mumtaz Khan, Suresh Sevliya, “A Review on induction heating system, SSRG International Journal of Electrical and Electronics Engineering (SSRG-IJEEE) – volume 3 Issue 5 May 2016
[2].Ms.Shwetha Laxman Hajare, Ms.Narendra Navinkumar Jakka, Ms. Dipali Namdev Shinde, Ms. Kajal Jaiprakash Rajput, “A Review on Solar Based Induction Heater”, IJIRT, Volume 6, Issue 12, May 2020.
[3]. Dr. Smt. G. PRASANTHI and Y.GURAVAIAH, “Fabrication of Solar Powered Automatic Induction Heater”, VOLUME 4 I ISSUE 4 I OCT. – DEC 2017.
[4]. M Athish Subramanian, Naveen Selvam, Rajkumar S, R Mahalakshmi, J Ramprabhakar, “Gas Leakage Detection System using IoT with integrated notifications using Pushbullet-A Review”.
[5]. V. Naren, P. Indrajith, R. Aravind Prabhu, C S Sundar Ganesh. “Intelligent Gas Leakage Detection System with IOT Using Esp. 8266 Module”. International Journal of Advanced Research in Electrical, Vol. 7, No. 12, December 2018.
[6]. Chaitali Bagwe, Vidya Ghadi, Vinayshri Naik, Neha Kunte. “IOT Based Gas Leakage Detection System with Database Logging, Prediction and Smart Alerting”. IOSR Journal of Engineering. Vol. 13, pp 11-16, 2018.
[7]. Anuge Sarine Francis, Bhavana Ffion, Faseen.K and Hema Mohan “Solar Based Induction Cook Top” 3rd International Conference on Electronics, Biomedical Engineering and its Applications (ICEBEA`2013) April 29-30, 2013 Singapore.
[8]. Mehmet Emin Tulu, Deniz Yildirim “Induction Cooker Design with Quasi Resonant Topology using Jitter Drive Method” 978-1-4673-3059-6/13/$31.00 ©2013 IEEE.
[9]. UR Prasanna and L Umanand. Modeling and design of a solar thermal system for hybrid cooking application. Applied Energy, 88(5):1740–1755, 2011.
[10]. Jagan Nath Shrestha. Experiments on induction cooking using trojan battery and inverter. 2015.

D.G. Anand, Lakshmi Niharika K., Shrikala3, Sunanda L., Varsha M., "The Development of Innovative Hybrid Stove with Solar and Biogas for Rural Area," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.25-28, 2021.

Gas stoves are one of the most important assets in the kitchen. All the cooking activities are carried out with the help of this apparatus. Through our project, we are trying to bring into focus those areas which can cause danger in the kitchen. Through our project, we are taking some important factors into consideration that will help the user to avoid those harmful situations. We have observed that most of the accidents in the kitchen take place due to the less careful behaviour of the user. Our project focuses on the idea to provide safety to the user hence avoiding accidents due to flame, gas leakage, or spillage of the flame. Project is the real-time application of the embedded system to turn off the gas stove knob in case of an emergency situation using a reverse gear mechanism. There are three cases in which the automatic turning off the gas stove will take place. 1) In the absence of a user for a set amount of time 2) When smoke or gas leakage is detected. 3) When flame blows off accidently.

Safety, Automation, Motion sensor, Smoke detection, Flame detection

[1] IEEE paper of ‘DESIGN OF A SIMPLE GAS KNOB’: an application . Implementation of Automatic Safety Gas stove By Ajinkya Yalmar and Mahesh Parihar December 2015 Conference: 2015 Annual IEEE India Conference (INDICON).
[2]. IEEE paper of ‘AUTOMATIC GAS STOVE WITH ADVANCED SAFETY FEATURES’ Manu Mathew, Neelkantha V. L. International Journal of Recent Contributions from Engineering, Science & IT (ijes). Vol 3 ,No.2, 2015.
[3] IOT based Smart Kitchen: Computer Science and Engineering SNS College of Technology, Coimbatore tamilnadu - India. International Journal of Computer Science Trends and Technology (IJCST) – Volume 7 Issue 2, Mar - Apr 2019
[4]. Automation and Monitoring Smart Kitchen Based on Internet of Things (IOT) F Nugroho and A B Pantjawat . International Journal of Computer Science Trends and Technology (IJCST) – Volume 7 Issue 2, Mar - Apr 2019
[5] .Smart gas cylinder using the embedded system, international journal of innovative research in electrical, electronics, instrumentation and control engineering Vol.2, Issue 2, Feb 2014.
[6]. Design, Characterisation, and management of a Wireless Network for Smart Gas Monitoring by Vana Jelicic, Micheal Magno, Giacomo Paci, Devid Brunelli, and Lucs Benini. IEEE Sensors journal 13 (1), 328-338, 2011.
[7].A study of fire safety and security at kitchen in apartment buildings by Ali Akbar Razon , Ishtiaque Ahmad (Department of Architecture, Premier University, Bangladesh), (Department of Architecture, Daffodil International University, Bangladesh). International Journal of Latest Engineering and Management Research(IJLEMR).Volume 02-Issue 03,.PP.62-71, March 2017
[8]. Kranz, M., Holleis, P., & Schmidt, A.. Embedded interaction: Interacting with the internet of things. IEEE internet computing, 14(2), 46-53. 2010.
[9].Apeh, S. T., Erameh, K. B., & Iruansi, U. Design and Development of Kitchen Gas Leakage Detection and Automatic Gas Shut off System. Journal of Emerging Trends in Engineering and Applied Sciences (JETEAS), 5(3), 222-228. 2014.
[10]. Hong, S., Kim, D., Ha, M., Bae, S., Park, S. J., Jung, W., & Kim, J. E. (). SNAIL: an IP-based wireless sensor network approach to the internet of things. IEEE Wireless Communications, 17(6), 321- 331. 2010.
[11].Wireless sensor network on lpg gas leak detection and automatic gas regulator system using Arduino.by l.devi and y.somantri. Published under licence by IOP Publishing Ltd
IOP Conference Series: Materials Science and Engineering, Volume 384, conference 1Citation L Dewi and Y Somantri 2018 IOP Conf. Ser.: Mater. Sci. Eng. 384 012064

Nehal Gholse, Sajal Khetan, Pranav Kanhegaonkar, V.K.Bairagi, "Safety in Kitchen Using IOT," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.29-32, 2021.

Testing is one of the most important phases in Software Development Life Cycle (SDLC). To test any CRM application such as Salesforce and Siebel, Test engineer pretends as an end user of the system and checks whether the application is working as expected. Test engineer may need to test hundreds of test data combinations to ensure the correctness of the software. The same functionality might be needed to test again with the same test dataset as the changes are made to the software. This paper discusses how we can use Salesforce APIs and Siebel web services to reduce the time required for test data set up and retesting. In this paper firstly the importance of Salesforce and Siebel web services is explained. Then the detailed explanation of the tool is given under the Methodology section and finally experimental results are discussed.

Salesforce; Siebel; Software Testing; Test Automation

[1] Arun Kumar Arumugam, “Software Testing Techniques and New Trends”, International Journal of Engineering Research & Technology (IJERT), Volume 8, Issue 12, December 2019
[2] Rakesh Kumar, Yogeshwari Sharma, Sonu Agarwal, Pragya, Bhanu Bhushan Parashar, “Extremely effective CRM Solution Using Salesforce”, Journal of Emerging Technologies and Innovative Research (JETIR), Volume 1 Issue 5, October 2014
[3] Hitesh Tahabildar, Bichitra Kalita, “Automated Software Test Data Generation”, International Journal of Computer Science & Engineering Survey (IJCSES), Vol.2, No.1, Feb 2011
[4] Ms. Karuturi Sneha, Mr. Malle Gowda M, “Research on Software Testing Techniques and Software Automation Testing Tools”, International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017), 12 August 2017
[5] Tarik Sheth, Dr. Santosh Kumar Singh, “Software Test Automation- Approach on evaluating test automation tool”, International Journal of Scientific and Research Publications, Volume 5, Issue 8, August 2015
[6] Prof Vina M Lomte, Rishikesh Chandra, Ayush Gondhali, “Data Driven Automation Testing Framework”, International Journal of Emerging Engineering Research and Technology, Volume 2, Issue 7, October 2014
[7] Sonu Lamba, Vinay Rishiwal, Arjun Rana, “An Automated Data Driven Continuous Testing Framework”, International Journal of Advanced Technology In Engineering And Science, Volume No 03, Special Issue No. 01, February 2015
[8] Khalid Eldrandaly, Mahmoud Abd ElLatif, Nora Zaki, “Comparative Study of Software Test Automation Frameworks”, International Journal of Engineering Trends and Technology (IJETT), Volume 67, Issue 11, Nov 2019
[9] Shruti Malve and Pradeep Sharma, “Investigation of Manual and Automation Testing using Assorted Approaches”, International Journal of Scientific Research in Computer Science and Engineering, Volume 5, Issue 2, 2017
[10] Nirali Honest, “Role of Testing in Software Development Life Cycle”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.886-889, 2019
[11] R. Sharma, R. Dadhich, “Implications of Software Testing Strategies at Initial Level of CMMI: An Analysis,” International Journal of Computer Sciences and Engineering, Vol.6, Issue.5, pp.1055-1061, 2018.

Rahul Pandurang Pokale, Nagaraja G.S., "Automation Tool For Customer Relationship Management Applications," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.33-36, 2021.

This paper focuses on developing a security model for cloud computing to enhance the security for data stored in the database. Therefore this paper is providing a solution by designing a model for securing and protecting data in the cloud computing platform using National Identity Number (NIN), One Time Password (OTP) and Advanced Encryption Standard (AES). The design are simulated using a web-system developed with PHP, MySQL and JavaScript. The System Design followed the OOADM methodology for computerization of the system Modules giving room for coupling, decoupling, modification, encapsulation and reuse, as well as easy maintainability. Unified Modeling Language (UML) was extensively used to simplify the explanation of the system Modules. The software performance was tested using speed of data retrieval and security of the data protection. The security looks at the ability of the system to determine fraudulent users and deny them access to the system. The result obtained from the new system developed shows a high level of data security level as compare to existing system that uses only password for authentication.

OTP, AES, NIN, UML, NETWORK

[1] Arnon, R., Peter, M., Maya, H. L., Jean, S., David, K., & Patti, R. (2020). Methodological review: Cloud computing: A new business paradigm for biomedical information sharing. J. of Biomedical Informatics, 43(2):342–353, April 2020. 8, 9, 12
[2] Agbasonu, V.C. (2017). Cloud computing security for data at rest and data on transit. International Journal of Scientific Research and Innovative Technology ISSN: 2313-3759 Vol. 4 No. 2; February 2017.
[3] Dan, L. & Anna, S. (2015). Data protection models for service provisioning in the cloud. In Proceedings of the 15th ACM symposium on Access control models and technologies, SACMAT’10, pages183–192, New York, NY, USA, 2015.ACM. 7, 12.
[4] Hamdi, M. (2017). Security of cloud computing, storage, and networking. In Collaboration Technologies and Systems (CTS), 2017. International Conference on, pages 1–5, May. 8
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Agbakwuru, A. Onyekachi., Njoku, D. Okechukwu, Amanze, B. Chibuike, "Development of Security Model for Protecting Data in the Cloud Using 3-TIER Authentication," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.37-44, 2021.

Content Delivery Network, or a CDN, is a globally distributed network of servers that helps provide good availability, faster and reliable performance, and security to the content distributors. In order to maintain a secure and reliable system any change proposed for the network needs to be thoroughly tested. But testing a full software release takes a lot of time because of a very huge database and complex dependencies between them. This increases the total time taken in the software development life cycle. In the existing system, a tester has to write test scripts for every Change Request (CR), which is a documented request to modify the current software system. This effort can be substantially reduced by developing a tool which can accurately test all the changes by dynamically generating test values for each metadata tag. (In this paper, the term “metadata tag” refers to settings used to control the configuration of web servers). This reduces the time to figure out all complex dependencies and test for each and every change made. The aim is to provide a simple, clean interface which allows the user to select a Change Request he wants to test and then dynamically generate positive and negative test values on which test will run on and provide a detailed result to the user whether the test passed or not.

Content Delivery Network; Metadata Tag; Change Request

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Jayesh Kumar Yadav, Nagaraja G.S., "Test Automation Framework for Content Delivery Network," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.45-48, 2021.

COVID-19 is a viral infection brought about by Coronavirus 2 (SARS-CoV-2). The spread of COVID-19 appears to have a hindering impact on the worldwide Economy and wellbeing. A positive chest X-beam of contaminated patients is a urgent advance in the fight against COVID-19. This has prompted the presentation of an assortment of profound learning frameworks and studies have shown that the exactness of COVID-19 patient recognition using chest X-beams is unequivocally idealistic. Profound learning organizations like convolutional neural organizations (CNNs) need a significant measure of preparing information. In this task, we present a technique to create engineered chest X-beam (CXR) pictures by fostering an Auxiliary Classifier Generative Adversarial Network (ACGAN) based Model called Covid GAN. Also, the proposed framework shows that the engineered pictures created from Covid GAN can be used to improve the exhibition of CNN based design called Resnet.

COVID-19, ACGAN, CXR.

[1] Shervin Minaee, Rahele Kafieh, Milan Sonka, Shakib Yazdani, and Ghazaleh Jamalipour Soufi,” Deep-covid: Predicting covid-19 from chest x-ray images using deep transfer learning”, Medical image analysis, 65:101794, 2020.
[2] Parnian Afshar, Shahin Heidarian, Farnoosh Naderkhani, Anastasia Oikonomou, Konstantinos N Plataniotis, and Arash Mohammadi, “Covidcaps: A capsule network-based framework for identification of covid-19 cases from x-ray images”, Pattern Recognition Letters, 138:638–643, 2020.
[3] Linda Wang, Zhong Qiu Lin, and Alexander Wong,” Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images,” Scientific Reports, 10(1):1–12, 2020.
[4] Eduardo Luz, Pedro Lopes Silva, Rodrigo Silva, Ludmila Silva, Gladston Moreira, and David Menotti,” Towards an effective and efficient deep learning model for covid-19 patterns detection in x-ray images”, arXiv preprint arXiv:2004.05717, 2020.
[5] Enzo Tartaglione, Carlo Alberto Barbano, Claudio Berzovini, Marco Calandri, and Marco Grangetto,” Unveiling covid-19 from chest x-ray with deep learning: a hurdles race with small data”, International Journal of Environmental Research and Public Health, 17(18):6933, 2020.

Arun Raj S., Anand. S. B.,Fathima B., Ponnu Raj R., "Covid-19 Detection from Chest X-Ray using ACGAN and RESNET," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.49-53, 2021.