Volume-10 , Issue-3 , Mar - 2022 Go Back

Volcano seismic events are a source of great hazards implicating human lives and material damage. Consequently, continuous monitoring of this natural phenomenon is of great importance to reduce their dramatic effects on people and nearby economy. A seismic network is usually deployed around the crater to achieve this monitoring task. The different produced volcano seismic events (e.g., long period LP, tremor TR, volcano tectonic VT) are related to physical phenomenon (explosion, eruption, depressurization …etc) occurring at the source. The seismic network may also record seismic events that are not related to volcanoes such as tectonic events (TC) produced by geological faults. The first vital task in volcano monitoring is to recognize the source of each detected event. This task should be performed automatically due to the large amount of data recorded daily. In this work, we propose an easy and straightforward method to classify volcano seismic events using the cross-correlation function in time domain. We applied this method using three approaches. The application of these approaches to the seismic database of the Llaima volcano (Chile) gives good results, particularly the third approach that achieves a global accuracy of 92.7%.

Volcano seismic events, Classification, Cross-correlation, Time domain.

[1] S. Scarpetta, F. Giudicepietro, E. Ezin, S. Petrosino, E. Del Pezzo, M. Martini, M. Marinaro, “Automatic classification of seismic signals at Mt. Vesuvius volcano, Italy, using neural networks”, Bulletin of the Seismological Society of America, Vol. 95, No. 1, pp. 185–196, 2005.
[2] H. Langer, S. Falsaperla, T. Powell, G. Thompson, “Automatic classification and a posteriori analysis of seismic event identification at Soufrière hills volcano, Montserrat”, Journal of Volcanology and Geothermal Research 153 (1–2 SPEC. ISS.), 1–10, 2006.
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[4] M. Titos, A. Bueno, L. Garcia, & C. Benitez, “A Deep Neural Networks Approach to Automatic Recognition Systems for Volcano-Seismic Events”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11(5), 1533-1544, 2018.
[5] M. Malfante, M. D. Mura, J.-P. Métaxian, J. Mars, O. Macedo, A. Inza. “Machine Learning for Volcano-Seismic Signals: Challenges and Perspectives”, IEEE Signal Processing Magazine, Institute of Electrical and Electronics Engineers, 35 (2), pp.20 – 30, 2018.
[6] J.P. Canário , R.F. de Mello , M. Curilem , F. Huenupan , R.A. Rios, “In-Depth Comparison of Deep Artificial Neural Network Architectures on Seismic Events Classification”, Journal of Volcanology and Geothermal Research, volume 30, 105627, 2020.
[7] M. Titos, A. Bueno, L. Garcia, M. C. Benitez, & J. Ibanez, “Detection and Classification of Continuous Volcano-Seismic Signals With Recurrent Neural Networks” IEEE Transactions on Geoscience and Remote Sensing, volume 57, Issue 4, pp.1936–1948, 2018.
[8] M. Titos, A. Bueno, L. Garcia, C. Benitez, & J. C. Segura, “Classification of Isolated Volcano-Seismic Events Based on Inductive Transfer Learning” IEEE Geoscience and Remote Sensing Letters, vol. 17, Issue. 5, pp.869–873, 2019.
[9] A. Salazar, R. Arroyo, N. Perez, & D. Benitez, “Deep-Learning for Volcanic Seismic Events Classification”, In the Proceedings of the 2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020), 2020.
[10] M. Lopez-Perez, L. Garcia, C. Benitez, & R. Molina, “A Contribution to Deep Learning Approaches for Automatic Classification of Volcano-Seismic Events: Deep Gaussian Processes”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 59, Issue. 5, pp. 3875 - 3890, 2020.
[11] P. Venegas, N. Perez, D. S. Benitez, R. Lara-Cueva, & M. Ruiz, “Building Machine Learning Models for Long-Period and Volcano-Tectonic Event Classification”, In the Proceedings of the 2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON 2019), Chile, pp. 1-6, 2019.
[12] R. Carnel, “Characterization of volcanic regimes and identification of significant transitions using geophysical data: a review”, Bulletin of Volcanology, 76(8), pp. 1-22, 2014.
[13] K. Mohabkumar, K. Sangeetha, “A Study on Earthquake Pediction Using Neural Network Algorithms”, International Journal of Computer Sciences and Engineering. Vol. 6, Issue.10, 2018.
[14] P. Venegas, N. Perez, D. Benitez, R. Lara-Cueva, & M. Ruiz, “Combining Filter-Based Feature Selection Methods and Gaussian Mixture Model for the Classification of Seismic Events From Cotopaxi Volcano”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 12, Issue. 6, pp. 1991 - 2003, 2019.
[15] R. Soto, F. Huenupan, P. Meza, M. Curilem, & L. Franco, “Spectro-temporal features applied to the automatic classification of volcanic seismic events”, Journal of Volcanology and Geothermal Research, 358, pp.194–206, 2018.
[16] N. Perez, P. Venegas, D. Benitez, F. Grijalva, R. Lara, & M. Ruiz, “Benchmarking Seismic-Based Feature Groups to Classify the Cotopaxi Volcanic Activity”, IEEE Geoscience and Remote Sensing Letters, Vol. 19, pp.1–5, 2020.
[17] T. Permana, T. Nishimura, H. Nakahara and N. Shapiro, “Classification of volcanic tremors and earthquakes based on seismic correlation: application at Sakurajima volcano, Japan” Geophysical Journal International, 229, pp. 1077–1097, 2022.
[18] M. Curilem, R.F. de Mello, F. Huenupan, C. San Martin, L. Franco, E. Hernández, R.A. Rios. “Discriminating seismic events of the Llaima volcano (Chile) based on spectrogram cross-correlations”, Journal of Volcanology and Geothermal Research 367, pp.63–78, 2018.
[19] E. H. Ait Laasri, E. Akhouayri, D. Agliz, A. Atmani, “Automatic detection and picking of P-wave arrival in locally stationary noise using cross-correlation”, Digital Signal Processing, Vol. 26, pp. 87- 100, 2014.
[20] F. Wagner, A. Tryggvason, R. Roberts and O. Gudmundsson. “Processing automatic seismic event detections: an iterative sorting algorithm improving earthquake hypocentres using interevent cross-correlation”, Geophysical Journal International. 219, pp. 1268–1280, 2019.
[21] R.O. Salvage, J.W. Neuberg. “Using a cross correlation technique to refine the accuracy of the Failure Forecast Method: Application to Soufrière Hills volcano, Montserrat”, Journal of Volcanology and Geothermal Research 324, pp.118–133, 2016.
[22] D. P. Schaff, W.-Y. Kim, P. G. Richards, E. Jo, and Y. Ryoo, “Using Waveform Cross Correlation for Detection, Location, and Identification of Aftershocks of the 2017 Nuclear Explosion at the North Korea Test Site”, Seismological Research Letters, Vol. 89, Number 6 pp. 2113-2119, 2018.
[23] F. R. Suratwala, S. G. Kejgir, “A Robust Multi-Channel Digital Image Watermarking Technique with SVD, DWT, DCT”, International Journal of Computer Sciences and Engineering. Vol. 7, Issue.5, 2019.
[24] J. P. Canario, R. Mello, M. Curilem, F. Huenupan , and R. Rios, “Llaima Volcano Dataset: In-Depth Comparison of Deep Artificial Neural Network Architectures on Seismic Events Classification”, Data in Brief, 30, 105627, 2020.
[25] 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, 2021.

A. Atmani, E.H. Ait Laasri, D. Agliz, E. Akhouayri, "Llaima’s Volcano Seismic Event Classification Using The Cross-Correlation Function," International Journal of Computer Sciences and Engineering, Vol.10, Issue.3, pp.1-7, 2022.

This project aims in developing a conversational chatbot for college universities, which can converse in any desired language. The main purpose of the planned system is to develop a chatbot which can intelligently answer all the queries related to universities. For this purpose, machine learning algorithms are used. Primarily, NLP (Natural Language Processing) is used for the interaction between the chatbot and the user. To answer the users query, many different machine learning algorithms can be used. For this purpose, we move to cosine similarity. It, in a way, helps the chatbot to search for the answer to the user`s query. Also, various libraries are used for different purposes like accepting the audio input, converting speech to text, translating to desired language, etc. The pandemic has affected a lot of fields around the world. One of them is the educational field. Many students in the urban as well as the rural parts of India were not able to visit the universities to get information about academics. This chatbot would eliminate these worries. All the information needed can be accessed online.

Chatbot, Machine learning, Naive bayes classifier, NLP, Cosine similarity

[1] Prof. Darshan A. Patel, Neelkumar P. Patel1, “AI and Web-Based Human-Like Interactive University Chatbot (UNIBOT)”, IEEE Xplore, pp. 309-315, 2020.
[2] A K M Shahariar Azad Rabby, Md. Majedul Islam, “Language Detection using Convolutional Neural Network”, IEEE Xplore, pp. 1-5,2020.
[3] A. Ansari, M. Maknojia and A. Shaikh, “Intelligent question answering system based on ArtificialNeural Network,” IEEE International Conference on Engineering and Technology (ICETECH), pp. 1793-1805, 2017.
[4] B. R. Ranoliya, N. Raghuwanshi and S. Singh,” Chatbot for university related FAQs,” 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1-6, 2018.

T. Bhaskar, Y.A. Dive, A.J. Gujarathi, S.A. Gangurde, N.D. Rajput, "Modelling a Conversational AI Chatbot for Academic Websites," International Journal of Computer Sciences and Engineering, Vol.10, Issue.3, pp.8-12, 2022.

Bangladesh locates in disaster prone area in South Asia because of its geographical location. Various types of disasters like flood, cyclone, river-bank erosions, drought, salinity etc. are the common phenomenon in this land. Such natural disaster left huge people died or wounded and destroying valuable wealth’s around the year. In the recent time, natural calamities have increased remarkably. This study follows qualitative research methodology and reveals that the people living in coastal areas are facing severe natural hazard that badly impact on their lifestyle. The study found that the government has been conducting various risk reduction programmes like prewarning signals, disbursing posters and leaflets, conducing knowledge sharing meeting, awareness rising training to mitigate the losses. Besides, some nongovernment organizations also have been playing vital role to face this calamity. This study also has drawn a conclusion with recommendations that will leave the scope to take effective action in this circumstance.

Disaster, Flood, Risk Reduction, Bangladesh

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Md. Alaul Kabir, "The Role of Government in Disaster Risk Reduction (DRR) in the Coastal Areas of Bangladesh," International Journal of Computer Sciences and Engineering, Vol.10, Issue.3, pp.13-18, 2022.

In this growing world of science and technology, the pollution is still a major problem across the globe, and construction industries play a vital role in air pollution and soil pollution. The use of marble stone in houses is on huge demand and because of this stone cutting and dressing, factories produces the dust in large quantity, which results in accordance of many problems when left freely in environment. So, keeping these problems in view, the reuse or recycle of this dust can help us in many ways. Hence, we used the marble dust as a cementitious material, by partially replacing cement in concrete, can help in reuse by large quantity. Also, the use of fly ash with cement in concrete was taken. The testing cubes of grade M10 were tested on accountability of marble dust and fly ash. The compressive strength of cubes was tested at duration of 7th days. The positive and negative behavior of concrete with duration was noticed. The production of cubes is economic and environmental.

Compressive Strength, Partial Replacement, Marble Dust and Materials.

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[3]. Ostrowski. “The utilization of waste marble dust as a replacement in air-cured mortar”, 12 April 2019.
[4]. Tarun Chauhan, Iliyas Khaleel “study and investigation on utilization of fly ash, marble dust and stone dust in opc concrete”, ISSN: 2455-2631, vol.6, issue7, July 2021.
[5]. Mr. Ranjan Kumar, Shyam Kishor Kumar,”Partial replacement of cement with marble dust powder”, ISSN: 2248-9622, vol.5, issue 8, August 2015.
[6]. Rohan K, Roshan Rai, N Bhavani Shankar, Akshay NK. “Influence of marble dust as partial replacement of cement in normal curing concrete”, ISSN: 2349-5162, vol.8, issue 9, ESTD Year 2014, September 2021.
[7]. Nitisha Sharma, Ravi kumar. “Use of waste marble powder as partial replacement in cement sand mix”, ISSN: 2278-0181, vol.4, issue 05, May 2015.
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Naveen Thakur, Rahul Mahla, Divyam Maharshi, Rahul Karwasra, "Effect on Compressive Strength of Concrete on Partial Replacement of Cement by Marble Dust and Fly Ash," International Journal of Computer Sciences and Engineering, Vol.10, Issue.3, pp.19-21, 2022.

Electronic patient record (EPR) storage and transmission is an integral part of modern healthcare system. Steganography comes into play when the medical image becomes a potential storage of patient information. Generally steganographic techniques give higher importance to the perfect recovery of secret data than the actual reconstruction of the original image. But in medical system the actual reconstruction of the original medical image is importance as this is the primary source of disease understanding. Hence in this context reversible steganography plays an important role in telemedicine field in storing, maintenance and communication of electronic patient record within the image itself, so that where a doctor located at a geographically remote location can diagnose a patient on the basis of a medical image generated elsewhere with confidence. In this paper we have studied some elegant techniques for reversible medical image steganographic techniques and compare their performances.

Reversible, Steganography, medical image, EPR, DICOM

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[5] S. A. Parah, F. Ahad, J. A. Sheikh,G. M. Bhat, "Hiding Clinical Information in Medical Images: A New High Capacity and Reversible Data Hiding Technique," Journal of Biomedical Informatics , Vol. 66 , pp.214-230, 2017.
[6] S. K. Patel, C. Saravanan, V. K. Patel., "Cloud-Based Reversible Dynamic Secure Steganography Model for Embedding Pathological Report in Medical Images," International Journal of Computers and Applications Vol.43, Issue.10, pp.1002-1010, 2021.
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[13] K.Kim, M.Le, H.Y.Lee,H.K..Lee, "Reversible Data Hiding Exploiting Spatial Correlation between Sub-Sampled Images", Pattern Recognition Vol.42,Issue.11, pp. 3083-3096, 2009.
[14] H.Y.Lee, "Reversible Data Hiding for Medical Imagery Applications to Protect Privacy”, International Journal of Engineering Research and Technology Vol.12, Issue.1, pp. 42-49, 2019.
[15] W.L.Tai, C.M.Yeh ,C.C. Chang, "Reversible Data Hiding Based on Histogram Modification of Pixel Differences," IEEE Transactions on Circuits and Systems for Video Technology Vol.19, Issue.6, pp. 906-910,2009.

Bibek Ranjan Ghosh, Siddhartha Banerjee, Subham Kr Singh, Shreyas Gangopadhyay, "A Survey on Reversible Image Steganography in Telemedicine," International Journal of Computer Sciences and Engineering, Vol.10, Issue.3, pp.22-26, 2022.

Women play a significant role in uplifting the economy of every other country in the world today. We can take the example of Japan where women used to have very little share in the economy as they were mostly housewives but slowly and Steadily they started to progress when the government increased their involvement via employment schemes. As the name suggests Herrevolution is a website meant to empower women who are freelancers Mainly housewives to sell their products and reach out to the world with their inspiring Stories. It also provides a platform for an organization that deals with a technical aspect which can benefit from the business. Web Technology which is the foundation of our project is a rapidly advancing industry where new technologies are arriving day by day like Reacts and Angular as frontend technologies which are dynamic in nature and backend technologies like Nodejs and MongoDB. This paper contains details about how our website works and various aspects related to it and what impact it will have [1].

Women, Web Development, Herrevolution, website, business

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Chandra Prakash Patidar, "Herrevolution: Empowering Women Freelancers," International Journal of Computer Sciences and Engineering, Vol.10, Issue.3, pp.27-32, 2022.