Volume-07 , Special Issue-14 , May - 2019 Go Back

By knowing the difficulties that present in the process of decision system that the present agriculture is not able to solve this problems in the agriculture production in this environment, so the technologies that the agent will do which is used in the field of agriculture is presented in this paper. The idea that how this intelligent decision system in the agriculture field also displayed ,The design of this idea has also been constructed. So this system platform is developed by using java agent development framework to make the communication easy among agents with java language and also secure shell technology has been used for secured services SSH which is finally result to share the information of agriculture .The advantages of this process is to operate the crop cultivation, and also be the main role in environment protection and also used to change the economic condition to small scale.

Agriculture; Intelligent decision system

[1] Ritaban Dutta, Ahsan Morshed, Jagannath Aryal, Claire D`Este, and Aruneema Das. 2014. Development of an intelligent environmental knowledge system for sustainable agricultural decision support. Environ. Model. Softw. 52, C (February 2014), 264-272. DOI=http://dx.doi.org/10.1016/j.envsoft.2013.10.004
[2] Ştefan Conźiu and Adrian Groza. 2016. Improving remote sensing crop classification by argumentation-based conflict resolution in ensemble learning. Expert Syst. Appl. 64, C (December 2016), 269-286. DOI: https://doi.org/10.1016/j.eswa.2016.07.037
[3]Thorsten R. Arnold. 2013. Procedural knowledge for integrated modelling. Environ. Model. Softw. 39, C (January 2013), 135-148. DOI=http://dx.doi.org/10.1016/j.envsoft.2012.04.015
[4] 2017. An overview of the model integration process. Environ. Model. Softw. 87, C (January 2017)49-63DOI:
https://doi.org/10.1016/j.envsoft.2016.10.013
[5] Arash Bahrammirzaee. 2010. A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems. Neural Comput. Appl. 19, 8 (November 2010), 1165-1195. DOI=http://dx.doi.org/10.1007/s00521-010-0362-z
[6] Meriam Bayoudh, Emmanuel Roux, Gilles Richard, and Richard Nock. 2015. Structural knowledge learning from maps for supervised land cover/use classification. Comput. Geosci. 76, C (March 2015), 31-40. DOI=http://dx.doi.org/10.1016/j.cageo.2014.08.013
[7] 2007. Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02. IEEE Computer Society, Washington, DC, USA.
[8] R.C. Gilbert, T.B. Trafalis, M.B. Richman, L.M. Leslie, Machine learning methods for data assimilationDOI:10.1115/1.859599.paper14
[9] Amit Sheth. 2012. A new landscape for distributed and parallel data management. Distrib. Parallel Databases 30, 2 (April 2012), 101-103. DOI=http://dx.doi.org/10.1007/s10619-012-7091-5
[10] Dean P. Holzworth, Val Snow, Sander Janssen, Ioannis N. Athanasiadis, Marcello Donatelli, Gerrit Hoogenboom, Jeffrey W. White, and Peter Thorburn. 2015. Agricultural production systems modelling and software. Environ. Model. Softw. 72, C (October 2015), 276-286. DOI: https://doi.org/10.1016/j.envsoft.2014.12.013

Saiteja, A. Prasanth, Irshad Khan, Adarsha Bikram, Ambika B J, "Study on Intelligent Decision-Making Platform in the Agricultural Production", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.288-291, 2019.

Traffic jam is vehicles move in slower motion because there are more vehicles than the road capacity. This makes Traveling time longer and increases queuing and waiting time in traffic. Our proposed System control the traffic jam by survey the traffic density, efficiency and provide emergency services In this proposed project. A method is implemented for detecting the traffic congestion with the help of a monitoring system by using sensors providing emergency services, if any emergency situations like an accident, vehicle Malfunctioning happens. [1]The first module involves in the smart signal pole which scans the volume of the traffic, then as per the traffic volume signal timing will be increased or decreased accordingly. [2] The second module involves when traffic volume is increased to high an emergency alert message is triggered in a display, by seeing this alert message traveller can choose the alternate way for his destination. [3] Third module emergency condition like accidents panic button is provided by triggering this button manually an emergency message will be sent to Ambulance, Traffic Police, this helps to clear the traffic as early as possible.

Smart signal pole, traffic congestion / traffic volume, panic button, emergency message

[1]. M Jagadeesh, G. Merlin Suba, S Karthik and K Yokesh, “Smart Autonomous Traffic Light Switching by Traffic Density. Measurement through Sensors”, International Conference on Computers, Communications, and Systems, 2015.
[2]. Sk Riyazhussain, Riyazhussain, C.R.S. Lokesh, P.Vamsikrishna, Goli Rohan, “Raspberry Pi Controlled Traffic Density Monitoring System”, IEEE WiSPNET 2016 conference, 2016.
[3]. Abdullahi Chowdhury, “Priority Based and Secured Traffic Management System for Emergency Vehicle using IoT”.
[4]. Fevzi Yasin Kababulut, Damla Kuntalp, Timur Duzenli, “New Methods of Density Estimation for Vehicle Traffic.

Pavan Kumar B N , Raghavendra J, Rakesh M , Laxmi B Rananavare, "Automated Traffic Density Control With Emergency Service System", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.292-295, 2019.

We plan a self-organization mentioning centre including its item and hardware. It demonstrates the taste and expenses of the sustenance for customers to incorporate their solicitations. The traditional sustenance undertaking the official`s mode, remote self-organization mentioning the board information structure comprehends the intellectualized and informationalizated diner the board. The structure thus completes data tolerating, limit, show and examination. The structure (Eatery Management System) outfits with various ideal conditions as uncommon versatility, minimization, etc, and has a for the most part spread of use prospects.Hence,we propose to construct a product venture that can proficiently deal with and oversee different exercises of an eatery and every one of these exercises will occur under the supervision of the manager. The organizations in eateries are presently developing continually. In the meantime, the requirement for dealing with its activities and undertakings emerges. The most ideal approach to advance these exercises is developing the business online too. The present age energizes cutting edge benefits particularly over the Internet. Consequently, the task is grown capably to help eatery proprietors mechanize their business activities. This task serves the most ideal method for keeping up client`s data and provides food their necessities. The best advantage of maintaining a database for eatery is the any details regarding the eatery like branch details, or food menu or number of customers visiting the eatery and their review and also the details regarding the staff like the staff designation etc can be retrieved or searched very easily instead of manually checking them since retrieving takes very less time and is easy while manually doing the same takes more time and is also difficult as well.

Eatery Management, Menu, table booking

[1] Noor Azah Samsudin, Shamsul Kamal Ahmad Khalid, Mohd Fikry Akmal Mohd Kohar, Zulkifli Senin, MohdNor Ihkasan; “A Customizable Wireless Food Ordering System with Real-Time Customer Feedback.”; 2011 IEEE Symposium on Wireless Technology and Applications (ISWTA), September 25-28,2011, Langkawi, Malaysia.
[2] Aman Jain, Snehal Chauha, Anish Hirlekar, Suraj Sarange, Automated Restaurant Management System, INTERNATIONALJOURNALOFINNOVATIVERESEARCHINELECTRICAL, ELECTRONICS, instrumentation and controlengineeringvol.4, Issue 5, May 2016.
[3] Dr. Vinayak Ashok Bharadi, e-Restaurant: Online Restaurant Management System for Android, IJACSA Special Issue on Selected Papers from International Conference & Workshop on Advance Computing 2013.

Lakshmi S, Sharath Simha, Archana B H, Kishen Achar B R ,Chaithra M H, "Eatery Management System", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.296-302, 2019.

The Study into cyberbullying identification has expanded as of years, due to some portion to the multiplication of cyberbullying crosswise over internet-based life and its adverse impact on youngsters. An emerging collection of work is rising on mechanized ways to deal with cyberbullying identification. These methodologies use machine learning and natural language processing techniques to distinguish the attributes of a cyber bullying trade and naturally identify cyberbullying by matching textual data to the identified traits.Based on our general literature review, we arrange existing methodologies into 4 primary classes, (a) Supervised learning-based approaches typically use classifiers such as SVM and Naïve Bayes to develop predictive models for cyberbullying detection. (b) Lexicon based systems utilize word lists and use the presence of words within the lists to detect cyberbullying. (c) Rules-based approaches match text to predefined rules to identify bullying and (d) mixed-initiatives approaches combine human-based reasoning with one or more of the aforementioned approaches. We discovered absence of value agent named datasets and non-holistic thought of cyberbullying by study when creating location frameworks are two key challenges facing cyberbullying detection study. This paper basically maps out the best in class in cyberbullying discovery research and fills in as an asset for specialists to figure out where to best direct their future research endeavor’s in this field.

Machine Learning, Natural Language Processing Techniques, SVM, Navie Bayes

[1] Cynthia Van Hee, Gilles Jacobs, Chris Emmery, Bart Desmet, Els Lefever, Ben Verhoeven, Guy De Pauw, Walter Daelemans, Veronique Hoste, “Automatic Detection of Cyberbullying in Social Media Text”, arXiv:1801.05617v1 [cs.CL], 17 jan 2018.
[2] Semiu Salawu, Yulan He, Joanna Lemsden,“Approaches to Automated Detection of Cyberbullying: A Survey”, IEEE Transactions on effective Computing, ISSN: 1949-3045, October, 2017.
[3] Nikita Hatwar, Ashwini Patil, Diksha Gondane, “AI Based Chatbot”, International Journal of Emerging Trends in Engineering and Basic Sciences (IJEEBS), ISSN (Online) 2349-6967, Volume 3, PP.85-87, Issue 2 (March-April 2016).
[4] Noora AI Mutawa, Joanne Bryce, Virgina N.L. Franqueira, Andrew Marrington, “Forensic investigation of cyberstalking cases using Behavioral Evidence Analysis” DFRWS 2016 Europe; Volume 16, Supplement, Pages S96-S103, 29 March 2016.
[5] Zinnar Ghasem, Ingo Frommholz, Carsten Maple (2015),“A Machine Learning Framework to Detect And Document Text-based Cyberstalking”, R. Bergmann, S. Gorg, G. Muller (Eds.): Proceedings of the LWA 2015 Workshops: KDML, FGWM, IR, and FGDB. Trier, Germany, 7.-9. October 2015, published at http://ceur-ws.org
[6] Zinnar Ghasem, Ingo Frommholz, Carsten maple (2015),“Machine Learning Solutions for controlling Cyberbullying and Cyberstalking”, vol : 7-9, 2015.
[7] Rekha Sugandhi, Anurag Pande, Siddhant Chawla, Abhishek Agrawal, Husen Bhagat,“Methods for detection of cyberbullying : A survey”,Intelligent Systems Design and Applications (ISDA), 2015.
[8] Dadvar, M., Ordelman, R., Jong, F. D. (2012). Trieschnigg. D. “Towards User Modelling in the Combat against Cyberbullying, in Natural Language Processing and Information Systems”, Springer-Verlag Berlin Heidelberg, 277–283, 2012.
[9] Dinakar, K,“Modeling the Detection of Textual Cyberbullying”, in The Social Mobile Web, 11–17. 2011.
[10] Ingo Frommholz, Haider M. al-Khateeb, Martin Potthast, Zinnar Ghasem, Mitul Shukla, Emma Short,“Textual Analysis and Machine Learning for Cyberstalking Detection”, 2009.

Ayesha Banu R, Gopal K Shyam, "Approaches to Automated Detection of Cyberbullying: A Survey", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.303-310, 2019.

Diabetic Retinopathy (DR) is one of the main sources of visual impairment and eye malady in working age population of the world. This undertaking is an endeavour towards finding a robotized approach to distinguish this ailment in its initial stage. In this task we are utilizing directed learning strategies to characterize a given arrangement of pictures into 5 classes. For this task we are employing various image processing techniques and filters to enhance many important features. This approach intends towards finding an automated, suitable and sophisticated approach using image processing and pattern recognition so that DR can be detected at early levels easily and damage to retina can be minimized and also to help ophthalmologists to diagnose fast, accurate, and reliable diabetic retinopathy

Diabetic Retinopathy (DR), Supervised learning, Image processing

[1] https://ffb.ca/learn/diabetic-retinopathy/
[2] kaggle Challenge Diabetic Retinopathy Detection. https://www.kaggle. com/c/diabetic-retinopathy-detection
[3] Wong Li Yun , U. Rajendra Acharya, Y.V. Venkatesh, Caroline Chee, Lim Choo Min, E.Y.K. Ng Identi cation of di erent stages of diabetic retinopathy using retinal optical images. July 2007
[4] Jagadis h Naya k, P Subba nna Bhat, Rajen dra Achar ya U,C. M. Lim, Manjunath Kagathi Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images November 2007
[5] Diabetic Retinopathy http://en.wikipedia.org/wiki/Diabetic_ retinopathy
[6] Starter code Startercode,http://bit.ly/1Eefvvd
[7] Equalization http://opencv-python-tutroals.readthedocs.org/en/ latest/py_tutorials/py_imgproc/py_histograms/py_histogram_ equalization/py_histogram_equalization.html
[8] Equalization http://en.wikipedia.org/wiki/Histogram_ equalization
[9] Morphological operations http://opencv-python-tutroals. readthedocs.org/en/latest/py_tutorials/py_imgproc/ py_morphological_ops/py_morphological_ops.html# morphological-ops
[10] https://ieeexplore.ieee.org/document/7434449
[11] Diabetic Retinopathy Analysis. R Sivakumar, B Ravindran, M mutthaya, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1138264
[12] https://www.hindawi.com/journals/joph/2017/8234186/
[13] http://www.rroij.com/open-access/an overview- n -image-processing-techniques.php?aid=47175
[14] https://medium.com/@Adoriasoft/image-recognition-and-image-processing-techniques-fe3d35d58919
[15] Convolution Neural Network. https://skymind.ai/wiki/convolutional-network
[16] https://www.webopedia.com/TERM/G/gray_scaling.html
[17] https://communities.bentley.com/products/microstation/microstation_printing/f/printing-and-plotting-forum/85795/print-select-references-in-gray-scale
[18] https://medium.com/technologymadeeasy/the-best-explanation-of-convolutional-neural-networks-on-the-internet-fbb8b1ad5df8
[19] https://statistics.laerd.com/statistical-guides/understanding-histograms.php
[20] https://datavizcatalogue.com/methods/histogram.html
[21] https://docs.python.org/3/library/
[22] https://www.edureka.co/blog/python-libraries
[23] https://www.kdnuggets.com/2018/06/top-20-python-libraries-data-science-2018.html
[24] https://www.geeksforgeeks.org/best-python-libraries-for-machine-learning/
[25] https://www.infoworld.com/article/3008915/20-practical-python-libraries-for-every-python-programmer.html

Mujeefa. M. Shaikh, Nadhiya S, Nandini Sriram, Nupur Choudhury, Tanuja K, "Detection of Diabetic Retinopathy using Image Processing", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.311-314, 2019.

Wireless sensor network is a cluster of various sensors, which has capability of sensing and communicating the data collected. Effective and efficient data aggregation algorithm in Wireless Sensor Networks (WSNs) can increase the lifecycle of the network by bringing down the communication of unnecessary data and make the security of the networks better. The conventional data aggregation algorithm in WSNs predominantly aims to raise the level of energy utilization, and pay no heed to security and lifecycle. We propose a data aggregation algorithm by constructing a data aggregation tree to deal between energy utilization and lifecycle. The algorithm extends the duration of lifecycle by reducing the maximum energy consumption by the nodes. In scheduled data aggregation, selected communications are accounted to deal between low weighted delay and high network lifecycle. Simulation results shows that the proposed algorithm utilizes less energy for aggregation of the data from the sensor nodes, and hence increases the lifecycle of the network.

WirelessSensor Networks, scheduled data aggregation, tree based data aggregation tree

[1] RRajagopalan, P.K.Varshney."data aggregation techniques in sensor networks: survey", IEEE communications surveys and tutorials,Vol.8 Issue 4
[2] P Patil,U Kulkarni."analysis of data aggregation techniques in wireless sensor networks”,International Journal of Computational Engineering and Management,Vol.16 Issue 1.
[3] Hu Yanhua,Zhang Xincai. "Aggregation tree data aggregation algorithm in wireless sensor network". International journal of online and biomedical engineering,Vol.12,http://dx.doi.org/10.3991/ijoe.v12i06.5408
[4] Goel P K, Sharma V K. "Data Aggregation Security in Wireless Sensor Network". International Journal of Advanced Research in Computer Science, Vol.3,Issue 5
[5] Li H, Lin K, Li K. Energy-efficient and high-accuracy secure data aggregation in wireless sensor networks[J]. Computer Communications, 2011, 34(4): 591-597. http://dx.doi.org/10.1016/j.com com.2010.02.026
[6] Ibrahim Atoui et al. "Tree-based data aggregation approach in wireless sensor network using fitting functions".Sixth International Conference on Digital Information Processing and Communications
[7] Katiyar V, Chand N, Soni S. Clustering algorithms for heterogeneous wireless sensor network: A survey[J]. International Journal of Advanced Networking and Applications, 2011, 2(4): 745-754.
[8] Boyinbode O, Le H, Takizawa M. A survey on clustering algorithms for wireless sensor networks[J]. International Journal of Space-Based and Situated Computing, 2011, 1(2-3): 130-136. http://dx.doi.org/10.1504/IJSSC.2011.040339
[9] Przydatek B, Song D, Perrig A. SIA: Secure information aggregation in sensor networks[C]//Proceedings of the 1st international conference on Embedded networked sensor systems. ACM, 2003: 255-265.
[10] Mahimkar A, Rappaport T S. SecureDAV: A secure data aggregation and verification protocol for sensor networks[C]//Global Telecommunications Conference, 2004. GLOBECOM`04. IEEE. IEEE, 2004, 4: 2175-2179.
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[12] Sugandhi N, Manivannan D. Analysis of Various Deterioration Factors of Data Aggregation in Wireless Sensor Networks[J]. IJET, Vol.5, No1, ISSN, 2013: 0974-4024.
Sun Y, Luo H, Das S K. A trust-based framework for fault tolerant data aggregation in wireless multimedia sensor networks[J]. Dependable and Secure Computing, IEEE Transactions on, 2012, 9(6): 785-797. http://dx.doi.org/10.1109/TDSC.2012.68
[13] Wu J Q, Guo J H. Improved pattern-based encrypted data aggregation scheme for clustered wireless sensor networks[C]//Design, Manufacturing and Mechatronics: Proceedings of the 2015 International Conference on Design, Manufacturing and Mechatronics (ICDMM2015). World Scientific, 2015: 475-481.

Aishwarya R, Akarsha. D.P, Anupama. D.M, Anusha P, Vani K, "Tree based Data aggregation algorithm in wireless sensor networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.315-318, 2019.

The Android-supported IoT(Internet of Things) stage simply same the recent Android application provides an condition that makes it easy to use Google`s uses framework administrations includes advancement method and APIs used through which it controls and support the different sensors of a IoT gadgets. Applications used on the Android-supported IoT are frequently User Interface are available free and are used without knowing the client`s agree to enlisted authorizations. It is difficult to find the solution on the misuse of consents just as to check them whether they are registered while upgrading any applications. In this paper breaks down the renditions of previously and after an application the update application running on the Android Application on different stage and the collected consent records. It intends to find the similar authorizations when the update, and erased and recently included authorizations after the any update were distinguished, and accordingly permission and security issues that can require from the authorizations and management that not required for IoT devices to find out the specific capacities

Android permissions,Management on premission, Android IoT platform, Android update, Android security

[1] Kimberly Tam, Ali Feizollah, Nor Badrul Anuar, and Rosli Salleh, Lorenzo Cavallaro, “The Evolution of Android Malware and Android Analysis Techniques”, Journal of ACM Computing Surveys. 02. 2017.
[2] Yonghong Wu, Jianchao Luo and Lei Luo, “Porting mobile web application engine to the Android platform”, IEEE International Conference Computer and Information Technology, 07. 2010.
[3] Sung Wook Moon, Young Jin Kim, Ho Jun Myeong, Chang Soo Kim, Nam Ju Cha, and Dong Hwan Kim, “Implementation of Smartphone Environment Remote Control and Monitoring System for Android Operating System-based Robot Platform”, International Conference on Ubiquitous Robots and Ambient Intelligence, 11. 2016.
[4] Xuetao Wei, Lorenzo Gomez, Lulian Neamtiu, Michalis Faloutsos, “Permission Evolution in the Android Ecosystem”, Proceedings of the 28th Annual Computer Security Applications Conference 2012, ACSAC ’12, pp. 31-40, 07. 2012.
[5] Xiang Li, Jianyi Liu, Yanyu Huo, Ru Zhang, Yuangang Yao, “An Android malware detection method based on androidmanifest file”, Cloud Computing and Intelligence Systems (CCIS), 08. 2016
[6] Jignesh Joshi, Chandresh Parekh, “Android Smartphone Vulnerabilites : A Survey”, Advances in Computing, Communication, & Automation (ICACCA) (Spring), 09. 2016.
[7] Kathy Wain Yee Au, Yi Fan Zhou, Zhen Huang, Phillipa Gill and David Lie, “Short Paper: A Look at SmartPhone Permission Models”, In Proceedings of the 1st ACM Workshop on Security and Privacy in SmartPhones and Mobile Devices, SPSM ’11, pp. 63-68, 10. 2011.
[8] Mengyu Qiao, Andrew H. Sung and Qingzhong Liu, “Merging Permission and API Features for Android Malware Detection”, IIAI International Congress on Advanced Applied Informatics, 07. 2016.
[9] Bhaskar Sarma, Ninghui Li, Chris Gates, Rahul Potharaju, Cristina Nita-Rotaru, “Android Permissions: A Perspective Combining Risks and Benefits”, Proceedings of the 17th ACM symposium on Access Control Models and Technologies, SACMAT ’12, pp. 13-22. 06. 2012.
[10]Panagiotis Andriotis, Martina Angela Sasse, Gianluca Stringhini, “Permissions Snapshots: Assessing Users’ Adaptation to the Android Runtime Permission Model”.

Menakarani R, Udayarani V, "Android-based IoT Platform Environment and Permission Management", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.319-322, 2019.

there have been many technologies developed recently on embedded processing that have enabled the vision based systems to detect fire using convolutional neural networks (CNN). All such methods need large memory and more computational time. In this research paper we initiate more efficient fire detection strategy with high performance. Here we are considering computational complexity and exact model for the problem by comparing other computational expensive networks. By considering the nature of problem statement, we can increase the efficiency and accuracy of the model. The results on benchmark datasets of fire shows us the efficient work of the proposed system with validation for detection of fire under cctv maintenance compared to other art of methods.

CNN, Fire detection, stride, filters, pooling, surveillance videos

[1] KHAN MUHAMMAD 1 , (Student Member, IEEE), JAMIL AHMAD 1 , (Student Member, IEEE),IRFAN MEHMOOD 2 , (Member, IEEE), SEUNGMIN RHO 3 , (Member, IEEE), SUNG WOOB BAIK 1 , (Member, IEEE) “ Convolutional Neural Networks based Fire Detection in Surveillance Videos”.
[2] K. Muhammad, J. Ahmad, and S. W. Baik, "Early Fire Detection using Convolutional Neural Network during Surveillance for Effective Disaster Management," Neurocomputing, 2017/12/29/ 2017.
[3] B. U. Töreyin, Y. Dedeoğlu, U. Güdükbay, and A. E. Cetin,"Computer vision based method for real-time fire and flame detection," Pattern recognition letters, vol. 27, pp. 49-58, 2006.
[4] L. Rossi, M. Akhloufi, and Y. Tison, "On the use of stereovision to develop a novel instrumentation system to extract geometric fire fronts characteristics," Fire Safety Journal, vol. 46, pp. 9-20, 2011.
[5] P. V. K. Borges and E. Izquierdo, "A probabilistic approach for vision-based fire detection in videos," IEEE transactions on circuits and systems for video technology, vol. 20, pp. 721-731, 2010.
[6] P. Foggia, A. Saggese, and M. Vento, "Real-Time Fire Detection for Video-Surveillance Applications Using a Combination of Experts Based on Color, Shape, and Motion," IEEE TRANSACTIONS on circuits and systems for video technology, vol. 25, pp. 1545-1556, 2015.
[7] Vinay Chowdary and Mukul Kumar Gupta “Automatic Forest Fire Detection and Monitoring Techniques: A Survey “
pp. 2018.
[8] Pasquale Foggia ; Alessia Saggese ; Mario Vento “Real-Time Fire Detection for Video-Surveillance Applications Using a Combination of Experts Based on Color, Shape, and Motion” Volume: 25 , Issue: 9 , Sept. 2015
[9] M. V. D. Prasad, G. Jaya Sree, K. Gnanendra, P. V. V. Kishore and D. Anil Kumar “Fire Detection Using Computer Vision Models in Surveillance Videos” Vol. 12, No. 19, pp. October 2017

Shruthi G, Disha Bhat, Gagana H, Dimple M K, Kavitha, "Forest Fire Detection Using Convolutional Neural Networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.323-325, 2019.

The Banking industry has undergone numerous changes within the manner they conduct the business and target modern technologies to contend the market. The industry has started realizing the importance of making the knowledge domain and its utilization for the advantages of the bank within the space of strategic progressing to survive within the competitive market. within the era, the technologies area unit advanced and it facilitates to get, capture and store information area unit inflated staggeringly. The rise within the large volume of information as a vicinity of day to day operations and through different internal and external sources, forces information technology industries to use technologies like data processing to remodel information from information. Data processing technology provides the ability to access the correct information at the correct time from large volumes of information. Banking industries adopt the information mining technologies in numerous areas particularly in client segmentation and gain, predictions on Prices/Values of various investment merchandise, market business, dishonorable dealings detections, risk predictions, default prediction on evaluation. It is a valuable tool that identifies useful information from great deal of information. This study shows the importance of information mining technologies and its blessings within the banking and monetary sectors. This paper plans to exhibit the huge movements and latest DM executions in banking post 2013. By gathering and examining the patterns of research center, information assets, mechanical guides, and information systematic apparatuses, this paper adds to conveying important bits of knowledge as to the future improvements of both DM and the financial segment alongside a far reaching one stop reference table. Additionally, we recognize the key deterrents and present a rundown for every single invested individual that are confronting the difficulties of enormous information. This paper incorporates the general Data Mining system to defeat the contentions of bank database, misrepresentation recognition, database security and to make the safe exchanges from the database.

Data Mining, Banking Sector, Financial Fraud, Risk Management, Customer Relationship Management, Database security, Money Laundering, Decision Tree, CRISP-DM, Naïve Bayes, Neural Network, C5.0

[1] H. Hassani, X. Huang, E. Silva,“Digitalisation and Big Data Mining in Banking”, MDPI, Big Data and Cognitive Computing, 2018, 2, 18; doi:10.3390/bdcc2030018
[2] S. K. Rodge,“Study Of Data Mining On Banking Database in Fraud Detection Techniques”, International Research Journal of Engineering and Technology (IRJET), Vol. 3 Issue. 5,2016, e-ISSN: 2395 -0056, p-ISSN: 2395-0072
[3] S. Pulakkazhy, R.V.S. Balan,“Data Mining In Banking And Its Applications-A Review”, Journal of Computer Science 9 (10): 1252-1259, 2013, ISSN: 1549-3636
[4] M. R. Islam and M. A. Habib,“A Data Mining Approach to Predict Prospective Business Sectors for Lending in Retail Banking Using Decision Tree”,International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.5, No.2, 2015
[5] S. Moro, R. Laureano, P. Cortez,“Using Data Mining For Bank Direct Marketing: An Application Of The CRISP-DM Methodology”,The 2011 European Simulation And Modelling Conference, Portugal, 2011, EUROSIS-ETI Publication, ISBN: 978-90-77381-66-3
[6] H. A. Elsalamony,“Bank Direct Marketing Analysis of Data Mining Techniques”, International Journal of Computer Applications (0975 – 8887), Vol. 85, No. 7, 2014
[7] V. Jayasree, R. V. S. Balan, “A Review on Data Mining in Banking Sector”, American Journal of Applied Sciences 10 (10): 1160-1165, 2013, ISSN: 1546-9239

N. B. Rao, V. R. Hulipalled, "A Survey on The Applications and Techniques Used in Bank Data Mining", International Journal of Computer Sciences and Engineering, Vol.07, Issue.14, pp.326-334, 2019.

This paper aims to provide a solution that uses a multimodal approach to analyse large intake of audio and video data and use it to understand the emotions of a subject and to describe the current surroundings to the subject in question. The model is trained on the egocentric data, which contains audio and video signals. The model contains emotion recognition and a speech recognition which extract features of their own allowing to perform a classification on the emotions. The large inflow of data from up and coming technologies like Google Lens and onset of Internet of things are key application points for this solution.

Emotion Recognition, Face Extraction, Speech Recognition,Scene Description,Life Logging

[1] A. Gutierrez and Z. Robert. Lip Reading Word Classification
[2] Ashish B. Ingale,.D.S.Chaudhari "Speech Emotion Recognition" in International Journal of Soft Computing and Engineering (IJSCE)
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