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The Internet of Things (IoT) and its various application domains are drastically changing people`s lives by providing intelligent services that will eventually become an intrinsic part of their daily environment. The data flows received from various actuators and sensors are used to power the IoT services. The accuracy and security of sensor data supplied across the Internet of Things system is a vital aspect in ensuring that IoT services work properly. As a result, data validation in a remote IoT network is becoming increasingly important. Even though the immediate option of establishing duplicate identical systems can provide validation, real-world change limitations can make this difficult, if not impossible. So here in this paper we have proposed an intelligent validation scheme. We have evaluated the performance and effectiveness of proposed scheme by comparing with an existing technique that uses BL and KP-ABE scheme. In terms of time necessary to sense the data, data gathering time, time required to validate the data, and end to end delay, the suggested method outperforms the existing validation methodology.

Ingternet of Things, Information Validation, Multi agents

[1] I. M. Pires, N. M. Garcia, N, Pombo, F. Florez-Revuelta, N. D. Rodriguez, "Validation Techniques for Sensor Data in Mobile Health Applications", Journal of Sensors, Vol. 2016.
[2] J. Ravichandran, A. I. Arulappan, "Data Validation Algorithm for Wireless Sensor Networks", International Journal of Distributed Sensor Networks, Vol. 2013.
[3] N. A. M. Alduais, J. Abdullah, A. Jamil, L. Audah and R. Alias, "Effect of Data Validation Schemes on the Energy Consumptions of Edge Device in IoT /WSN", In the Proceedings of the 14th International Wireless Communications & Mobile Computing Conference (IWCMC), Limassol, , pp. 77-81, 2018.
[4] N. A. M. Alduais, J. Abdullah, A. Jamil, L. Audah and R. Alias, "Sensor Node Data Validation Techniques for Realtime IoT/WSN Application", In Proceedings of the 14th International Multi-Conference on Systems, Signals & Devices (SSD), Marrakech, , pp. 760-765, 2017.
[5] G. D`Emilia and A. Gaspari, "Data Validation Techniques for Measurements Systems Operating in a Industry 4.0 Scenario a Condition Monitoring Application", 2018 Workshop on Metrology for Industry 4.0 and IoT, Brescia, , pp. 112-116, 2018.
[6] L. Russell, F. Kwamena and R. Goubran, "Towards Reliable IoT: Fog-Based AI Sensor Validation", 2019 IEEE Cloud Summit, Washington, DC, USA, pp. 37-44, 2019.
[7] H. Sandor, B. Genge and Z. Szanto, "Sensor data validation and abnormal behavior detection in the Internet of Things", In the Proceedings of the 16th RoEduNet Conference: Networking in Education and Research (RoEduNet), Targu Mures, pp. 1-5, 2017.
[8] V. Chacko and V. Bharati, "Data Validation and Sensor Life Prediction Layer on Cloud for IoT", In the Proceedings of the IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Exeter, pp. 906-909, 2017.
[9] M. Sutaone, P. Mukherj and S. Paranjape, "Trust-based Cluster head validation and outlier detection technique for Mobile Wireless Sensor Networks", In the Proceedings of the 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, pp. 2066-2070, 2016.
[10] Y. Zhang, C. M. Bingham, M. Gallimore, Z. Yang and J. Chen, "Applied sensor fault detection and validation using transposed input data PCA and ANNs", In the Proceedings of the 2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Hamburg, pp. 269-274, 2012.
[11] A. Appice, P. Guccione, D. Malerba, A. Ciampi, "Dealing with temporal and spatial correlations to classify outliers in geophysical data streams", Information Sciences: an International Journal, Vol.285(1), pp. 162-180, 2014.
[12] F. Angiulli, F. Fassetti, "Detecting Distance-Based Outliers in Streams of Data", In the Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management, CIKM, Lisbon, Portugal, 2007.
[13] M. Gupta, J. Gao, C. C. Aggarwal and J. Han, "Outlier Detection for Temporal Data: A Survey", IEEE Transactions on Knowledge and Data Engineering, Vol. 26, No. 9, pp. 2250-2267, 2014, doi: 10.1109/TKDE.2013.184.
[14] S. Yuxiang, X. Kunqing, M. Xiujun, J. Xingxing, P. Wen and G. Xiaoping, "Detecting Spatio-temporal Outliers in Climate Dataset: A Method Study", In the Proceedingsof the 2005 IEEE International Geoscience and Remote Sensing Symposium. IGARSS `05., Seoul, Korea (South), 2005.
[15] M. Mathioudakis, N. Bansal, N. Koudas, "Identifying, Attributing and Describing Spatial Bursts", In the Proceedings of the VLDB Endowment, Vol. 3. No. 1, pp. 1091-1102, 2010.
[16] Y. Zhang, N. Meratnia and P. Havinga, "Outlier Detection Techniques for Wireless Sensor Networks: A Survey", In the Proceedingsof the IEEE Communications Surveys & Tutorials, Vol. 12, No. 2, pp. 159-170, Second Quarter 2010.
[17] P. Yang, D. Stankevicius, V. Marozas, Z. Deng, E. Liu, A. Lukosevicius, F. Dong, L. Xu, G. Min, "Lifelogging Data Validation Model for Internet of Things Enabled Personalized Healthcare", IEEE Transactions on Systems, Man and Cybernetics: Systems, Vol. 48, No. 1, pp. 50-64, 2018.
[18] M.S. Kakkasageri, S.S. Manvi, "Information Management in Vehicular Ad hoc Networks: A Review", Journal of Network and Computer Applications, Vol. 39, pp 334-350, 2014.
[19] M. A. Cuguero, M. Christodoulou, J. Quevedo, V. Puig, D. Garcia and M. P. Michaelides, "Combining contaminant event diagnosis with data validation/reconstruction: Application to smart buildings", In the Proceedings of the 22nd Mediterranean Conference on Control and Automation, Palermo, Italy, pp. 293-298, 2014.
[20] Y. Chen, J. Yang and S. Jiang, "Data validation and dynamic uncertainty estimation of self-validating sensor", In the Proceedings of the 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Pisa, Italy, pp. 405-410, 2015.
[21] R. Sharifi, R. Langari, "A Hybrid AANN-KPCA Approach to Sensor Data Validation", In the Proceedings of the seventh WSEAS International Conference on Applied Informatics and Communications, Athens, Greece, pp.85-91, 2007.
[22] P. H. Ibarguengoytia, L. E. Sucar and S. Vadera, "Real time intelligent sensor validation", In the Proceedings of the IEEE Transactions on Power Systems, Vol. 16, No. 4, pp. 770-775, 2001.
[23] J. Rivera-Mejia, E. Arzabala-Contreras and A. G. Leon-Rubio, "Approach to the validation function of intelligent sensors based on error`s predictors", In the Proceedings of the 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings, Austin, TX, USA, pp. 1121-1125, 2010.
[24] B. Mounika, G. Raghu, S. Sreelekha and R. Jeyanthi, "Neural network based data validation algorithm for pressure processes", In the Proceedings of the 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kanyakumari, India, pp. 1223-1227, 2014.
[25] Z. Shen and Q. Wang, "Data validation and confidence of self-validating multifunctional sensor",Journal of SENSORS IEEE, Taipei, Taiwan, pp. 1-4, 2012.
[26] Q. Wang, Z. Shen and F. Zhu, "A multifunctional self-validating sensor", In the Proceedings of the 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Minneapolis, MN, USA, pp. 1283-1288, 2013.
[27] Narander Kumar and S. Jitendra Kumar, “Secure Data Validation and Transmission in Cloud and IoT Through Ban Logic and KP-ABE”, International Journal of Sensors, Wireless Communications and Control.
[28] P. H. Sharanappa, M. S. Kakkasageri, "Intelligent Information Gathering Scheme in Internet of Things (IoT)", In the Proceedings of the 11th International Conference on Advanced Computing (ICoAC), Department of Computer Technology, Anna University, MIT Campus, Chennai, India, pp.136-140, 2019.
[29] P. M. Chanal, M. S. Kakkasageri, “Security and Privacy in IoT: A Survey”, Journal of Wireless Personal Communication, Springer, Vol. 115, No. 3, pp.1667–1693, 2020
[30] P. M. Chanal, M. S. Kakkasageri, “Hybrid Algorithm for Data Confidentiality In Internet of Things”, In the Proceedings of the 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT), IIT Kanpur, India, 2019.
[31] P. M. Chanal, M. S. Kakkasageri, “Preserving Data Confidentiality in Internet of Things”, Journal of SN Computer Science, Springer, Vol. 2, No. 1, pp. 1-12, 2021.

Sharanappa P. H., Mahabaleshwar S. Kakkasageri, "Dynamic Information Validation Scheme in Internet of Things: Software Agent based Approach," International Journal of Computer Sciences and Engineering, Vol.9, Issue.7, pp.1-10, 2021.

The recognition of image is one of the most important fields in the image processing and computer vision. Image recognition has many branches but the food image classification is very unique. In today’s world people are very conscious about their health. Many people around the world use some dietary assessment system for planning of their diet. In dietary assessment system people make the use of food image classification to classify the food from the image. The classification of food images is a very difficult task as the dataset of food images is highly non-linear. In this paper, we proposed a method that can classify food images. We used pre trained models for the food image classification. The pre trained models is based on the convolutional neural network. In neural networks the CNNs is highly effective at the task of image classification and other computer vision problem. We classified a food image dataset i.e. food11 and obtained an accuracy of 96.75% in our experiment.

Deep Learning, CNN, Computer Vision, Image processing

[1] Md Tohidul Islam, Sagidur Rahman, B.M. Nafiz Karim Siddique, Taskeed Jabid, “Image Recognition with Deep Learning,” 2018 International Conference on Intelligent Informatics and Biomedical, Bangkok, Thailand, pp. 106-110, 2018.
[2] “Health effects of overweight and obesity in 195 countries over 25 years,”, New England Journal of Medicine 377, no. 1, 13–27, PMID: 28604169, 2017.
[3] Y. He, C. Xu, N. Khanna, C. J. Boushey and E. J. Delp, "Analysis of food images: Features and classification," IEEE International Conference on Image Processing (ICIP), Paris, pp. 2744-2748, 2014.
[4] Z. Zong, D. T. Nguyen, P. Ogunbona and W. Li, "On the Combination of Local Texture and Global Structure for Food Classification," IEEE International Symposium on Multimedia, Taichung, pp. 204- 211, 2010.
[5] S. J. Minija and W. R. S. Emmanuel, "Food image classification using sphere shaped — Support vector machine," International Conference on Inventive Computing and Informatics (ICICI), Coimbatore , pp. 109-113, 2017.
[6] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool, “Food-101 – mining discriminative components with random forests,” Computer Vision – ECCV (Cham) (David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars, eds.), Springer International Publishing, pp. 446–461, 2014.
[7] Chang Liu, Yu Cao, Yan Luo, Guanling Chen, Vinod Vokkarane, and Yunsheng Ma, “Deepfood: Deep learning-based food image recognition for computer-aided dietary assessment,” CoRR abs/1606.05675, 2016.
[8] K. Yanai and Y. Kawano, "Food image recognition using deep convolutional network with pre-training and fine-tuning," IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Turin, pp. 1-6, 2015.
[9] N. Hnoohom and S. Yuenyong, "Thai fast food image classification using deep learning," International ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI-NCON), Chiang Rai, pp. 116-119, 2018.
[10] G. Özsert Yi??i?t and B. M. Özyildirim, "Comparison of convolutional neural network models for food image classification," IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Gdynia, pp. 349-353, 2017.
[11] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, and Zbigniew Wojna, “Rethinking the inception architecture for computer vision,” CoRR abs/1512.00567 ,2015.
[12] J. Deng, W. Dong, R. Socher, L. J. Li, Kai Li and Li Fei-Fei, "ImageNet: A large-scale hierarchical image database," IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, pp. 248- 255, 2009.
[13] D. Ciregan, U. Meier and J. Schmidhuber, "Multi-column deep neural networks for image classification," IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, pp. 3642-3649, 2012.
[14] Kiyoharu Aizawa, Makoto Ogawa, “FoodLog: Smartphone based Multimedia Food Recording Tool” International Conference on Artificial Reality and Telexistence (ICAT), Tokyo, Japan, pp.143-144, 2013.
[15] Keigo Kitamura, Toshihiko Yamasaki, Kiyoharu Aizawa, “FoodLog: Capture, Analysis and Retrieval of Personal Food Images via Web” CEA, Beijing, China, pp. 23-29, 2009.
[16] Fengqing Zhu, Marc Bosch, Insoo Woo, SungYe Kim, Carol J. Boushey, David S. Ebert & Edward J. Del, “The Use of Mobile Devices in Aiding Dietary Assessment & Evaluation”, IEEE Journal of Selected Topics In Signal Processing, pp. 756-766, 2010.
[17] Christian Szegedy, Wei Liu, Yangqing Jio, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich, “Going Deeper with Convolution”,CVPR, pp. 1-9, 2015.
[18] Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alexander A. Alemi, “Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning”, AAAI Conference on Artifical Intelligence, pp. 4278-4284, 2017.
[19] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun, “Deep Residual Learning for Image Recognition”, IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016.

Yash Baid, Avinash Dhole, "Food Image Classification Using Deep Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.9, Issue.7, pp.11-15, 2021.

Machine learning is one of the fast growing areas of interest in artificial intelligence adopted by professional in every spheres of life that uses algorithms with data to sytematically learn patterns and improve from experience. The increasing competitive and robust predicting methods of machine learning are becoming more interesting and popular. This is valuable to investors, surveyors and valuers against manually computed payment table values that depends on emperical results. There are tedious and rigorous processes in valuation practice that involves some aspects of financial analysis in computations for the one pound table values. The aim is to build K-nearest neigbr and decision tree model to predict the nemeric values in amount of one pound table at a give rate of interest and period of years.This model is useful to investors, accountants, data professionals, surveyors and valuers interested in financial analysis and its applications. A cross validation test was carried out with predicted R-squared test to detect overfitting and generalize model performance on testing dataset. We introduced noisy data with smoothing curve expeoneintial function to overcome the risk of overfitting in predicting target varaible. The K-nearest neighbor and decision tree techniques were trained, tested and resulted into 95.76% and 99.86% respectively.

Artificial intelligence, Decision tree, K-nearest neighbor, Machine learning

[1] Y. LeCun, Y. Bengio, and G. Hinto, "Deep Learning," Nature, vol.521, no.7553, pp.436-444, 2015.
[2] L. Breiman, "Random Forests, "Machine Learning", vol.45, no.1, pp.5-12, 2001.
[3] A. Gupa, A. Mohammad, A. Syed, and M. N. Halgamuge, "A Comparative Study of Classification Algorithms Using Data Mining: Crime and Accidents in Denver City the USA," International Journal of Advanced Computer Science and Applications(IJACSA), vol.7, no.7, pp.374-381, 2016.
[4] K. Wisaeng, "A Comparison of Different Classification Techniques for Bank Direct Marketing," International Journal Of Soft Computing and Engineering(IJSCE), vol.3, no.4, 116–119, 2013.
[5] M. Fatima, M. Pasha, "Survey of Machine Learning Algorithms for Disease Diagnostic," Journal of Intelligent Learning Systems and Applications,vol.9, no.1, pp.1-16, 2017.
[6] M. Abedini, N. C. F. Codella, R. Connell, R. Garnavi, M. Merler, S. Pankanti, J. R. Smith, and T. Yeda-Mahmood, "A Generalized Framework for Medical Image Classification and Recognition," IBM J Res Dev. vol.59, no.(2/3), pp.1-18, 2015.
[7] G. Kashyap, and E. Chauhan, "Parametic Comparisons of Classification Techniques in Data Mining Applications," International Journal of Engineering Development and Research(IJEDR), vol.4, no.2, pp.1116-1121, 2016.
[8] A. Upadhayay, S. Shukla, and S. Kumar, "Empirical Comparison by data mining Classification algorithms (C 4.5 A & C 5.0) For thyroid cancer data set," International Journal of Computer Science and Communication Networks(IJCSCN), vol.3, no.1, pp.64–68, 2013.
[9] K. H. Rao, G. Srinivas, A. Damodhar, and M. V. Krishna, (2011). Implementation of anomaly detection technique using machine learning algorithms, International Journal of Computer Science and Telecommunication(IJCST), vol.2, no.3, pp.25-31, 2011.
[10] S. Ruggieri, "Efficient C4.5 Classification Algorithm," Knowledge and Data Engineering, IEEE Transactions, vol.14, no.2, pp.438-444, 2002.
[11] J. G. Moreno-Torres, J. A. Saez, and F. Herrera, "Study on the Impact of Partition-Induced Dataset Shift on K-Fold Cross-Validation," IEEE Transactions on Neural Networks and Learning Systems, vol.23, no.8, pp.1304-1312, 2012.
[12] I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, "Practical Machine Learning Tools and Techniques," Morgan Kaufmann, pp.34-89, 2016.
[13] V. Mongia and G. Singh, "Automatic Evaluation of Best Investment Options for Investors Using Optimal Decision Tree Algorithm," International Journal of Computer Applications(IJCA), vol.96, no.20, pp.39-44, 2014.
[14] S. Borde, A. Rane, G. Shende, and S. Shetty, "Real Estate Investment Advising Using Machine Learning," International Research Journal of Engineering and Technology(IRJET), vol.4, no.3, pp.1821-1825, 2017.
[15] D. S. Jadhav, and H. P. Channe, "Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques," International Journal of Science and Research (IJSR), vol.5, no.1, pp.1842-1845, 2014.
[16] K. Alkhatib, H. Najadat, I. Hmeidi, and M. K. Ali-Shatnawi,. "Stock Price Prediction Using K-Nearest Neighbor (kNN) Algorithm," International Journal of Business, Humanities and Technology(IJBHT), vol.3, no.3, pp.32-44, 2013.
[17] A. Singh, M. N. Halgamuge, and R. Lakshmiganthan, "Impact of Different Data Types on Classifier Performance of Random Forest, Naive Bayes, and K-Nearest Neighbors Algorithm," International Journal of Advanced Computer Science and Applications(IJACSA), vol.8, no.12, pp.1-11, 2017.
[18] A. Liaw, and M. Wiener, "Classification and Regression by Random Forest," R News, vol. 2, no.3, pp.18-22, 2003.
[19] T. Mary-Huard, and S. Robin, “Tailored aggregation for classification,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, no.11, pp.2098–2105, 2009.
[20] A. W. Davidson, "Parry`s Valuation and Investment Tables," Estates Gazette, 13th Edition, pp.1-550, 2013.
[21] V. Podgorelec, P. Kokol, B. Stiglic, I. Rozman, "Decision Trees: An Overview andTheir Use in Medicine," Journal of Medical Systems Kluwer Academic/Plenum Press, vol.25, no.5, pp.445-463, 2002.
[22] M. Anyanwu, and S. Shiva, "Comparative Analysis of Serial Detection Tree Classification Algorithms," International Journal of Computer Science and Security, vol.3, no.3, pp.229-240, 2019.
[23] L. Wei-yin, "Fifty Years of Classification and Regression Trees," International Statistical Review, vol.82, no.3, pp.329-348, 2014.
[24] M. Xu, J. Wang, and T. Chen, "Improved Decision Tree Algorithm ID3+, Intelligent Computing in Signal Processing and Pattern Recognition, vol.345, pp.141-149, 2006.
[25] S. B. Imandoust, and M. Bolandraftar, "Application of K-Nearest Neighbor (KNN) Approach for Predicting Economic Events: Theoretical Background", International Journal of Engineering Research and Applications, vol.3, no.5, pp.605-610, 2013.
[26] P. Hall, B. U. Park, and R. J. Samworth, "Choice of Neighbor Order in Nearest-Neighbor Classification," The Annals of Statistics, vol.36, no.5, pp.2135-2152, 2008.
[27] A. Solichin, "Comparison of Decision Tree, Naïve Bayes and KNearest Neighbors for Predicting Thesis Graduation," Proceedings of the Electrical Engineering Computer Science and Informatics, pp.18-20, 2019.
[28] M. T. Van, N. V. Tuan, T. T. Son, H. Le-Minh, and A. Burton, "Weighted K-nearest Neighbor Model for Indoor VLC Positioning," IET Communications, vol.11, no.6, pp.864-871, 2017.
[29] R. Benetis, C. Jensen, G. Karciauskas, and S. Saltenis, "Nearest and Reverse Nearest Neighbor Queries for Moving Objects," The International on Very Large Data Bases, vol.15, no.3, pp.229-250, 2006.
[30] Y. S. Chen, Y. P. Hung, T. F. Yen, and C. S. Fuh, "Fast and Versatile Algorithm for Nearest Neighbor Search Based on a Lower Bound Tree," Pattern Recognition, vol.40, no.2, pp.360–375, 2007.
[31] J. Geu, L. Du, Y. Zhang, and T. Xiog, "A New Distance-Weighted K-nearest Neighbor Classifier," Journal of Information and Computational Science, vol.9, no.6, pp.1429-1436, 2012.
[32] V. Athitsos, J. Alon, S. Sclaroff, and G. Kollios, "Boostmap: An Embedding Method for Efficient Nearest Neighbor Retrieval," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, no.1, pp.89–104, 2007.
[33] C. Domeniconi, J. Peng, and D. Gunopulos, "Local Adaptive Metric Nearest-Neighbor Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, no.9, pp.1281-1285, 2002.
[34] H. Wang, “Nearest Neighbors by Neighborhood Counting,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, no.6, pp.942–953, 2009.
[35] E. Bax, "Validation of Nearest Neighbor Classifiers," IEEE Transaction Information Theory, vol.46, pp.2746-2752, 2007.
[36] S. Zhao, C. Rui, and Y. Zhang, "MICKNN Multi-instance Covering KNN Algorithm," Tsinghua Science and Technology, vol.18, no.4, 360-368, 2013.
[37] X. Zhang, and S. A. Jiang, "A Splitting Criteria Based on Similarity in Decision Tree Learning," JSW, vol. 7, no. 8, pp.82-1775, 2012.
[38] Koli, S. "Sentiment Analysis with Machine Learning," International Journal of Computer Science and Engineering(IJCSE), Vol.9, no.6, pp.77-82, 2012.
[39] Ignatious, J., Kulkarni, Y., Bari, S. and Naglot, D. "Comparative Analysis of Machine Learning Algorithms For Credit Card Fraud Detection," International Journal of Computer Science and Engineering(IJCSE), vol.8, no.6, pp.6-9, 2020.

Stanley Ziweritin, Iduma Aka Ibiam, Taiwo Adisa Oyeniran, Godwin Epiahe Oko, "KNN and Decision Tree Model to Predict Values in Amount of One Pound Table," International Journal of Computer Sciences and Engineering, Vol.9, Issue.7, pp.16-21, 2021.

Sentiment analysis is a technique which is used for Natural language Processing, text analysis, text pre-processing etc. are the trending research field in current time. Sentiment analysis using different techniques and tools for analyze and arrange the unstructured data in a manner that objective results can be generated from them. By using these techniques, allow a computer to understand what is being said by humans. Sentiment analysis uses different techniques to determine the sentiment from a text or sentence or expression. The Internet is a huge source of natural language. People share their thoughts and experiences which are subjective in nature. Many a time, it is difficult for customer to identify whether the product shown by seller is good or bad. Companies may also unaware of customer requirements. Based on product reviews it is necessary to understand the perspective of customer towards a particular product. However, these are in huge amount; therefore a summary of positive and negative reviews needs to be generated. In this project, the main focus is on the review of products and techniques used for extract feature wise summary of the product and analyzed them to form an authentic review. Future work will include more product reviews websites and will focus on higher level natural language processing tasks. Using best and new techniques or tool for more accurate result in which the system except only those keywords which are in dataset rest of the words are eliminated by the system.

Sentiment Analysis, Polarity, Natural Language Processing, Product reviews

[1] D. Vidhate, P. Kulkarni,“Performance comparison of multiagent cooperative reinforcement learning algorithms for dynamic decision making in retail shop application”, International Journal of Computational Systems Engineering, Inderscience Publishers (IEL), Vol 5, Issue 3, pp 169-178, 2019.
[2] Tin Kam Ho, “Random decision forests”, in Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278 – 282, 2018.
[3] D. Vidhate, P. Kulkarni, “A Framework for Dynamic Decision Making by Multi-agent Cooperative Fault Pair Algorithm (MCFPA) in Retal Shop Application”,Information and Communication Technology for Intelligent Systems, Springer, Singapore, pp 693-703, 2018.
[4] R. Moraes, J. F. Valiati, andW. P. Gavio Neto, “Document-level sentiment classification: An empirical comparison between SVM and ANN”, Expert Syst. Appl.,vol. 40,no. 2,pp. 621-633,Feb. 2016.
[5] D. Vidhate, P. Kulkarni,“A Novel Approach by Cooperative Multiagent Fault Pair Learning (CMFPL)”, Communications in Computer and Information Science, Springer, Singapore, Volume 905, pp 352-361, 2018.
[6] H. Tang, S. Tan, and X. Cheng, “A survey on sentiment detection of reviews”, Expert Systems Applications, vol. 36, no. 7, pp. 10760-10773, Sep. 2018.
[7] D. Vidhate, P. Kulkarni,“Exploring Cooperative Multi-agent Reinforcement Learning Algorithm (CMRLA) for Intelligent Traffic Signal Control”, Smart Trends in Information Technology and Computer Communications, Volume 876, pp 71-81,2018.
[8] The Random Forest Algorithm Towards Data Science.: https://towardsdatascience.com/the-random-forest-algorithm
[9] D. Vidhate, P. Kulkarni, “A Framework for Improved Cooperative Learning Algorithms with Expertness (ICLAE)”, International Conference on Advanced Computing and Communication Technologies Advances in Intelligent Systems and Computing, Springer Singapore, Volume 562, pp. 149-160, 2017.
[10] Random Forest Simple Explanation William Koehrsen Medium. https://medium.com/@williamkoehrsen/random-forest-simple
[11] D.Vidhate, P. Kulkarni,“Expertise Based Cooperative Reinforcement Learning Methods (ECRLM)”, International Conference on Information & Communication Technology for Intelligent System, Springer book series Smart Innovation, Systems and Technologies (SIST, volume 84), Springer Cham, pp 350-360, 2017 .

P.S. Gayke, Snehal Kardile, Nutan Dongare, Shweta Pathare, Pallavi Sakat, "Spammer Detection and Fake User Identification in E-Commerce Site," International Journal of Computer Sciences and Engineering, Vol.9, Issue.7, pp.22-25, 2021.

The need to meet the demands of subscribers of wireless services is imperative to Global System for Mobile (GSM) Communication companies. These demands which evolves round maintenance of good network coverage, reduction in service costs and improved Quality of Service (QoS) depends largely on the nature of cellular network masts hoisting. The existing optimization model however, do not handle hoisting of cellular network masts for small size populated areas effectively as a result of insufficient solution space and thus, suffers from poor network coverage. To overcome this challenge, Greedy Algorithm model was proposed. Object Oriented Analysis and Design Methodology (OOADM) was deployed and the proposed model was developed using Java programming language. A comparative analysis between the developed model and Genetic Algorithm was carried out with an aim of determining the best optimization technique for small size populated areas. The statistical results were significant in all the tests performed. The analysis of the results shows that Greedy Algorithm performed better than Genetic Algorithm in optimizing cellular network masts hoisting for small populated areas. Therefore, with this model, optimization of cellular network mast hoisting for small size populated area will be more accurate and hence, more reliable.

Greedy Algorithm (GR) ; Genetic Algorithm (GA) ; Mast; Optimization; Coverage

[1] T. D`Roza, G. Bilchev, “An overview of location-based services”, BT Technology Journal, Vol. 21, pp. 20-27, 2003.
[2] V. F. O’Flaherty, “The Legal Landscape When a Tower Collapses”, Journal of the Missouri Bar, Vol. 57, 2001.
[3] R. Haupt, S. Haupt, “Practical Genetic Algorithm” 2nd edition, John Wiley & Sons, publication, USA, ISBN 0-417-45565-2, 2004.
[4] Zunia, “The Knowledge Exchange”, 2011.
[5] Mastsanity, “Mast Sanity Quick Guide To Mobile Phone Masts”, Registered UK Charity no: 1109757, 2008.
[6] S. Davood, M. Mahnam, A.S. Nookabadi, “An efficient approach to discrete Multiple Different Facility Location Problem,” Service Operations and Logistics and Informatics, In the proceedings of 2008 IEEE International Conference, Vol.2, pp2519, 2524, 2008.
[7.] Ren et al., “Bilevel Simulated Annealing Algorithm for Facility Location Problem” In the proceedings of the 2008 Information Management, Innovation Management and Industrial Engineering. ICII International Conference, Vol.3, pp 17,22, 2008.
[8] K. Rakesh, M.S. Bhuvan, S. Chamarti, P. Bhat, M. Jothish and K. Annappa, “Algorithmic Approach for Strategic Cell Tower Placement”, In the proceedings of the 2014 Fifth International Conference on Intelligent Systems, Modelling and Simulation, 2014.
[9] C. Ming-Tsung, T. Shian-Shyong, “A genetic algorithm for multicast routing under delay constraint in WDM network with different light splitting”, Journal of Information Science and Engineering, Vol. 21, pp 85–108, 2005.
[10] J. Hubaux, S. Znaty: In K. Terplan, P. Morreale., “The Telecommunications Handbook”, CRC Press LLC, 2000.
[11] International Journal Of Advanced Research in Computer and Communication Engineering Vol.2, Issue 12, 2013.
[12] M. Michael, “Voronoi Diagrams”, The Mathematics Enthusiast: Vol 1: No2, p.44-55, 2004.
[13] L. Snyman,” Practical Mathematical Optimization: An Introduction to Basic Optimization theory and Classical and New Gradient-Based Algorithms”, Springer Science & Business Media, 2005.
[14] Szabó Gábor Vilmos,”Optimization Problems in Mobile Communication”, 2005.
[15] A. Okabe, B. Boots, K. Sugihara & S.N. Chiu, “Spatial Tessellations – Concepts and Applications of Voronoi Diagrams”, John Wiley publisher, 2nd edition., 2000.

T.A. Enisuoh, O.R. Okonkwo, N.N. Mbeledogu, "Comparative Study between Greedy and Genetic Algorithms in Optimal Cellular Masts Hoisting Over Population Coverage," International Journal of Computer Sciences and Engineering, Vol.9, Issue.7, pp.26-34, 2021.

Nowadays, random numbers become essential element in many activities. Random numbers can be used in several applications, like simulations, security systems, managements, financial operations, and so on. Indeed, there are many of random numbers generators ‘RNGs’ currently in used, but so far, there is no ideal one, and the requirement of RNGs are increased while the passed time. The main defects in the available RNGs are the short period of its repeat cycle length and also the predefined values of static factors as well. This research will try to suggest a method to extend the periodic cycle of the repetition, and to improve the quality of the generated numbers randomly. The main idea of this research is to build spherical structure to become memory cells that contain initial random numbers, using a combination of two linear congruently generators. Every cell contains one number. Generating any random number takes place by determining the memory cell index found on spherical structure and make the generation of any random number affects the values of many cells of its neighbors, and also affected by the values of many cells randomly. The spherical structure can be represented by three dimensions matrix, with suitable sizes’ not less than 10, 10, 10,.

spherical, memory cell, initial values, linear congruently

[1] William Stallings, “Cryptography and Network Security: Principles and Practice” 3rd Ed. India Reprint. Agrawal-M IETE-Technical-Review. 2009.
[2] Jerry Banks, etc.., "Discrete-Event System Simulation", 3th Ed. Pearson Education, Singapore. 2001.
[3] Bruce Schneier, “Applied Cryptography” 3rd Edition John Wiley & Sons. (ASIA) Pvt. Ltd., 2 Clementi Loop # 02-01, Singapore 129809. 2010.
[4] Borosh. S., and Niederreiter, H., "Optimal Multipliers For Pseudo-Random Number Generation By The Linear Congruential Method", BIT 23,65-74. 1983.
[5] Figiel, K.D., and Sule. D.R., "New Lagged Product Test for Random Number Generators". Comput. Ind. Eng. Vol. 9, 287-296, Mar. 1985.
[6] P. L’Ecuyer, “Efficient and portable combined random number generators”, Communications of the ACM 31 Volume 31 Number 6, USA, June 1988

Saleh Noman Alassali, Hameed Mansour AL-Aqelee, "Random Number Generator with Long Cycle Based on Memory Cells," International Journal of Computer Sciences and Engineering, Vol.9, Issue.7, pp.35-40, 2021.

Most of the time, Students need to visit college administration office to collect various information regarding college such as Tuition fees, Term Schedule, etc. during admission process or as per their daily needs. Hence, to overcome this problem, a chatbot can be designed and developed which can be easily integrated with any college website to provide necessary information regarding college. The goal of AI based chatbot is to make an efficient conversation between human and machine via auditory or textual methods. This project uses Natural language processing to process the user’s query and generate a meaningful response. Based on the information stored in the database, bot itself determines appropriate response of a particular query fired by user. The Chatbot is based on an Artificial Intelligence algorithm, which analyses user’s question and responds with a Naive Bayes’ algorithm. This system will be a Web Application and can reduces work of college administration providing information to students. It also reduces the workload on the staff to answer all the queries of the students.

Chatbot, Query, Graphical User Interface, Natural Language Processing, Artificial Intelligence

[1] K. Bala ,M. Kumar, S.Hulawale, and S. Pandita, “Chat-Bot For College Management System Using A.I”, International Research Journal of Engineering and Technology (IRJET), Vol. 04, Issue.11, pp.2395-0072, 2017.
[2] P. Nikhila, G. Jyothi, K. Mounika, Mr.K. K. Reddy and Dr. B.V. Ramana Murthy, “AI and Web-Based Human-Like Interactive University Chatbot (UNIBOT)”,In theProceedings of the Third International Conference on Electronics Communication and Aerospace Technology[ICECA 2019], pp.1-12,2019.
[3] B.Setiaji, F. W. Wibowo, "Chatbot Using A Knowledge in Database- Human-to-Machine Conversation Modeling", In the Proceedings of the 2016 International Conference on Intelligent Systems, Modelling and Simulation, pp.2166-0670, 2016.
[4] K. Shivam, K. Saud, M. Sharma, S. Vashishth, and S. Patil, “Chatbot for College Website”, International Journal of Computing and Technology, Vol.5, Issue.6, pp.2348-6090, 2018.
[5] E. Haller and T. Rebedea, "Designing a Chat-bot that Simulates a Historical Figure", Faculty of Automatic Control and Computers university of Bucharest, IEEE 978-0-7695-4980-4/13, 2013.
[6] S. B. Sonawane, A. S.Badwar, R. H. Dalvi, G. N. More and S. A. Talekar, “Design of Chatbot System for Student Counselling”, International Journal of New Innovations in Engineering and Technology, Vol.13, Issue.3, pp.2319-6319, 2020.
[7] P. Jain,“College Enquiry Chatbot Using Iterative Model", International Journal of Scientific Engineering and Research (IJSER),Vol.7, Issue.1, pp.2347-3878,2019.
[8] N. Hatwar, A. Patil and D. Gondane, “AI BASED CHATBOT”, International Journal of Emerging Trends in Engineering and Basic Sciences (IJEEBS) ISSN (Online) 2349-6967 Volume 3, Issue 2 (March-April 2016).
[9] A. Prajapati, P. Naik, S. Singh and A. Kale, “Android Based Chatbot For College”,International Journal of Scientific & Engineering Research, Vol.9, Issue.4, pp.2229-5518, 2018.
[10] A. Ohm, K. Bhavani,“Chatbot for Career Guidance Using AI”, International Journal of Computer Sciences and Engineering (IJCSE), Vol.7, Issue.6 ,pp.2347-2693, 2019.
[11] K. G. Yohandi, M. I. Sani, “Designing a Mobile Chatbot For Elementary School Vocabulary”, International Journal of Computer Sciences and Engineering(IJCSE), Vol.8, Issue.7, pp.2347-2693, 2020.

Shivani Pravin Rashinkar, Neha Dilip Wanjol, Shivani Nandkumar Rane, Pushkar P. Shinde, Sopan A. Talekar, "Design of Chatbot System for College Website," International Journal of Computer Sciences and Engineering, Vol.9, Issue.7, pp.41-45, 2021.

The predominant vocabulary of the deaf and dumb, Sign Language serves as a natural, visual language which our brain is capable of processing and deciphering linguistic details. For the past two decades, scientists have been researching the automated recognition of sign language using translating gloves and complex systems with several cameras. Most of these systems can provide partial or complete recognition of the vocabulary but aren’t cost-effective for the average and below-average section of the demographic. With the advent of AI, we’re trying to overcome this biasness in technology. Google’s MediaPipe, which is an open-source framework for multimodal (video, audio, time-series) features with applied ML pipelines, came into existence in 2019. Using MediaPipe’s Multi-hand Tracking model pipeline we can get landmarks of our fingers. This paper advocates the use of MediaPipe Hand Tracking to get hand landmarks, training a Keras RNN-LSTM model with that data to detect Sign Language of 5 trained words in real-time.

MediaPipe, American Sign Language, OpenCV, RNN, LSTM, Real-time

[1] Arpit Mittal, Andrew Zisserman, Philip HS Torr, “Hand detection using multiple proposals”, The British Machine Vision Conference, Vol.40, pp.75.1–75.11, 2011.
[2] Ruchi Manish Gurav, Premanand K. Kadbe, “Real time finger tracking and contour detection for gesture recognition using OpenCV”, In the proceedings of 2015 International Conference on Industrial Instrumentation and Control, ICIC 2015, pp. 974–977, 2015, isbn: 9781479971657, doi: 10.1109/IIC.2015.7150886
[3] Sarfaraz Masood, Adhyan Srivastava, Harish Chandra Thuwal, Musheer Ahmad, “Real-Time Sign Language Gesture (Word) Recognition from Video Sequences Using CNN and RNN”, Intelligent Engineering Informatics, Ed. by Vikrant Bhateja, Carlos A. Coello Coello, Suresh Chandra Satapathy, Prasant Kumar Pattnaik., Springer, Singapore, pp. 623–632, 2018, isbn: 978-981-10-7566-7
[4] Kirsti Grobel and Marcell Assan, "Isolated sign language recognition using hidden Markov models", In the proceedings of 1997 IEEE International Conference on Systems, Man, and Cybernetics, Computational Cybernetics and Simulation, Vol.1, pp. 162-167, 1997, doi: 10.1109/ICSMC.1997.625742
[5] Pradeep Kumar, Himaanshu Gauba, Partha Pratim Roy, Debi Prosad Dogra, “Coupled HMM-based multi-sensor data fusion for sign language recognition”, Pattern Recognition Letters, Vol.86, Pages 1-8, 2017, ISSN 0167-8655, doi : /10.1016/j.patrec.2016.12.004
[6] S. A. Mehdi and Y. N. Khan, "Sign language recognition using sensor gloves," In the proceedings of the 9th International Conference on Neural Information Processing, 2002, ICONIP `02., Vol.5, pp. 2204-2206, 2002, doi: 10.1109/ICONIP.2002.1201884.
[7] J. Ga?ka, M. M?sior, M. Zaborski, K. Barczewska, "Inertial Motion Sensing Glove for Sign Language Gesture Acquisition and Recognition,", IEEE Sensors Journal, Vol.16, pp. 6310-6316, 2016, doi: 10.1109/JSEN.2016.2583542.
[8] Fan Zhang, Valentin Bazarevsky, Andrey Vakunov, Andrei Tkachenka, George Sung, Chuo-Ling Chang, Matthias Grundmann, “MediaPipe Hands: On-device Real-time Hand Tracking”, Google AI Blog, 2020.
[9] Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca, “Python Deep Learning: Exploring deep learning techniques and neural network architectures with Pytorch, Keras, and TensorFlow”, Packt Publishing Ltd, pp. 198-212, 2019.
[10] C. Lugaresi, J. Tang, H. Nash, C. McClanahan, E. Uboweja, M. Hays, F. Zhang, Cl. Chang, MG. Yong, J. Lee, WT. Chang, “Mediapipe: A framework for building perception pipelines”, Google AI Blog, 2019, doi:1906.08172.
[11] Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, Le Lu, Ziyue Xu, Isabella Nogues, Jianhua Yao, Daniel Mollura, Ronald M, "Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning", IEEE Transactions on Medical Imaging, Vol.35, Issue.5, pp. 1285-1298, 2016, doi: 10.1109/TMI.2016.2528162.
[12] Saurav Singla, Anjali Patel, "Comparative Study of the Deep Learning Neural Networks on the basis of the Human Activity Recognition", International Journal of Computer Sciences and Engineering, Vol.8, Issue.11, pp.27-32, 2020.
[13] J. Sun, J. Wang, T. C. Yeh, “Video understanding: from video classification to captioning”. In the Proceedings of the Computer Vision and Pattern Recognition, Stanford University, pp.1-9, 2017.
[14] H. Li, J. Li, X. Guan, B. Liang, Y. Lai, X. Luo, "Research on Overfitting of Deep Learning," In the proceedings of 2019 15th International Conference on Computational Intelligence and Security (CIS), pp. 78-81, 2019, doi: 10.1109/CIS.2019.00025.
[15] S. Bock, M. Weiß, "A Proof of Local Convergence for the Adam Optimizer," In the proceedings of 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1-8, 2019, doi: 10.1109/IJCNN.2019.8852239.
[16] N. D. Marom, L. Rokach, A. Shmilovici, "Using the confusion matrix for improving ensemble classifiers," In the proceedings of 2010 IEEE 26-th Convention of Electrical and Electronics Engineers in Israel, pp. 000555-000559, 2010, doi: 10.1109/EEEI.2010.5662159.
[17] D. Zhang, J. Wang, X. Zhao, X. Wang, "A Bayesian Hierarchical Model for Comparing Average F1 Scores," In the proceedings of 2015 IEEE International Conference on Data Mining, pp. 589-598, 2015, doi: 10.1109/ICDM.2015.44.
[18] M. Genovese, E. Napoli, N. Petra, "OpenCV compatible real time processor for background foreground identification," In the proceedings of 2010 International Conference on Microelectronics, pp. 467-470, 2010, doi: 10.1109/ICM.2010.5696190.
[19] Fatima Ansari, Anwar Hussain Mistry, Yusuf Mirkar, Alim Merchant, "Real Time ASL (American Sign Language) Recognition", International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.848-851, 2019.
[20] S. Singh et al., "Action Replication in GTA5 using Posenet Architecture with LSTM Cells,"In the proceedings of 2021 2nd International Conference on Intelligent Engineering and Management (ICIEM), pp. 544-549, 2021, doi:10.1109/ICIEM51511.2021.9445358.
[21] S. Sharma, K. Shanmugasundaram and S. K. Ramasamy, "FAREC — CNN based efficient face recognition technique using Dlib," In the proceedings of 2016 International Conference on Advanced Communication Control and Computing Technologies (ICACCCT), pp. 192-195, 2016, doi: 10.1109/ICACCCT.2016.7831628.

Souradeep Ghosh, "Proposal of a Real-time American Sign Language Detector using MediaPipe and Recurrent Neural Network," International Journal of Computer Sciences and Engineering, Vol.9, Issue.7, pp.46-52, 2021.

In cyberspace, phishing is one of several cybercrimes that often target internet users all over the world. Phishing performs by trying to trick the victim into accessing a web page which looks original, then instructing them to send important data. For prevention, it is essential to build a phishing detection system (PDS). Recent phishing detection system based on data mining and machine learning techniques. Development of an effective detection system while minimizing false positives and negatives is still a challenge. Instead of using single classification approach it would be better to use ensemble approach. In this work an ensemble approach is utilized to build a phishing website classification system. Bagging also known as Bootstrap Aggregating is a meta algorithm established to enhance the machine learning algorithms performance. To detect phishing website various classification models have been developed and implemented. It is observed that combination of Bagging, AdaBoost and j48 gives best results that is 97.2% accuracy.

Meta-algorithm, classification, web phishing, website, internet, cyber security

[1] F. Furedi, “How the Internet and social media are changing culture,” 2015. [Accessed: 22-Apr-2019].
[2] M. Chewae, S. Hayikader, H. Hasan, and J. Ibrahim, “How Much Privacy We Still Have on Social Network?,” Int. J. Sci. Res. Publ., vol. 5, no. 1, pp. 1– 5, 2015.
[3] P. Patil, R. Rane, and M. Bhalekar, “Detecting spam and phishing mails using SVM and obfuscation URL detection algorithm,” Proc. Int. Conf. Inven. Syst. Control. ICISC 2017, pp. 1–4, 2017.
[4] M. Ganesan and P. Mayilvahanan, “Cyber Crime Analysis in Social Media Using Data Mining Technique,” Int. J. Pure Appl. Math., vol. 116, no. 22, pp. 413–424, 2017.
[5] R. Pompon, D. Walkowski, S. Boddy, and M. Levin, “2018 Phishing and Fraud Report: Attacks Peak During the Holidays” 2018. [Accessed: 20- Apr-2019].
[6] Pritesh Saklecha, Jagdish Raikwar, "Prevention of Phishing Attack using Hybrid Blacklist Recommendation Algorithm", International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.188-191, 2018.
[7] P.Priyadevi, V.Lalithadevi, M.Sughashini, "An Efficient and Usable Client-Side Phishing Detection Application", International Journal of Computer Sciences and Engineering, Vol.06, Special Issue.02, pp.398-401, 2018.
[8] M. Karabatak and T. Mustafa, “Performance comparison of classifiers on reduced phishing website dataset,” in International Symposium on Digital Forensic and Security (ISDFS), 2018, pp. 1– 5.
[9] A. Subasi, E. Molah, F. Almkallawi, and T. J. Chaudhery, “Intelligent phishing website detection using random forest classifier,” 2017 Int. Conf. Electr. Comput. Technol. Appl. ICECTA 2017, vol. 2018-January, pp. 1–5, 2018.
[10] R. M. Mohammad, F. Thabtah, and L. McCluskey, “Intelligent rule-based phishing websites classification,” IET Inf. Secur., vol. 8, no. 3, pp. 153– 160, 2014.
[11] L. Rahman, N. A. Setiawan, and A. E. Permanasari, “Feature Selection Methods in Improving Accuracyof Classifying Students’ Academic Performance,” 2017 2nd Int. Conf. Inf. Technol. Inf. Syst. Electr. Eng. (ICITISEE)., no. 1, pp. 267–271, 2017.
[12] A. F. Nugraha, & L. Rahman, “Meta-Algorithms for Improving Classification Performance in the Web-phishing Detection Process”. In 2019 4th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) (pp. 271-275). IEEE.
[13] UCI Machine Learning, “UCI Machine Learning Repository?: Phising Websites Data Set,” 2019. [Accessed: 20-Apr-2019].
[14] R. M. Mohammad, F. Thabtah, and L. Mccluskey, “Phishing Websites Features,” Ieee. pp. 1–7, 2013.
[15] L. Breiman, “Bagging predictors,” Dep. Stat. Univ. Calif., no. 2, p. 19, 1994.
[16] L. Chen, “Basic Ensemble Learning (Random Forest, AdaBoost, Gradient Boosting)- Step by Step Explained,” 2016. [Accessed: 20-Apr-2019].
[17] Y. Freund and R. E. Schapire, “A Short Introduction to Boosting,” J. Japanese Soc. Artif. Intell., vol. 14, no. 5, pp. 771–780, 1999.
[18] V. Estivill-Castro, M. Lombardi, and A. Marani, “Improving binary classification of web pages using an ensemble of feature selection algorithms,” ACM Int. Conf. Proceeding Ser., 2018
[19] Md. Nurul Amin, Md. Ahsan Habib "Comparison of Different Classification Techniques Using WEKA for Hematological Data" American Journal of Engineering Research (AJER).
[20] N. Saravanan, V. Gayathari “Performance and Classification Evaluation of J48 Algorithm and Kendall’s Based J48 Algorithm (KNJ48)” In 2018 International Journal of Computational Intelligence and Informatics.

Himanshi Agrawal, Rajni Ranjan Singh, "An Ensemble Approach for Detecting Phishing Attacks," International Journal of Computer Sciences and Engineering, Vol.9, Issue.7, pp.53-59, 2021.

The primary aim of Blockchain Technology is to maintain a wide variety of digital information that can be efficiently documented and distributed but, makes it difficult or impossible to edit, hack or cheat the system. In other words, the Blockchain Technology ensures security, transparency as well as decentralization of the digital asset. It can also be thought as a chain or interconnected records, that is stored in the form of blocks which is controlled by no single authority. Blockchain Technology forms the substratum for Bitcoin and other Cryptocurrencies. The Cryptocurrencies can operate without the need for central authority, with the help of Blockchain Technology. This research paper deals with how the World of Cryptocurrency is driven by the Blockchain Technology.

Blockchain Technology, Bitcoin, Cryptocurrencies, and blocks

[1] Zibin Zheng, Shaoan Xie, Hong-Ning Dai Authors, “An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends.”
[2] https://www.investopedia.com/terms/c/consensus-mechanism-cryptocurrency.asp
[3] https://www.binance.com/en
[4] coinmarketcap.com
[5] Syed Zishan Ali, Dolly Sahu, Jatin Sahu Authors, “Bitcoin in Blockchain: A Survey”, Vol. 7, Issue 6, June 2019, IJCSE.
[6] Mausumi Das Nath, Tapalina Bhattasali Authors, “Impact of Blockchain to Secure E-Banking Transaction”, Vol. 7, Issue 18, May 2019, IJCSE.

Greeshma C Shekar, "Blockchain and Cryptocurrency: The World of Blockchain and Cryptocurrency," International Journal of Computer Sciences and Engineering, Vol.9, Issue.7, pp.60-63, 2021.