Volume-7 , Issue-5 , May - 2019 Go Back

Natural scene images are captured at a larger distances to include details in scenery. It is much difficult to identify categories because of uncertain shapes & forms present inside these images. Such ambiguous form of nature, which lacks sharp boundaries, makes discrimination among the classes a complex task. This paper attempts to measure this ambiguity. A natural scene image also can belong to multiple categories at a time which makes a task of classification much more difficult and often leads to classification errors. Binary classification fails to capture this ambiguity while doing multi label classification of the image. This problem can be handled by using fuzzy membership function with assumption that class categories in a natural image are non-mutually exclusive. This work provides a ranking based class membership instead of binary classification.

Fuzzy Membership Function, Multi-Label Classification, Ranking, Supervised Learning

[1] Janhavi Borse, N. M. Shahane, “Multi-Label Classification of A Scene Image using Fuzzy Logic”, IJCA Proceedings ETC 2016, ISBN : 973-93-80975-01-2, vol. 01, no. 2, pp. 4-10, March 18, 2017. 

[2] Alex P. Pentland, “Fractal-Based Description Of Natural Scenes”, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. Pami-6, No. 6, pp. 661-674, November 1984. 

[3] G. Lemaitre and M. Rodojevi, “Texture segmentation: Co- occurrence matrix and Laws’ texture masks methods”, pp. 1-34. 

[4] B.S. Manjunathi and W.Y. Ma, “Texture Features for Browsing and Retrieval of Image Data”, IEEE transactions on pattern analysis and machine intelligence, vol. 18, no. 8, pp. 837-842, august 1996. 

[5] S. E. Grigorescu, N. Petkov, and P. Kruizinga , “A comparative study of filter based texture operators using Mahalanobis Distance”, 0-7695-0750-6/00, IEEE, pp. 1-4, 2000. 

[6] M. Tuceryan and Jain , “Texture Analysis”, ResearchGate Article, pp. 1-42, September 2000. 

[7] M. Lindenbaum and R. Sandler, “Gabor Filter Analysis for Texture Segmentation”, pp. 1-58, May 2005. 

[8] J. Ilonen, J.-K. Kamarainen, H. Kalviainen, “Efficient computation of Gabor features”, pp. 1-29, 2005. 

[9] Y. Alqasrawi, D. Neagu and P. I. Cowling, “Fusing integrated visual vocabularies-based bag of visual words and weighted colour moments on spatial pyramid layout for natural scene image classification”, research gate article in signal image and video processing, pp. 1-25, July 2011. 

[10] G. Madjarov, DragiKocev, DejanGjorgjevikj and S. Dzeroski, “An extensive experimental comparison of methods for multi-label learning” , Elsevier publication on Pattern Recognition, pp. 3084- 3104, 2012. 

[11] M. Pagola, C. Lopez-Molina, “Interval Type-2 Fuzzy Sets Constructed From Several Membership Functions: Application to the Fuzzy Thresholding Algorithm”, IEEE transactions on fuzzy systems, vol. 21, no. 2, pp. 230-244, April 2013. 

[12] Min-Ling Zhang and Zhi-Hua Zhou, “A Review on Multi-Label Learning Algorithms”, IEEE transactions on knowledge and data engineering, vol. 26, no. 8, pp. 1819-1837, august 2014. 

[13] J. Wu1, V. Sheng2, J. Zhang3, Peng peng Zhao1, Z. Cui, “Multi- label Active Learning for Image Classification”, ICIP, pp. 1-5, 2014. 

[14] L. Jing and M. K. Ng , “Sparse Label-Indicator Optimization Methods for Image Classification”, IEEE transactions on image processing, vol. 23, no. 3, pp. 1002-1014, march 2014. 

[15] M. Celuszak and D. Jabry, “ESGI100: Gabor Filter Selection and Computational Processing for Emotion Recognition”, pp. 1-23, 18 may 2014. 

[16] C. H. Lim, A. Risnumawan, and C. S. Chan, “A Scene Image is Nonmutually Exclusive-A Fuzzy Qualitative Scene Understanding”, IEEE transactions on fuzzy systems, vol. 22, no. 6, pp. 1541- 1556, December 2014. 

[17] Riddhi H.Shaparia, Narendra M.Patel, Zankhana H. Shah, “Flower Classification using Different Color Channel”, International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.2, pp.1-6, 2019.
[18] N.S. Lele , “Image Classification Using Convolutional Neural Network”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.22-26, 2018.

Janhavi H. Borse, Dipti D. Patil, "Texture based Ranking of Categories in a Natural Image," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.183-187, 2019.

Capital investment in retail sector and competition in the market has changed the style of marketing. At the same time the enhancements in the field of information technology provided an upper hand to the marketer to know the exact need, preference and perches trend of the customer. By knowing the actual need, preference and purchase trend of customers the marketer can make a future business plan to increase the sale and earn more profit. This paper provides a framework to the retail marketer to find the potential customer by analyzing the previous purchase history of the customer. This task can be accomplished by the use of data mining technique. In this paper we have used k-mean clustering algorithm and Naive Bayes’ classifier for in identifying potential customer for a particular section of products of the retailer.

Naive Bayes,Cluster, Centroid,Foreign Direct Investment(FDI)

[1] Revenue Management Through Dynamic Cross Selling in E-Commerce Retailing Operations Research 54(5 – Serguei Netessine,See Profile, Available from Serguei Netessine, Serguei Netessine -1997
[2] Insurance Databases for Effective Prediction of Customer Preferences over Life Insurance Products – S. Balaji, S. K. Srivatsa, PhD Senior Professor - A LITERATURE REVIEW – Olof Wahlberg, Christer Strandberg, Karl W. Sandberg
[3] Unsupervised Learning in Large Datasets for Intelligent Decision
Making – S. Balaji,Dr. S. K. Srivatsa.
[4] important influences on classification accuracy – Mohammad Saad Al-Ahmadi, Peter A. Rosen, Rick L. Wilson
[5] Decision Support System for A Customer Relationship Management Case Study – Ozge Kart, Alp Kut, Vladimir Radevski
[6] Proceedings of the 40th Hawaii International Conference on System Sciences- 2007 Synergies of Data Mining and Operations Research –Stephan Meisel, Dirk Chr Mattfeld
[7] Stakeholder Perceptions Regarding eCRM: A Franchise Case Study –unknown authors
[8] Xin Zhao, Yi Wang, and Hongwang Cha. “A New Prediction Model of Customer Churn Based on PCA Analysis”. In: Information Science and Engineering (ICISE), 2009 1st International Conference on. IEEE. 2009, pp. 4657–4661.
[9] Qing-an Cui et al. “Using PCA and ANN to identify significant factors and modeling customer satisfaction for the complex service processes”.In: Industrial Engineering and Engineering Management (IE&EM),2011 IEEE 18Th International Conference on. IEEE. 2011, pp. 1800–1804.
[10] Volodymyr Kuleshov. “Fast algorithms for sparse principal component analysis based on Rayleigh quotient iteration”. In: International Conference on Machine Learning. 2013, pp. 1418–1425.
[11] Zhexue Huang. “Extensions to the k-means algorithm for clustering large data sets with categorical values”. In: Data mining and knowledge discovery 2.3 (1998), pp. 283–304.
[12] Charu C. Aggarwal, Alexander Hinneburg, and Daniel A. Keim. “On the Surprising Behavior of Distance Metrics in High Dimensional Space”. In: Lecture Notes in Computer Science. Springer, 2001, pp. 420–434.
[13] James Bennett, Stan Lanning, et al. “The netflix prize”. In: Proceedings of KDD cup and workshop. Vol. 2007. New York, NY, USA. 2007, p. 35.
[14] James Bennett. “The Cinematch system: Operation, scale coverage, accuracy impact”. In: Summer School on the Present and Future of Recommender Systems (2006).
[15] Yehuda Koren. “The bellkor solution to the netflix grand prize”. In:Netflix prize documentation 81 (2009), pp. 1–10.
[16] Andreas T ̈oscher, Michael Jahrer, and Robert M Bell. “The bigchaos solution to the netflix grand prize”. In: Netflix prize documentation (2009), pp. 1–52.
[17] Martin Piotte and Martin Chabbert. “The pragmatic theory solution to the netflix grand prize”. In: Netflix prize documentation (2009).
[18] A Literature Review on Text Mining Techniques and Methods,IJCSE,Vol.06,Issue.02,pp.96-99,Mar-2018.
[19] Meteric for evaluating availability of an information system: A Quantitative approach based on component dependency,IJNSA,Vol-9,number-2,Mar-2017.

I.Gayathri Devi, G. Surya Kala Eswari, G. Kumari, "Classification of Customer to Upgrade Profits in Retail Market with Deep Learning Methodology," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.188-191, 2019.

Installation of capacitors in primary and secondary networks of distribution systems is one of the efficient methods for power loss reduction and voltage profile improvement. The loss of energy mainly incurred in the distribution network. The objective is to minimize the cost function along with power loss reduction and voltage profile improvement. Now a day this multi-objective problem creates a lot of concentration of the researcher. In this paper, a study based on two-stage procedure is presented. In the first stage, the optimum bus location is done by using a sensitivity index and voltage norm. In the second stage, Fruit Fly algorithm is used to optimize the size of the capacitor as well as bus location, a comparative study is made between Particle swarm optimization (PSO) and Fruit Fly optimization for solving the optimal capacitor problem. The Fruit Fly gives a better result in comparison with PSO for this optimization problem in terms of power loss and cost.

Backward sweep forward sweep, Radial distribution, Loss sensitivity Index, Voltage norm, Fruit Fly Optimization

[1] Adel Ali Abou El- Ela, Ragab A.El-Sehiemy, Abdel-Mohsen Kinawy, Mohamed Taha Mouwafi “Optimal capacitor placement in distribution systems for power loss reduction and voltage profile improvement”, IET Generation, Transmission & Distribution, vol. 10, no. 5, pp. 1209-1221, 2016.
[2] K. Prakash, M. Sydulu, “Particle swarm optimization based capacitor placement on radial distribution system”, in Proceedings of IEEE power engineering society general meeting, pp. 1-5, 2007.
[3] Srinivasan Sundararajan, Anil Pahwa “Optimal selection of capacitors for radial distribution systems using a genetic algorithm”, IEEE Transactions on Power Systems, vol. 9, no. 3, August 1994.
[4] K.R. Devabalaji, K. Ravi a, D.P. Kothari, “Optimal location and sizing of capacitor placement in radial distribution system using Bacterial Foraging Optimization Algorithm”, Electrical Power and Energy Systems, vol. 71, pp. 383-390, 2015.
[5] M. D. Reddy, V.C.V Reddy, “Optimal capacitor placement using a fuzzy and real-coded genetic algorithm for maximum savings”, Journal of Theoretical and Applied Information Technology, vol. 4, no. 3, 2008.
[6] El-Fergany, A. Attia, Almoataz Y. Abdelaziz, “Capacitor allocations in radial distribution networks using cuckoo search algorithm”, IET Generation, Transmission & Distribution, vol. 8, no. 2, pp. 223-232, 2014.
[7] A.Y. Abde-laziz , E.S. Ali, S.M. Abd Elazim, “Flower Pollination Algorithm and Loss Sensitivity Factors for optimal sizing and placement of capacitors in radial distribution systems”, Electrical Power and Energy Systems, vol. 78, 207-214, 2016.
[8] V.V.K. Reddy, M. Sydulu, “Index and GA based optimal location and sizing of distribution system capacitors”, IEEE Power Engineering Society General Meeting, pp. 1-4, 2007.
[9] Elsheikh Ahmed, Yahya Helmy, Yasmine Abouelseoud, Ahmed Elsherif, “Optimal capacitor placement and sizing in radial electric power systems”, Alexandria Engineering Journal, vol. 53, no. 4, pp 809-816, 2014.
[10] D. Das, D. P. Kothari, A. Kalam. “Simple and efficient method for load flow solution of radial distribution networks”, International Journal of Electrical Power & Energy Systems, vol. 17, no. 5, pp. 335-346, 1995.
[11] M. Gogoi, Ashim Jyoti Gogoi, S. A. Begum, “Optimizing error function of backpropagation neural network”, International Journal of Computer Science and Engineering, vol. 7, no. 4, 2019.
[12] Wen-Tsao Pan, "A new fruitfly optimization algorithm: taking the financial distress model as an example", Knowledge-Based systems vol. 26, pp. 69-74, February 2012.

Deepjyoti Talukdar, Pranobjyoti Lahon, Ashim Jyoti Gogoi, "Optimal Capacitor Placement using Fruit Fly Optimization," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.192-198, 2019.

Data mining is a process of analyzing data from different perspective and gathering the knowledge from it. Heart disease and stroke prediction is a tough task in classifying and predicting the disease from the obesity, overweight, smoking affected persons. To control, we need an intelligent heart disease prediction system. To develop that system, medical terms such as sex, blood pressure, and cholesterol like 13 input parameters are used. To get more appropriate results, two more attributes i.e. obesity and smoking are used, as these attributes are considered as important attributes for heart disease[1]. The data mining classification techniques such as Neural Networks, Decision Trees, and Naive Bayes are used.

Neural Networks, Decision trees, Naïve bayes, classification, prediction, data mining

[1] Chaitrali S. Dangare and Sulabha S. Apte, PhD, “Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques” published by International Journal of Computer Applications(0975-888), vol. 47,issue no. 10, June 2012.
[2] Frawley and G. Piatetsky -Shapiro, “Knowledge Discovery in Databases: An Overview”. Published by the AAAI Press/ The MIT Press, Menlo Park, C.A 1996.
[3] Sellappan Palaniappan, Rafiah Awang, "Intelligent Heart Disease Prediction System Using Data Mining Techniques", IJCSNS International Journal of Computer Science and Network Security, Vol.8 No.8, August 2008
[4] Niti Guru, Anil Dahiya, Navin Rajpal, "Decision Support System for Heart Disease Diagnosis Using Neural Network", Delhi Business Review, Vol. 8, No. 1 (January - June 2007).
[5] Heon Gyu Lee, Ki Yong Noh, Keun Ho Ryu, “Mining Biosignal Data: Coronary Artery Disease Diagnosis using Linear and Nonlinear Features of HRV,” LNAI 4819: Emerging Technologies in Knowledge Discovery and Data Mining, pp. 56-66, May 2007.
[6] Shantakumar B.Patil, Y.S.Kumaraswamy “Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network”. ISSN 1450-216X Vol.31 No.4 (2009), pp.642-656.
[7] Carlos Ordonez, "Improving Heart Disease Prediction Using Constrained Association Rules," Seminar Presentation at University of Tokyo, 2004.
[8] Kiyong Noh, Heon Gyu Lee, Ho-Sun Shon, Bum Ju Lee, and Keun Ho Ryu, "Associative Classification Approach for Diagnosing Cardiovascular Disease", Springer, Vol:345, pp: 721- 727, 2006.
[9] Franck Le Duff, Cristian Munteanb, Marc Cuggiaa, Philippe Mabob, "Predicting Survival Causes After Out of Hospital Cardiac Arrest using Data Mining Method", Studies in health technology and informatics, Vol. 107, No. Pt 2, pp. 1256-9, 2004.
[10]Clevelanddatabase: http://archive.ics.uci.edu/ml/datasets/Heart+Disease
[11] Dr. Yashpal Singh, Alok Singh chauhan “Neural Networks in data mining” Journal of Theoretical and Applied Information Technology , 2005 - 2009 JATIT.

G. Venugopal, K. Vinay, B. Yasaswi, G. Mohan Sai, G. Trilok Chandra Nithin, "An Enhanced Study on Predicting Heart Diseases Using Datamining Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.199-203, 2019.

The world has revolutionized over the years with the advent of various technologies and life of mankind has taken a significant turnaround in terms of getting the official problems solved in an effective and efficient manner in no time. One of the most powerful technologies that has come up in recent years is cloud computing. This technology has captured a special place in various Information Technology (IT) sectors and business organizations. Among all the aspects of this technology that are in existence, cloud data search optimization has become a key area of focus for the researchers. Various research works were conducted based on several fundamentals such as Gossip Protocol, Genetic Algorithm, Hybrid Algorithm, Multi-Keyword Synonym Query, Particle Swarm Optimization, Honey Bee Optimization, etc. and all these were put into practical purpose with the primary objective of optimizing the search technique in the cloud. In our paper, we have suggested the use of Ant Colony Optimization Algorithm for an effective data search in database and allocating them to the respective clients through shortest possible network path in no time. We have used the concept of pheromone values to conduct this procedure. Our suggested techniques ensure that our algorithm will achieve a higher degree of performance in terms of increased throughput and increased efficiency as compared to the traditional techniques which were carried out earlier.

Database, Client machines, Data Carrier Equipment, Wires, Quadrilateral Obstruction, Pheromone value, Ant Colony Optimization Algorithm

[1] Elham Azhir, Nima Jafari Navimipour, Mehdi Hosseinzadeh, Arash Sharifi, Aso Darwesh, “Deterministic and non-deterministic query optimization techniques in the cloud computing”, Concurrency and Computation Practice and Experience, 5th March 2019, DOI: 10.1002/cpe.5240.
[2] YoungJu Moon, HeonChang Yu, Joon Min Gil and JongBeom Lim, “A slave ants based ant colony optimization algorithm for task scheduling in cloud computing environments”, Human-centric Computing and Information Sciences, 9th October 2017, DOI: 10.1186/s13673-017-0109-2.
[3] Gurneet Kaur, “Role And Importance Of Search Engine Optimization”, International Journal Of Research-Granthaalayah, Volume 5, Issue 6, June 2017, DOI: 10.5281/zenodo.818213.
[4] Sudipta Sahana, Rajesh Bose, Debabrata Sarddar, “An Enhanced Search Optimization Protocol Based on Gossip Protocol for the Cloud”, International Journal of Applied Engineering Research, Volume 12, Number 19, pp. 8436-8442, ISSN 0973-4562, 2017.
[5] Li Liu, Miao Zhang, Rajkumar Buyya, Qi Fan, “Deadline-constrained coevolutionary genetic algorithm for scientific workflow scheduling in cloud computing”, Concurrency and Computation Practice and Experience, WILEY, 22nd July 2016, DOI: 10.1002/cpe.3942.
[6] Mohammed Abdullahi, Md Asri Ngadi, “Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment”, PLOS One, 27th June 2016, DOI: 10.1371/journal.pone.0158229.
[7] Mohammed Abdullahi, Md Asri Ngadi, Shafi’i Muhammad Abdulhamid, “Symbiotic Organism Search optimization based task scheduling in cloud computing environment”, Future Generation Computer Systems, 24th August 2015, DOI: 10.1016/j.future.2015.08.006.
[8] Manish M. Pardeshi, R. L. Paikrao, “Enhanced and Efficient Search Multimedia Data by Using Multi-Keyword Synonym Query”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), Volume 4, Issue 6, June 2015.
[9] George Suciu, Ana Maria Sticlan, Cristina Butca, Alexandru Vulpe, Alexandru Stancu and Simona Halunga, “Cloud Search Based Applications for Big Data - Challenges and Methodologies for Acceleration”, ARMS-CC 2015, LNCS 9438, Springer International Publishing Switzerland 2015, pp. 177–185, 2015, DOI: 10.1007/978-3-319-28448-4_13.
[10] Qiang Xu, Zhengquan Xu, Tao Wang, “A Data-Placement Strategy Based on Genetic Algorithm in Cloud Computing”, International Journal of Intelligence Science, 2015, 5, 145-157, April 2015, DOI: 10.4236/ijis.2015.53013.
[11] Gunvir Kaur, IEr. Sugandha Sharma, “Research Paper on Optimized Utilization of Resources Using PSO and Improved Particle Swarm Optimization (IPSO) Algorithms in Cloud Computing”, International Journal of Advanced Research in Computer Science & Technology (IJARCST 2014), Vol. 2, Issue 2, Ver. 3, April-June 2014.
[12] Medhat Tawfeek, Ashraf El-Sisi, Arabi Keshk and Fawzy Torkey, “Cloud Task Scheduling Based on Ant Colony Optimization”, The International Arab Journal of Information Technology, Vol. 12, No. 2, 23rd April 2014.
[13] Vimmi Makkar, Sandeep Dalal, “Ranked Keyword Search in Cloud Computing: An Innovative Approach”, International Journal of Computational Engineering Research, Volume 3, Issue 6, June 2013.
[14] Dr S. Saravanakumar, K Ramnath, R Ranjitha and V.G.Gokul, “A New Methodology for Search Engine Optimization without getting Sandboxed”, International Journal of Advanced Research in Computer and Communication Engineering, Volume 1, Issue 7, ISSN: 2278-1021, September 2012.
[15] Lizheng Guo, Shuguang Zhao, Shigen Shen, Changyuan Jiang, “Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm”, Journal of Networks, Volume 7, No. 3, March 2012, DOI: 10.4304/jnw.7.3.547-553.

Sudipta Sahana, Tanmoy Mukherjee, Debabrata Sarddar, "A Logical Approach Towards Effective Data Search using Ant Colony Optimization in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.204-210, 2019.

Cloud computing is a paradigm that allows on-demand network access to a shared pool of configurable and reliable computing resources to cloud customers in pay-per-use, fashion. Despite the existence of such merits, there are Security issues such as data integrity, users’ confidentiality, and service availability because of its open and distributed architecture that place restrictions on the use of cloud computing. A preventive approach is to identify such issues and eliminate before it can cause the serious impact to the cloud users. Nowadays, Intrusion Detection Systems (IDSs) are the most widely used method to detect attacks on cloud. Recently, learning-based techniques for security applications are gaining popularity in the literature with the emergence in machine learning. A deep learning is a novel approach to detect cloud threats. The existing Cloud IDSs suffer from low detection accuracy and a high false positive rate. In this research, proposed solution will use deep learning algorithm to improve the effectiveness of our proposed solution. Furthermore, the comparisons with other deep learning algorithm to demonstrate the effectiveness of our proposed solution are given.

Cloud Security, Network Intrusion Detection System, Deep Learning

[1] Mehmood, Yasir, et al. "Intrusion detection system in cloud computing: challenges and opportunities." 2013, IEEE.
[2] Idhammad, Mohamed, Karim Afdel, and Mustapha Belouch. "Distributed intrusion detection system for cloud environments based on data mining techniques." Procedia Computer Science 127 (2018): 35-41.
[3] Hamid, Yasir, M. Sugumaran, and LudovicJournaux. "Machine learning techniques for intrusion detection: a comparative analysis." Proceedings of the International Conference on Informatics and Analytics. ACM, 2016.
[4] Haq, Nutan Farah, et al. "Application of machine learning approaches in intrusion detection system: a survey." IJARAI-International Journal of Advanced Research in Artificial Intelligence 4.3 (2015): 9-18.
[5] Dhanabal, L., and S. P. Shantharajah. "A study on NSL-KDD dataset for intrusion detection system based on classification algorithms." International Journal of Advanced Research in Computer and Communication Engineering 4.6 (2015): 446-452.
[6] Buczak, Anna L., and ErhanGuven. "A survey of data mining and machine learning methods for cyber security intrusion detection." IEEE Communications Surveys & Tutorials 18.2 (2016): 1153-1176.
[7] Nguyen, Khoi Khac, et al. "Cyberattack detection in mobile cloud computing: A deep learning approach." 2018 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2018.
[8] Van, Nguyen Thanh, Tran Ngoc Thinh, and Le Thanh Sach. "An anomaly-based network intrusion detection system using deep learning." 2017 International Conference on System Science and Engineering (ICSSE). IEEE, 2017.
[9] Feng, Fang, et al. "Anomaly detection in ad-hoc networks based on deep learning model: A plug and play device." Ad Hoc Networks 84 (2019): 82-89.
[10] Shone, Nathan, et al. "A deep learning approach to network intrusion detection." IEEE Transactions on Emerging Topics in Computational Intelligence 2.1 (2018): 41-50.
[11] Kwon, Donghwoon, et al. "A survey of deep learning-based network anomaly detection." Cluster Computing (2017): 1-13.
[12] Aldwairi, Tamer, Dilina Perera, and Mark A. Novotny. "An evaluation of the performance of Restricted Boltzmann Machines as a model for anomaly network intrusion detection." Computer Networks 144 (2018): 111-119.
[13] Almseidin, Mohammad, et al. "Evaluation of machine learning algorithms for intrusion detection system." Intelligent Systems and Informatics (SISY), 2017 IEEE 15th International Symposium on. IEEE, 2017.
[14] Haque, MdEnamul, and Talal M. Alkharobi. "Adaptive hybrid model for network intrusion detection and comparison among machine learning algorithms." International Journal of Machine Learning and Computing 5.1 (2015): 17.
[15] Ahmed, Mohiuddin, AbdunNaser Mahmood, and Jiankun Hu. "A survey of network anomaly detection techniques." Journal of Network and Computer Applications 60 (2016): 19-31.
[16] Liu, Qiang, et al. "A survey on security threats and defensive techniques of machine learning: a data driven view." IEEE access 6 (2018): 12103-12117.
[17] Kwon, Donghwoon, et al. "A survey of deep learning-based network anomaly detection." Cluster Computing (2017): 1-13.
[18] Belavagi, Manjula C., and BalachandraMuniyal. "Performance evaluation of supervised machine learning algorithms for intrusion detection." Procedia Computer Science 89 (2016): 117-123.
[19] Shanmugavadivu, R., and N. Nagarajan. "Network intrusion detection system using fuzzy logic." Indian Journal of Computer Science and Engineering (IJCSE) 2.1 (2011): 101-111.
[20] Shone, Nathan, et al. "A deep learning approach to network intrusion detection." IEEE Transactions on Emerging Topics in Computational Intelligence 2.1 (2018): 41-50.
[21] Park, Kinam, Youngrok Song, and Yun-Gyung Cheong. "Classification of Attack Types for Intrusion Detection Systems Using a Machine Learning Algorithm." 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (Big Data Service). IEEE, 2018.
[22] https://en.wikipedia.org/wiki/Deep_learning

Nidhi Thakkar, Miren Karamta, Seema Joshi, M. B. Potdar, "Anomaly Detection and Categorization in Cloud Environment using Deep Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.211-214, 2019.

Nowadays, a country’s economy development is estimated from tracking light from space at night. Therefore, electricity has become a major factor in determining national economy. Accurate models have become necessary to help electricity companies to forecast load in advance so that electricity is always present in every corner of a country. In this paper, we have made an attempt to review electricity load forecasting techniques. This review paper overviews the existing electricity demands prediction approaches such as traditional approaches, statistical approaches and machine learning based approaches. It further presents the pros and cons of various techniques. It also presents the challenges of this predictive analysis.

Prediction, smart grid, artificial neural network, short-term load forecasting

[1] H. S. Hippert, C. E. Pedreira, and C. R. Souza, “Neural Networks for Short-Term Load Forecasting: A review and Evaluation”, IEEE Trans. Power Syst., vol. 16, no. 1, pp. 44-51, 2001.
[2] Box GEP, Jenkins GM, “Time series analysis, forecasting and control”, Holden-Day, San Francisco, CA, 1970.
[3] Hamid R. Khosravani, Maria Del Mar Castilla, Manuel Berenguel, Antonio E. Ruano and Pedro M. Ferreira, “A comparison of Energy Consumption Prediction model based on Neural Networks of a Bioclimatic Building”, Energies, 2016.
[4] Kangji Li, Chenglei Hu, Guohai Liu, Wenping Xue, “Building electricity consumption prediction using optimized artificial neural networks and principle component analysis”, Energy and Buildings, vol. 108, 2015 pp 106-113.
[5] Jihoon Moon, Jinwoong Park, Eenjun Hwang, Sanghoon Jun, “Forecasting power consumption for higher educational institutions based on machine learning”, Springer, 2017.
[6] S. Saravanan, S. Kannan and C. Thangaraj, “Forecasting India’s Electricity Demand Using Artificial Neural Network”, IEEE- International Conference on Advances in Engineering, Science And Management (ICAESM-2012), March 30, 31, 2012.
[7] Rodrigo F. Berriel, Andre Teixeira Lopes, Alexandre Rodrigues, Flavio Miguel Varejao, Thiago Oliveira-Santos, “Monthly Energy Consumption Forecast: A Deep Learning Approach”, International Joint Conference on Neural Networks, 2017.
[8] Wei Yu, Dou An David Griffith, Qingyu Yang and Guobin Xu, “Towards Statistical Modelling and Machine Learning Based Energy Usage Forecasting in Smart Grid”, ACM, 2015.
[9] Shu Fan and Rob J Hyndman, “Short-term load forecasting based on a semi-parametric additive model”, IEEE Transactions on Power Systems, 2010.
[10] Vincent Thouvenot, Audrey Pichavant, Yannig Goude, Anestis Antoniadis and Jean-Michel Poggi, “Electricity Forecasting using Multi-Stage Estimators of Nonlinear Additive Models”, IEEE Transactions on Power Systems, vol. 31, no. 5, September 2016.

Diksha Rai, Vandan Tewari, "Energy Demand Forecasting: A Review on Methodologies and Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.215-218, 2019.

Wireless technologies are becoming more popular around the world and the consumers appreciate this wireless lifestyle. Technology is a never ending process. To be able to design a product using the current technology that will be beneficial to the lives of others is a huge contribution to the community. This paper presents the design and implementation of a low cost but and secure mobile phone based home automation system. The design is based on a stand-alone Arduino BT board and the home appliances are connected to the input/ output ports of this board via relays. The communication between the cell phone and the Arduino BT board is wireless. This system is designed to be low cost and scalable allowing variety of devices to be controlled with minimum changes to its core. Protection can also used in this system so that only authorized user can access this system(like using password on your mobile phone).

Smart Home; Automation System; Microcontroller; Wireless Communication; Arduino

[1] Bilal Ghazal and Khalid Al-Khatib, “Smart Home Automation System for Elderly, and Handicapped People using Xbee”, Vol. 9, No. 4, pp. 203-210, 2015.
[2] R. Piyare, M.Tazil, “Bluetooth Based Home Automation System Using Cell Phone”, IEEE 15th International Symposiumon Consumer Electronics, Vol. 4, pp. 192-195, 2011.
[3] Akbar Satria, Muhammad Luthfi Priadi Lili Ayu Wulandhari and Widodo Budiharto, “The Framework of Home Remote Automation System Based on Smartphone”, Vol. 9, No. 1, pp. 53-60, 2015.
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Deepti Aggarwal, Sushmita Gupta, Sukirti Katheria, Bipin Kumar Verma, "A Review on Home Automation Using Smart Phone," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.219-222, 2019.

With information and data collection in richness, there is a much need for high safety. The Face recognition (FR) is a firm growing and interesting vicinity in real time constituting of various aspects. For this a number of set of rules has been developed. The consideration made here is for studying a variety of methodologies or techniques used in for the face recognition (FR) aspect. This consist of the “Principle Component Analysis” concept, the “Linear Discriminant Analysis”, the “Independent Component Analysis”, the “Support Vector Machines”, the Gabor wavelet, also the soft computing tools like the ANN for recognition & some hybrid combination. The evaluation investigates some of these strategies with parameters and challenging situations of FR scenario like the illumination, pose variations and the facial expressions.

FR, Principal Component Analysis (PCA), the Linear Discriminant Analysis (LDA), Independent Component Analysis (ICA), Artificial Neural Network (ANN)

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Rajendra Kumar, Papendra Kumar, Abhishek Gupta, "Review Paper on Face Recognition Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.223-229, 2019.

Classification problem in data mining is widely used to discover the potential information hidden in the data. Clinical, microarray data or image data related to medical field consists of high dimensions which pose difficulties for biomedical researchers in acquiring and analyzing data. Three principal challenges related to high dimensional data are Volume, Velocity and Variety. Various dimensionality reduction techniques are been used to remove irrelevant features to make the task easier and efficient. Also, using dimensionality techniques result in improved classification performance of the classifiers. This paper presents a review on the supervised machine learning algorithms for classification and prediction of various diseases. It also discusses various splitting criterion to determine the best attributes. Decision Tree algorithms are easy to understand and easy to use among all the classifiers.

Classification, CART, C4.5, C5.0, Decision tree , Dimensionality Reduction, ID3

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Diksha, D. Gupta, "Review of Decision Tree Based Classification Algorithms in Medical Data," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.230-234, 2019.