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The main Motive of Internet of Things is to segregate everything in our world under a common controlled environment that will give us control of day to day life things, as well as providing us status updates of the things. In Accordance with this, Effective implementation of Internet of Things is completely depends upon Technologies and protocols incorporated to communicate between electronic devices and its controlling circuitry. This paper Introduces about basic standards and parameters of the various Communication technologies inculcated in Internet of Things. In this paper, we illustrated the survey of key technologies involved in the implementation of Internet of Things along with the wide application area where the Internet of Things will have major role. We also discussed about detailed comparison of different parameters of various wireless Communication technologies.

Internet of Things, Communication Technologies, Comparison

[1] Jasper Tan ; Simon G.M. Koo ,” A Survey of Technologies in Internet of Things” 2014 IEEE International Conference on Distributed Computing in Sensor Systems
[2] Sajjad Hussain Shah ; Ilyas Yaqoob,” A survey: Internet of Things (IOT) technologies, applications and challenges”, 2016 IEEE Smart Energy Grid Engineering (SEGE)
[3] Shayan Nalbandian,” A survey on Internet of Things: Applications and challenges”, 2015 International Congress on Technology, Communication and Knowledge (ICTCK)
[4] Ala Al-Fuqaha ; Mohsen Guizani ; Mehdi Mohammadi ; Mohammed Aledhari ; Moussa Ayyash,” Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications”, IEEE Communications Surveys & Tutorials ( Volume: 17, Issue: 4, Fourthquarter 2015 )
[5] R. Porkodi ; V. Bhuvaneswari,” The Internet of Things (IoT) Applications and Communication Enabling Technology Standards: An Overview”, 2014 International Conference on Intelligent Computing Applications
[6] Somayya Madakam, R. Ramaswamy, Siddharth Tripathi,” Internet of Things (IoT): A Literature Review”, Journal of Computer and Communications, 2015, 3, 164-173
[7] Luigi Atzori , Antonio Iera , Giacomo Morabito,” The Internet of Things: A survey” Elsevier Computer networks journal, Computer Networks 54 (2010) 2787–2805

M. Avatade, M. Lal, A. Jain, "A Survey of Wireless Communication Technologies in Internet of Things," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.823-826, 2018.

Electrical energy is used throughout the globe to power devices, appliances, and strategies of transportation used in everyday life. The uses of electricity are Residential uses, industrial uses, and Transportation. This paper targeted on Forecasting Energy Consumption of a House using historical information. The proposed work uses the Radial Basis Function (RBF) Network for forecasting the demand for energy consumption of a house using historical data. The result showed that the Radial Basis Function Network performs better than FeedForward BackPropagation Network (FFBPN), and Elman BackPropagation Network (EBPN) were compared with Mean Square Error (MSE) and accuracy values.

Radial Basis Function, Neural Network, FeedForward BackPropagation Network, Elman BackPropagation Network, RBF

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[3] Mohanad S. Al-Musaylh, Ravinesh C. Deo, Jan F. Adamowski, Yan Li, “Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia”, Advanced Engineering Informatics, Volume 35, January 2018, Pages 1-16.
[4] Changhao Xia, Jian Wang, Karen McMenemy, “Short, medium and long term load forecasting model and virtual load forecasterbased on radial basis function neural networks”, International Journal of Electrical Power & Energy Systems, Volume 32, Issue 7, September 2010, Pages 743-750.
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[6] Juan Vilar,Germán Aneiros, Paula Rana, “ Prediction intervals for Electricity demand and price using functional data”, International Journal of Electrical Power & Energy Systems, Volume 96, March 2018, Pages 457-472.
[7] Sukumar Mishra, Vivek Kumar Singh, “ Monthly Energy Consumption Forecasting Based on Windowed Momentum Neural Network”, IFAC- PapersOnline, Volume 48, Issue 2015.
[8] Pedro M. Ferreira, António E. Ruano, Rui Pestana, Laszlo T. Koczy, “Evolving RBF Predictive Models to Forecast the Portuguese Electricity Consumption”, IFAC Proceedings Volumes, Volume 42, Issue 19, 2009, Pages 414-419.
[9] Antonino Marvuglia, Antonio Messineo, “Using Recurrent Artificial Neural Networks to Forecast Household Electricity Consumption”, Energy Procedia, Volume 14,2012, Pages 45-55.
[10] Aowabin Rahman, Vivek Srikumar, Amanda D. Smith, “Predicting Electricity consumption for commercial and residential buildings using deep recurrent neural networks”, Applied Energy, Volume 212, 15 February 2018, Pages 372-385.
[11] A. S. Ahmad, M. Y. Hassan, M. P. Abdullah, H. A. Rahman, R. Saidur, “A review on applications of ANN and SVM for building electrical energy consumption forecasting”, Renewable and Sustainable Energy Reviews, Volume 33, May 2014,Pages 102-109
[12] Zuzana Majdisova, Vaclav Skala, “Radial Basis Function approximations: comparison and applications”, Applied Mathematical Modelling,Volume 51, November 2017, Pages 728-743
[13] L. G. B. Ruiz, R. Rueda, M. P. Cuéllar, M. C. Pegalajar, “Energy consumption forecasting based on Elman neural networks with evolutive optimization”, Expert Systems with Applications, Volume 92, February 2018, Pages 380-389.
[14] Michal Smolik, Vaclav Skala, Zuzana Majdisova, “Vector field radial basis function approximation”, Advances in Engineering Software,Volume 123, September 2018, Pages 117-129.
[15] Radiša Z. Jovanović, Aleksandra A. Sretenovic, Branislav D. Zivkovic, “Ensemble of various neural networks for prediction of heating energy consumption”, Energy and Buildings, Volume 94, 1 May 2015, Pages 189-199.
[16] Kunlong Chen, Jiuchun Jiang, Fangdan Zheng, Kunjin Chen, “A novel data-driven approach for residential electricity consumption prediction based on ensemble learning”, Energy, Volume 150,1 May 2018, Pages 49-60.
[17] Jihui Yuan, Craig Farnham, Chikako Azuma, Kazuo Emura, “Predictive artificial neural network models to forecast the seasonal hourly electricity consumption for a University Campus”, Sustainable Cities and Society, Volume 42, October 2018, Pages 82-92.
[18] V.O. Oladokun, A.T. Adebanjo, and O.E. Charles-Owaba, “Predicting students’ academic performance using artificial neural network:A case study of an engineering course,” Pacific Journal of Science and Technology, vol. 9, iss. 1, pp. 72-79, 2008.
[19] K. P. Amber, R. Ahmad, M. W. Aslam, A. Kousar, M. S. Khan, “Intelligent techniques for forecasting electricity consumption of buildings”, Energy, Volume 157, 15 August 2018, Pages 886-893.
[20] O.L. Usman, O.B. Alaba, “Predicting Electricity Consumption using Radial Basis Function Network”, International Journal of Computer Science and Artificial Intelligence, June 2014, Vol.4 Iss.2,PP.54-62.

N. Saranya, B.S.E. Zoraida, "Forecasting Energy Consumption of a House using Radial Basis Function Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.827-830, 2018.

Age Estimation is predicting a person’s age and is a very important attribute used for identity authentication. One of the major factors affecting the age estimation result is the identification of features of a person’s face accurately. Age Estimation has several real-world applications, equivalent to security management, biometrics, client relationship management, recreation, and cosmetology. The foremost ordinarily used age estimation technique is regression based mostly as a result of it takes into consideration the interrelationship among the age values for face pictures. The current work is an overview of techniques employed previously for age estimation.

Age Estimation, Forensics, Age based retrieval, Security, Surveillance, Label based learning, Label Distribution based learning

[1] Z.Hu and Y.Wen, Facial Age Estimation with Age Difference, IEEE TRANSACTIONS ON IMAGE PROCESSING,2016
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[3] V.G. Khetade and S. B. Thakare, An Efficient Method for Human Age Estimation by Label Distribution Learning,2014
[4] Karthikeyan D and Balakrishnan G, A comprehensive age estimation on face images using hybrid filter based feature extraction., Biomedical Research 2018.
[5] A.Sharma and S.Chhabra, A Hybrid Feature Extraction Technique for Face Recognition, International Journal of Advanced Research in Computer Science and Software Engineering,2017.
[6] Geng X, Yin C & Zhou ZH.” Facial age estimation by learning from label distributions.”, IEEE Trans Pattern Anal Mach Intell. 2013 Oct;35(10):2401-12. doi: 10.1109/TPAMI.2013.51.
[7] S.Soman and A.Austine, A Survey on Age Estimation Techniques, International Journal of Computer Applications ,2017.
[8] Shara M. S. and Shemitha P. A. A Survey on Facial Age Estimation Based on Multiple CNN , International Journal for Scientific Research & Development,2017.
[9] S.Soman & A.Austine,” A Survey on Age Estimation Techniques”, International Journal of Computer Applications, Volume 161 - Number 4,2017
[10] Xin Geng, Label Distribution Learning, IEEE TRANSACTIONS,2014
[11] W.Shen, K.Zhao, Y.Guo,and A.Yuille, Label Distribution Learning Forests, 31st Conference on Neural Information Processing Systems (NIPS 2017).
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[19] X. Geng, K. Smith-Miles, and Z. Zhou. Facial age estimation by learning from label distributions. In Proc. AAAI, 2010.
[20] X. Geng, C. Yin, and Z. Zhou. Facial age estimation by learning from label distributions. IEEE Trans. Pattern Anal. Mach. Intell., 35(10):2401–2412, 2013.
[21] X. Yang, X. Geng, and D. Zhou. Sparsity conditional energy label distribution learning for age estimation. In Proc. IJCAI, pages 2259–2265, 2016.
[22] X. Geng. Label distribution learning. IEEE Trans. Knowl. Data Eng., 28(7):1734–1748, 2016.
[23] X. Geng and P. Hou. Pre-release prediction of crowd opinion on movies by label distribution learning. In Pro. IJCAI, pages 3511–3517, 2015.
[24] C. Xing, X. Geng, and H. Xue. Logistic boosting regression for label distribution learning. In Proc. CVPR, pages 4489–4497, 2016.
[25] R.K.Shukla, A.Agarwal & A.K.Malviya,” An Introduction of Face Recognition and Face Detection for Blurred and Noisy Images”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.39-43 , June (2018).
[26] A. S.Banu, P.Vasuki, S. M.M.Roomi & A. Y.Khan,” SAR Image Classification by Wavelet Transform and Euclidean Distance with Shanon Index Measurement”, Journal (IJSRNSC) Vol.6 , Issue.3 , pp.13-17, Jun-2018.

Vishnu Prasad Verma, Dipti Verma, "A Survey on Facial Age Estimation Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.831-834, 2018.

Technology brings a rapid growth of data. It becomes a challenge to process data with previous methods and applications. The unstructured, scalable, complex data is available through distinct sources like social sites, news, applications and so on. Hadoop is a most demanding development tool for big data because it processes the data in the structured and unstructured form. When you think about Hadoop usually it comes into mind regarding Linux. It is not mandatory for all to learn Linux first and then install Hadoop on a system and then use it. There is an advantage of Linux that it provides basic production and development platform for Hadoop. This paper reflects the light on the variety of platforms, open source systems available for Hadoop installation. Now Hadoop is not only limited to the Linux platform, you can put it on Windows too. Windows is user-friendly and you can install Hadoop without Linux on another hand there are enormous resources by which you can easily go into the Linux environment and place Hadoop. Java is a prerequisite of Hadoop.

Powershell, Hyper-V, Dual Booting, Virtual Machine, Cygwin

[1] https://www.cio.com/article/3195905/windows/18-things-you-should-know-about-using-linux-tools-in-windows-10.html
[2] Shantanu Sharma “Installing Hadoop-2.6.x on Windows 10” University of California, Irvin.
[3] “Introduction to Hyper-V on Windows 10” Microsoft Documents, United States Feb 05,2016. https://docs.microsoft.com/en-us/virtualization/hyper-v-on-windowsv
[4] S. Prasad “Hadoop Installation” https://www.it.iitb.ac.in/frg /wki/images/a/af/03D05011_Sandeep_Prasad_Group_7_Week_3_Report_1_Hadoop_Installation.pdf
[5] N.S. Hoe, C. Charles “User Guide to using the Linux Desktop”, UNDP-APDIP, Kuala Lumpur, Malaysia,2004
[6] Y. Bassil,” Windows and Linux Operating System from a Security Perspective”, Journal of Global Research in Computer Science.
[7] N.Economides, E.Katsamakas,”Linux vs. Windows: A comparison of Applications and platform Innovation Incentives for Open Source and Proprietary Software Platform ”Elsevier pp.207-218, 2006.
[8] K. Parimala G. Rajkumar , A. Ruba , S. Vijayalakshmi, , “Challenges and Opportunities with Big Data ”, International Journal of Scientific Research in Computer Science and Engineering. pp:16-20, 2017
[9] A.Tanuja, D. Swetha Swatha, “Processing and Analyzing Big Data using Hadoop” International Journal off Computer Science & Engineering, pp: 91-94, April,2016.

A.K. Chela, H.S. Sidhu, "Installation of Hadoop on Windows," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.835-839, 2018.

The aim of the research paper is to study about the portrayal of women in cinema. Also, it aimed to analyse the changing mind set of the young budding directors from the cinema industry. The emerging directors of our current film industry are very well aware of the portrayal of women which makes them to form a well-scripted for their film story. In this research, these young directors of cinema industry with a feministic approach in their movies are interviewed and analysed for the research. The social issues faced by women in both psychological and physical aspect is been brought to limelight and bravely disclosed in their films. The main films which are been discussed for this research are IRAIVI, THARAMANI and ARUVI which is been released in the year of 2016, 2017 and 2018 consecutively. These directors are been categorised under neo wave film making directors of cinema industry where they portray a favourable opinion regarding the strong characterisation of women and their struggle against gender bias. Henceforth these types of films had created a reflection of rational thoughts among the viewers and audiences to understand the present scenario of social issues against women prevailing in the authoritarian society. This research tries to provide awareness for the future budding directors about the importance of social touch in a movie in connection with the entertainment in the whole form as infotainment.

Neo-Wave Films, Feminist thoughts, Gender Bias, catharsis effect, Infotainment, Tamil film industry, Script

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[2] Geoffrey nowell-smith,“The story of film”. pavilion new edition November 1, 2013.
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[5] Giannetti, Louis D., “Understanding Movies, Upper Saddle River”, N.J.: Pearson/Prentice Hall, 11th Edition 2008.
[6] Joseph M. Boggs and Dennis W. Petrie, “The Art Of Watching Films”, New York: McGraw-Hill, 7th Edition 2008.
[7]Alexandra, “Film Studies: Women in Contemporary World Cinema”Framing Film), Peter Lang International Academic Publishers, 2002
[8]Lalitha Gopalan, “Cinema of Interruptions: Action Genres in contemporary Indian Cinema”, British Film Institute , 2002.
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Anthony Kimton Prabhu, "A Case Study on Neo-Wave Cinema Based on Feminism," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.840-842, 2018.

Association-rule mining is commonly used mining technique for finding frequent patterns. In real world applications traditional association-rule mining is not appropriate since the purchased item have different factors, for example, amount and benefit. High utility pattern mining was used for unravelling the limitations of the association-rule mining as far as amount and benefit. There are algorithms for finding high utility patterns from static databases. Some researches worked for handling dynamic dataset but huge computational time and multiple database scan was required. In this paper, a system is proposed for finding high utility patterns which uses list based data structure for dynamic dataset. To improve computational time and memory, constraints like item, date or length are used. A few experiments are led to demonstrate the execution of the proposed system with and without using constraints regarding time and memory.

Association rules (ARs); Association rule mining; frequent patterns; high utility patterns; pattern mining

[1] M. Liu , J.-F. Qu , “Mining high utility itemsets without candidate generation”, in: International Conference on Information and Knowledge Management (CIKM 2012), 2012, pp. 55–64.
[2] J.C.-W. Lin , T. Li , P. Fournier-Viger , T.-P. Hong , J. Zhan , M. Voznak , “An efficient algorithm to mine high average-utility itemsets”, Adv. Eng. Inform. 30 (2) (2016) 233–243.
[3] J. Liu , K. Wang , B.C.M. Fung , “Mining high utility patterns in one phase without generating candidates”, IEEE Trans. Knowl. Data Eng. 28 (5) (2016) 1245–1257 .
[4] J. Liu , K. Wang , B.C.M. Fung , “Direct discovery of high utility itemsets without candidate generation”, in: Proceedings of the 2012 IEEE International Conference on Data Mining (ICDM 2012), 2012, pp. 984–989.
[5] L. Troiano , G. Scibelli , “Mining frequent itemsets in data streams within a time horizon”, Data Knowl. Eng. 89 (2014) 21–37.
[6] C.F. Ahmed , S.K. Tanbeer , B.-S. Jeong , H.-J. Choi , “Interactive mining of high utility patterns over data streams”, Expert Syst. Appl. 39 (15) (2012) 11979–11991.
[7] V.S. Tseng , C.-W. Wu , P. Fournier-Viger , P.S. Yu , “Efficient algorithms for mining the concise and lossless representation of high utility itemsets”, IEEE Trans. Knowl. Data Eng. 27 (3) (2015) 726–739.
[8] J. Sahoo , A.K. Das , A. Goswami , “An efficient approach for mining association rules from high utility itemsets”, Expert Syst. Appl. 42 (13) (2015) 5754–5778.
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[12] U. Yun , D. Kim , H. Ryang , G. Lee , K.-M. Lee , “Mining recent high average utility patterns based on sliding window from stream data”, J. Intell. Fuzzy Syst. 30 (6) (2016) 3605–3617.
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A. G. Deshpande, R. J. Deshmukh, "Discovery of High Utility Patterns from Retail Database with adding constraints on LBHUP algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.843-846, 2018.

When videos are captured under the influence of atmospheric or climatic conditions, the overall quality of the image gets effected with fog and haze. This work focuses on haze removal which is also called as the visibility restoration which can infer to the various procedures employed for removing or reducing degradation from the image. Degradation in image can be due to various reasons such as miss-focus, relative atmospheric conditions and object-camera motion. This paper presents a method in which the fog in video can be removed by using dark channel method followed by Guided filtering. Guided filter has better filtering capability, fast running time and gives a clear haze free video. So, we can say that this method can be employed in real-life settings or video applications. Restoration of hazy videos is important in computer graphics, remote sensing and outdoor surveillance.

Guided filter, dark channel method, Transmission map, Video Restoration

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[4]. R. Fattal, “Single Image Dehazing,” SIGGRAPH, 2008.
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[7]. Schechner, Yoav Y., Srinivasa G. Narasimhan and Shree K. Nayar, "Instant dehazing of images using polarization", The Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition ( CVPR), vol. 1, pp. I-325, 2001.
[8]. Shwartz, Sarit, Einav Namer and Yoav Y. Schechner, "Blind haze separation", IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1984-1991, 2006.
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Mounika Kanala, Shaik Taj Mahaboob, "Foggy Video Restoration Using Guided Filter," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.847-852, 2018.

Psychological stress is a biggest threat to human’s health. Hence, it is vital to detect and manage stress before it turns into severe problem. However, conventional stress detection strategies rely on psychological scales and physiological devices, which require active individual participation making it labor-consuming and expensive. With the rapid evolution of social media networks, people are willing to sharetheir everyday events and moods via social media platforms, making it practicable to leverage this online social media content for stress detection as these data timely reflect user’s real-life emotional state. To automatically predict stress, we have defined a set of stress-related textual ‘F = {f1, f2, f3, f4}’, visual ‘vF = {vf1, vf2}’, and social ‘sf’ features, and thenproposed a hybrid model Psychological Stress Detection (PSD) - a Probabilistic Naïve Bayes Classifier combined with Visual (Hue, Saturation, Value) and Social modules,to leverage text, image and social interaction information for stress detection from social media contentExperimental results show that the proposed PSD model improves the detection performance,when compared to TensiStrength and Teenchat frameworkPSD achieves 95% of Precision rate. PSD model would be useful in developing stress detection tools for mental health agencies and individuals.

Psychological Stress Detection; Social Media interaction; Health agencies; Physiological Signals

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Shaikha Hajera, Mohammed Mahmood Ali, "Psychological Stress Detection from Social Media Data using a Novel Hybrid Model," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.853-862, 2018.

Machine Translation (MT) is one of the tasks of Natural Language Processing. It can be used by intellectuals to attain information from the documents written in different languages. In the following paper, we have discussed problems faced in MT in Indian languages, various approaches of MT, limitations of some of the current existing MT Systems and the work that has been done till now in MT in Indian language perspective. We have also discussed performance metrics that are used for evaluation of MT System.

Machine Translation, Rule Based Approach, Example Based Approach

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Deepti Chopra, Nisheeth Joshi, Iti Mathur, "Machine Translation In Indian Languages," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.863-868, 2018.

In every field of technology there are major issues of security. Biometric images are used as input and then the features like edge, texture are extracted. This is done by applying feature extraction algorithms and data encryption standard cryptographic algorithm on Fingerprints, Iris, face and palm simultaneously. In this paper we have explained the information about multimodal biometrics, DES algorithm, and role of DES in multimodal. We have discussed the implementation of DES algorithm by using MATLAB on parameters of captured images according to age and gender. Parameters such as key size, input size, time taken, simulation, memory requirement, CPU usage. Matching algorithm, time delay, FAR, FRR is also a major issue in multimodal biometrics.

Cryptographic algorithm; Biometric traits; FAR, FRR; Data Encryption Standard; Cipher Text

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Sharmila S.More, Bhawna Narain, B.T.Jadhav, "Data Encryption Standard Algorithm in Multimodal Biometric Image," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.869-874, 2018.