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Medical data mining is a tremendously significant domain for exploration because of its importance in the expansion of innumerable applications in the medical domain. On the fact of briefing the deaths taking place globally, the heart disease seems as the foremost cause of death. The recognition of the chance of heart disease in an individual is a complex task for health specialists because it requires years of experience and intense medical tests to be conducted. In this research work, enhanced deep neural network (DNN) learning is introduced to treat patients accurately and for maintaining consistency in heart disease prediction system. So that anticipation of the loss of lives at the prior stage is possible. The results formulated ideally verify that the designed diagnostic scheme is able of calculating the risk level of heart disease efficiently when compared to other methodologies. The proposed model provides better results in heart diseases prediction compared to that of previous work. Early prediction of the disease reduces the costs and time of the treatment. The cost and time of treatment will be reduced due to the early prediction of heart disease.

Machine learning, Medical Data Mining, Heart Disease, Tensor Flow, Deep Neural Network

[1] Mai Shouman, Tim Turner and Rob Stocker, “Using data mining techniques in heart disease diagnosis and treatment”, Japan-Egypt Conference on Electronics, Communications and Computers (JEC- ECC), 2012.
[2] Pagidipati, N. J., & Gaziano, T. A., “Estimating Deaths From Cardiovascular Disease: A Review of Global Methodologies of Mortality Measurement. Circulation”, 127(6), 749–756, 2013. doi:10.1161/circulationaha.112.128413.
[3] K. Subhadra, Vikas B, "Neural Network Based Intelligent System for Predicting Heart Disease", International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-5,pp. 484-487, March 2019
[4] Zhi-Gen Hu, Jian-Ping Li, “Research And Application Of Data Warehouse And Data Mining Technology In Medical Field”, 12th International Computer Conference On Wavelet Active Media Technology And Information Processing (ICCWAMTIP), IEEE 2015.
[5] Kamran Farooq et al., “Clinical Decision Support Systems: A Visual Survey”.
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[8] Jaymin Patel et al., “Heart Disease Prediction Using Machine learning and Data Mining Technique”, volume 7, pp. 129-137, March 2016.
[9] Rairikar, A., Kulkarni, V., Sabale, V., Kale, H., & Lamgunde, A. , “ Heart disease prediction using data mining techniques”, 2017 International Conference on Intelligent Computing and Control (I2C2).
[10] Dr. B. Umadevi, “A Survey on Prediction of Heart Disease Using Data Mining Techniques”, International Journal of Science and Research (IJSR), 2015.
[11] Ambekar, S., & Phalnikar, R., “Disease Risk Prediction by Using Convolutional Neural Network”, Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 2018. doi:10.1109/iccubea.2018.8697423.
[12] Gavhane, A., Kokkula, G., Pandya, I., & Devadkar, P. K., “ Prediction of Heart Disease Using Machine Learning. 2018 Second International Conference on Electronics”, Communication and Aerospace Technology (ICECA), 2018.doi:10.1109/iceca.2018.8474922.
[13] Jabbar, M. A., & Samreen, S., “Heart disease prediction system based on hidden naïve bayes classifier”, 2016 International Conference on Circuits, Controls, Communications and Computing (I4C), 2016.doi:10.1109/cimca.2016.8053261.
[14] Ritika, Chadha, Mayank, Shubhankar, “Prediction of heart disease using data mining techniques”, Springer 2016. DOI: https://doi.org/10.1007/s40012-016-0121-0.
[15] Shaikh, S., Sawant, A., Paradkar, S., & Patil, K., “Electronic recording system-heart disease prediction system”, 2015 International Conference on Technologies for Sustainable Development (ICTSD), 2015.doi:10.1109/ictsd.2015.7095854.
[16] R. Kannan, V. Vasanthi, “Machine Learning Algorithms with ROC Curve for Predicting and Diagnosing the Heart Disease”, Springer Briefs in Forensic and Medical Bioinformatics, pp 63-72, 14 June 2018. DOI: https://doi.org/10.1007/978-981-13-0059-2_8.
[17] Sowmiya, C., & Sumitra, P., “Analytical study of heart disease diagnosis using classification techniques”, 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), 2017.doi:10.1109/itcosp.2017.8303115.
[18] M. Sultana, A. Haider, and M. S. Uddin, "Analysis of data mining techniques for heart disease prediction", 2016 3rd Int. Conf. Electr. Eng. Inf. Commun. Technol. iCEEiCT 2016.
[19] Vikas Chaurasia, Saurabh Pal, “Early Prediction of Heart Diseases Using Data Mining Techniques”, Caribbean Journal of Science and Technology, Vol. 1, pp. 208-21, December 2013.
[20] Yogita Solanki, Sanjiv Sharma, "A Survey on Risk Assessments of Heart Attack Using Data Mining Approaches", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.11, No.4, pp. 43-51, 2019. DOI: 10.5815/ijieeb.2019.04.05.
[21] Niyati I. Patel , Hiren R. Patel, “A Survey on Prediction of Disease with Data Mining”, International Journal of Computer Sciences and Engineering(IJCSE), Survey Paper Vol.-7, Issue-2, E-ISSN: 2347-2693,pp. 289-293, Feb 2019.
[22] H. Benjamin Fredrick David and S. Antony Belcy, “Heart Disease Prediction Using Data Mining Techniques”, Department of Computer Science and Engineering, Manonmaniam Sundaranar University, India, ICTACT JOURNAL ON SOFT COMPUTING, OCTOBER 2018.

Yogita Solanki, Sanjiv Sharma, "Analysis and Prediction of Heart Health using Deep Learning Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.309-315, 2019.

The process of Human Activity recognition nowadays had found a wide variety of applications in healthcare and security surveillance. The commonly used smartphones are now available with inbuilt accelerometer and gyroscope sensors. The data collected using these sensors are used for recognizing the activity performed by the person who carries the smartphone. The sensor data collected from these sensors are fed to activity classifiers to train them. In this paper, the performance of various machine learning techniques are trained and evaluated for finding the better classification technique. In particular, examines the use of Decision tree, Naive bayes, K-nearest neighbour, Support Vector Machine and Random forest. The evaluation metrics used are accuracy, sensitivity, specificity and precision. During evaluation the results showed that the SVM showed better accuracy with the smartphone data.

Activity Recognition, Smart phone, Accelerometer, Machine Learning, Support Vector Machines

[1] T.Dinh Le and C. Van Nguyen, "Human activity recognition by smartphone", 2015 2nd National Foundation for Science and Technology Development Conference on Information and Computer Science (NICS), Ho Chi Minh City, pp. 219-224, 2015.
[2] J. E. Bulbul, A. Cetin and I. A. Dogru, "Human Activity Recognition Using Smartphones", 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Ankara, pp. 1-6, 2018.
[3] A. M. Khan, Y. K. Lee and S. Y. Lee, ”Human activity Recognition via An Accelerometer Enabled Smartphone Using kernel discriminent Analysis”, 2010 5th International Conference on Future Information Technology, Busan, pp. 1-6, 2010.
[4] Jindong Wang, Yiqiang Chen, Shuji Hao, Xiaohui Peng, Lisha Hu," Deep Learning for Sensor-based Activity Recognition: A Survey", Pattern Recognition Letters, pp. 1-9, 2018.
[5] M. C. Sorkun, A. E. Danisman and O. D. Incel, "Human activity recognition with mobile phone sensors: Impact of sensors and window size", 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, pp. 1-4, 2018.
[6] K. Altun, B. Barshan, Tuncel," Comparative study on classifying human activities with miniature inertial and magnetic sensors", Pattern Recognition Letters, vol. 43, no.10, pp. 3605–3620, 2010.
[7] A. Wang, G. Chen, J. Yang, S. Zhao and C. Chang, "A Comparative Study on Human Activity Recognition Using Inertial Sensors in a Smartphone", IEEE Sensors Journal, vol. 16, no. 11, pp. 4566-4578, 2016.
[8] Meenu Shukla, Sanjiv Sharma, "Analysis of Efficient Classification Algorithm for Detection of Phishing Site", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.136-141, 2017.
[9] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. “A Public Domain Dataset for Human Activity Recognition Using Smartphones”, 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium, pp. 24-26, 2013.
[10] Gagandeep Kaur, Harmanpreet Kaur, "Ensemble based J48 and random forest based C6H6 air pollution detection", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.41-50, 2018.

Anju S.S., Kavitha K.V., "Performance Evaluation of Various Machine Learning Techniques for Human Activity Recognition using Smartphone," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.316-319, 2019.

Diabetes Mellitus is one of the chronic diseases affecting the world’s population. The development of diabetic patients is expanding step by step because of the ways of life. It is a significant issue influencing an excess of individuals today, and if it is left unchecked it can create enormous implications on the health of the population. Hence, diagnosing diabetes is extremely fundamental to spare human life from diabetes. Among the different non-invasive methods of finding, breath examination exhibits a simpler, increasingly precise and suitable technique in giving extensive clinical consideration to the illness. It is a well-known fact that Acetone focus in breath has an immediate connection with blood glucose level. The grouping of acetone levels in breath for monitoring blood glucose levels and is possible to predict its values with the use of feature extraction and classification techniques in the machine learning. The paper reviews different methodologies used to identify the presence of acetone in breath samples. Also, the various sensors technologies used in computing the acetone in breath are reviewed.

Acetone, Blood Glucose Level, Breath, Sensors

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[4] Motka, Rakesh, Viral Parmarl, Balbindra Kumar, and A. R. Verma. "Diabetes mellitus forecast using different data mining techniques." In 2013 4th International Conference on Computer and Communication Technology (ICCCT), pp. 99-103. IEEE, 2013.
[5] W. Miekisch, J. K. Schubert, and G. F. Noeldge-Schomburg, “Diagnostic potential of breath analysis focus on volatile organic compounds,” Clin. Chim. Acta, Vol. 347, No. 1, pp. 2539, 2004
[6] Yan, K., Zhang, D., Wu, D., Wei, H., & Lu, G. “Design of a breath analysis system for diabetes screening and blood glucose level prediction”. IEEE Transactions on Biomedical Engineering,Vol. 61, No.11,pp. 2787-2795, 2014.
[7] Deng, C., Zhang, J., Yu, X., Zhang, W., & Zhang, X. “Determination of acetone in human breath by gas chromatography-mass spectrometry and solid-phase microextraction with on-fiber derivatization”. Journal of Chromatography B, Vol. 810, No. 2, pp. 269-275, 2004.
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[10] Wang, C., & Surampudi, A. B. “An acetone breath analyzer using cavity ringdown spectroscopy: an initial test with human subjects under various situations.” Measurement Science and Technology, Vol.19, No.10, pp. 105604, 2008.
[11] Chuji Wang, Armstrong Mbi, and Mark Shepherd, “AStudy on Breath Acetone in Diabetic Patients Using a Cavity Ringdown Breath Analyzer: Exploring Correlations of Breath Acetone With Blood Glucose and Glycohemoglobin A1C”, IEEE Sensors Journal, vol. 10, no. 1, Jan 2010
[12] Arshak, K., Moore, E., Lyons, G. M., Harris, J., & Clifford, S. “A review of gas sensors employed in electronic nose applications”. Sensor Review, Vol.24, No. 2, pp. 181-198, 2004.
[13] Ping, W. et al. “A novel method for diabetes diagnosis based on electronic nose”, Biosensors and Bioelectronics, Vol. 12, No. 9, pp.1031–1036, 1997.
[14] Kim, I.; Rothschild, A.; Tuller, H.L. “Advances and new directions in gas-sensing devices”. Acta Mater. Vol. 61, pp. 974–1000, 2013.
[15] Ke Yan et.al, “Blood glucose prediction by breath analysis system with feature selection and model fusion”, 2014.
[16] Ke Yan and David Zhang, "A novel breath analysis system for diabetes diagnosis”, International Conference on Computerized Healthcare (ICCH), Hong Kong, 2012.
[17] Hamdi Melih Saraoglu et al, “Electronic nose system based on quartz crystal microbalance sensor for blood glucose and HbA1c levels from exhaled breath odor”, IEEE sensors journal, Vol. 13, No.11, Nov 2013.
[18] Dongmin Guo et.al, “A novel breath analysis system based on electronic olfaction”, IEEE Transactions on Biomedical Engineering, Vol. 57, No. 11, Nov 2010.
[19] Lekha, S., & Suchetha, M. “Real-time non-invasive detection and classification of diabetes using modified convolution neural network”. IEEE Journal of biomedical and health informatics, Vol.22, No.5, pp.1630-1636, 2017.
[20] Tayyab Hassan et.al, “Blood glucose level measurement from breath analysis”, International Journal of Biomedical and Biological Engineering, Vol.12, No.9, 2018.

J. Jannathul Firthous, M. Mohamed Sathik, "Blood Glucose Values Prediction Using Breath Analysis: A Literature Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.320-322, 2019.

Studies among the field communication system existing technique and proposes and by experimentation demonstrate a multiuser wavelength-division-multiplexing passive optical network (WDM-PON) system combining with orthogonal frequency division multiple (OFDM) technique. A tunable multiwavelength optical comb is intended to provide flat optical lines for helping the configuration of the multiple source-free optical network units WDM-OFDM-PON system supported normal single mode fiber (SSMF). In WDM based on fiber optical network communications using wavelength with multiplex or demultiplex may be a technology that multiplexes variety of optical carrier signals onto one fiber by victimization completely different wavelengths of optical device lightweight. this system allows bidirectional communications over one strand of fiber, also as multiplication of capability and calculate BER (Bit Error Rate) and OSNR (optical signal noise ratio) finally; a comparison of by experimentation achieved receiver sensitivities and transmission distances victimization these receivers is given. The very best spectral potency and longest transmission distance at the very best bit rate. WDM based many applications like transmission data, medical imaging data, and digital audio data and video conferencing data are information measure-intensive with the Advance in optical technology providing verdant bandwidth, it`s natural to increase the multicast construct to optical networks so as to realize increased performance. Our projected scheme (PGA) based on information load transmitted capability improve supported higher information transmitted over these channels and high data up to develop in Matlab tool and using optical interleaver the OFDM model and analysis the performance of WDM-PON system.

Wireless Network, Passive optical network (PON), source-free ONUs, optical comb, wired and wireless hybrid system. BER, OSNR, Wireless Fidelity, Binary Phase Shift Keying, QAM

[1] Jia, Xiao-Hua, Ding-Zhu Du, Xiao-Dong Hu, Man-Kei Lee, and Jun Gu. "Optimization of wavelength assignment for QoS multicast in WDM networks." IEEE Transactions on communications 49, no. 2 (2001): 341-350.
[2] Zhou, Xiang, and Jianjun Yu. "Multi-level, multi-dimensional coding for high-speed and high-spectral-efficiency optical transmission." Journal of Lightwave Technology 27, no. 16 (2009): 3641-3653.
[3] Mukherjee, Biswanath. "WDM optical communication networks: progress and challenges." IEEE Journal on Selected Areas in communications 18, no. 10 (2000): 1810-1824.
[4] Bosco, Gabriella, Vittorio Curri, Andrea Carena, Pierluigi Poggiolini, and Fabrizio Forghieri. "On the performance of Nyquist-WDM terabit superchannels based on PM-BPSK, PM-QPSK, PM-8QAM or PM-16QAM subcarriers." Journal of Lightwave Technology 29, no. 1 (2010): 53-61.
[5] Xiong, Yijun, Marc Vandenhoute, and Hakki C. Cankaya. "Control architecture in optical burst-switched WDM networks." IEEE journal on selected areas in communications18, no. 10 (2000): 1838-1851.
[6] Xue, Xuwei, Wei Ji, Kangrui Huang, Xiao Li, and Shicheng Zhang. "Tunable Multiwavelength Optical Comb Enabled WDM-OFDM-PON With Source-Free ONUs." IEEE Photonics Journal 10, no. 3 (2018): 1-8.
[7] Luo, Yuanqiu, Xiaoping Zhou, Frank Effenberger, Xuejin Yan, Guikai Peng, Yinbo Qian, and Yiran Ma. "Time-and wavelength-division multiplexed passive optical network (TWDM-PON) for next-generation PON stage 2 (NG-PON2)." Journal of lightwave technology 31, no. 4 (2012): 587-593.
[8] Tomkos, I., E. Palkopoulou, and M. Angelou. "A survey of recent developments on flexible/elastic optical networking." In 2012 14th International Conference on Transparent Optical Networks (ICTON), pp. 1-6. IEEE, 2012.
[9] Ding, Aijun, and Gee-Swee Poo. "A survey of optical multicast over WDM networks." Computer Communications 26, no. 2 (2003): 193-200.
[10] Mohit Borthakur, “A Survey of DWDM Networks, its Development and Future Scope in Telecommunication Domain”, International Journal of Innovative Research in Computer and Communication Engineering Vol. 3, Issue 8, August 2015.
[11] Anand V., Chauhan S. and Qiao C., “Sub-path protection:Anew framework for optical layer survivability and its quantitative evaluation”, Department of Computer Science and Engineering, State University of New York at Buffalo, Technical Report2002-01, 2002.
[12] Ho P. H. and Mouftah H. T., “SLSP: a new path protection scheme for the optical internet”, Proceedings of OFC’01, Anaheim, CA, Vol. 2, March 2001.
[13] Xin C., Ye Y., Dixit S. and Qiao C., “A joint lightpath routing approach in survivable optical networks”, Proceedings of the SPIE Asia-Pacific Optical and Wireless Communications, Vol. 4585, pp. 139-146, Nov.2001.
[14] Urban P. J., Huiszoon B., Roy R., M. M. de Laat, Huijskens F. M., Khoe G. D., Koonen A. M. J., and H. de Waardt, “High-bit rate Dynamically Reconfigurable WDM-TDM Access Network”, Journal of Optical Communication and Networking, Vol. 1, No. 2, pp. A143- A159, July 2009.
[15] Ramesh, G., S. Sundara Vadivelu, and Jose Anand. "A Survey on Wavelength Division Multiplexing (WDM) Networks." ICTACT Journal Of Communication Technology 01 (2010)..
[16] C. Ou, H. Zang, N.K. Singhal, K. Zhu, L.H. Sahasrabuddhe, R.A. Mc Donald, B. Mukherjee, Subpath protection for scalability and fast recovery in optical WDM mesh networks, Journal on Selected Areas in Communications (2004) 1859–1875.

Pooja Dangi, Manish Shrivastava, "Review on Enhanced WDM-OFDM-PON System Using Modulation Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.323-327, 2019.

Epilepsy has always baffled humans, in particular, the approach one needs to take for curing or at least subside its severity. Epilepsy is a continual lingering neurological ataxia generated by intermittent, transient, superfluous, wanton and unfounded seizures. Epilepsy never indicates cause of a person`s seizures or their severity. Electroencephalogram (EEG) is the tool of choice for analysis and diagnosis of epilepsy along with different automatic and visual inspection techniques. Several researchers have proposed diverse techniques for classification and analysis of epilepsy. Different pre-processing, feature extraction and classification approaches are presented. This paper attempts to catalogue various techniques and algorithms proposed so far for epileptic seizure analysis along with shortcomings thereof to facilitate further research in this complex area. This will help in online seizure detection and timely diagnosis.

Epilepsy, Seizure, Electroencephalogram (EEG), Brain, Wavelet, Hilbert-Huang Transform

[1] https://www.cureepilepsy.org/what-is-epilepsy
[2] http://www.who.int/mediacentre/factsheets/fs999/en
[3] http://www.epilepsy.com/learn/epilepsy-101/what-epilepsy
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[12] L. Murali, D. Chitra, T. Manigandan and B. Sharanya, "An Efficient Adaptive Filter architecture for Improving the Seizure Detection in EEG Signal", Circuits, Systems, and Signal Processing, Vol. 35, Issue 8, August 2016, pp. 2914–2931. Doi: 10.1007/s00034-015-0178-2
[13] M.J. van der Heyden et al.," Non-linear analysis of intracranial human EEG in temporal lobe epilepsy", Clinical Neurophysiology, Vol. 110, 1999, pp. 1726-1740.
[14] Leonardo Duque-Munoz, Jairo Jose Espinosa-Oviedo and Cesar German Castellanos-Dominguez, "Identification and monitoring of brain activity based on stochastic relevance analysis of short–time EEG rhythms", BioMedical Engineering OnLine, 13:123, 2014. Doi: 10.1186/1475-925X-13-123
[15] T. Baranidharan, and D. K. Ghosh. "Classification of medical images using fast Hilbert transform and decision tree algorithms." International Journal on Computer Science and Engineering 3.4 (2011): 1497-1500.
[16] Piotr J. Franaszczuk, Gregory K. Bergey, Piotr J. Durka and Howard M. Eisenberg, "Time-frequency analysis using the matching pursuit algorithm applied to seizures originating from the mesial temporal lobe", Electroencephalography and Clinical Neurophysiology, 106(6), pp. 513-521. Doi: 10.1016/S0013-4694(98)00024-8
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[19] Shiliang Sun and Changshui Zhang, "Adaptive feature extraction for EEG signal classification", Medical & Biological Engineering & Computing, 44(10), 2006, pp. 931-935. Doi: 10.1007/s11517-006-0107-4
[20] Nihal Fatma Guler, Elif Derya Ubeyli and Inan Guler, "Recurrent neural networks employing Lyapunov exponents for EEG signals classification", Expert Systems with Applications, Vol. 29, 2005, pp. 506–514. Doi: 10.1016/j.eswa.2005.04.011
[21] Abhishek Kumar, Rajneesh Sharma, "Fuzzy Lyapunov Reinforcement Learning for Non-linear Systems", ISA Transactions, Vol. 67, Mar. 2017, pp.151-159. Doi:10.1016/j.isatra.2017.01.026
[22] Alan WL Chiu et al., "Wavelet-based Gaussian-mixture hidden Markov model for the detection of multistage seizure dynamics: A proof-of-concept study", Biomedical Engineering Online, Vol. 10, No. 29, 2011.Doi: 10.1186/1475-925X-10-29
[23] L. Murali, D. Chitra, T. Manigandan and B. Sharanya, "An Efficient Adaptive Filter Architecture for Improving the Seizure Detection in EEG Signal", Circuits, Systems, and Signal Processing, Vol. 35, Issue 8, Aug. 2016, pp. 2914–2931. Doi: 10.1007/s00034-015-0178-2
[24] Abdulhamit Subasi and M. Ismail Gursoy, "EEG signal classification using PCA, ICA, LDA and support vector machines", Expert Systems with Applications 37 (2010) 8659–8666. Doi: 10.1016/j.eswa.2010.06.065
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Amit Kukker, Rajneesh Sharma, "EEG Based Epilepsy Seizure Analysis and Classification Methods: An Overview," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.328-346, 2019.

To keep pace with increased applications of recommender systems, collaborative filtering algorithms have played a major role in providing better and accurate recommendations to the users. Their performance in providing the top results, that actually help the users, has also improved over the previous years. Collaborative Filtering (CF) algorithms are used in the social media sites as well as in the personalized recommender systems for the users and deal with problems like cold start, data sparsity, information overload, synonymy etc. Here, the recommendation is based on the preferences of user`s friends or the user`s own past preferences. This paper gives a detailed review of the algorithms used by various recommender system that are based on collaborative filtering. It investigates the algorithms based on their input parameters, their performance and various other factors of importance.

Collaborative Filtering, Social Media, Folksonomy, Personalized Ranking, Data Sparsity, Tagging, User Similarity

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Prachi Dahiya, Neelam Duhan, "Comparative Analysis of Various Collaborative Filtering Algorithms," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.347-351, 2019.

From the few years’ people of cities has persevered to grow at a fast way. The most important purpose of occurrence in increasing international locations like Morocco is the agricultural migration. In truth, rural children are more and more attracted by the modern-day manner of lifestyles and the opportunity of employ presented with the aid of cities. This boom in populace density has a massive wide variety of terrible outcomes on the nice of life in the metropolis. To solve the parking issue, here presents the area and development of a clever parking gadget the use of the modern-day technology. Our device makes use of an adaptable and hybrid self-corporation set of rules for wi-fi sensors that adapts to all of car parkings present within the city, and offers a higher control of the power utilization during the wi-fi verbal exchange to growth the life of the sensor nodes.

Approaches of parking, RFID

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[21]. TS: technology used by sensor network; TD: technology used by the drivers; GD: guidance; PY: payment; SE: security; RE: reservation; IG: smart gateway; PS: parking management using Smartphone; AV: availability checking over internet; IOT: Internet of Things; WA: web application; SM: Smartphone;

V. Manideep Goud, B. Srinivas Rao, "Smart Car Parking System based on RFID," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.352-355, 2019.

World Wide Web is the largest source of information and huge information is available on the net. It is the growing tendency in users to express their opinions or thoughts using public opinion sites. Analysing all these opinions manually becomes challenging task so if we can develop the automated system to analyse what people want to say about any product, political party or any other thing it would be of great help. In this work we are trying to make readers life easier by providing the polarity of the reviews from user in automated way with better accuracy. The hybrid model is built using XGBoost and Logistic Regression classifiers and the performance of the hybrid model is compared to both the static models. As per expectation the hybrid model is performing better.

XGBoost, Logistic Regression, Hybrid Model, Sentiment Analysis, Opinion Mining

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Ashwini M Joshi, Sameer Prabhune, "Twitter Sentiment Analysis using XGBoost and Logistic Regression: A Hybrid Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.356-360, 2019.

As going deep under the water nothing can be seen properly as well as it is difficult to identify any substance residing or present under water. This survey basically focuses on the detection of the underwater image which are taken through various self-ruling submerged vehicles and remotely controlled vehicles, in order to improve the quality of the pictures. The factors include the low contrast, blur, non-uniform lighting and faded colors. This paper analyzed an image enhancement technique along with the image restoration technique that will help to acquire images that are of better quality. The algorithms applied on the degraded images comprises of two domains- Spatial Domain Methods, Frequency Domain Methods. The literature reviews used in this paper explained that the preprocessing algorithms used by various authors uses a standard filter techniques contain different combinations. The survey includes analysis in terms of qualitative and quantitative factors on hundreds of underwater images. The images taken in offshore water characterized by a heavy concentration of colored dissolved organic matter and total suspended matter, thus various methods have been applied on the images in a proper way so as to obtain a fresh image.

Offshore, underwater image restoration, under water imaging, underwater optical model

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[15] Yang, Miao, and Arcot Sowmya. A submerged shading picture quality evaluation metric. IEEE Transactions on Image Processing 24, no. 12 (2015): 6062-6071.
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Martina Martin, Nischol Mishra, "Object Detection and Filtering Techniques of Underwater Images : A Review," International Journal of Computer Sciences and Engineering, Vol.7, Issue.8, pp.361-365, 2019.