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On 8th August 2018, Kerala had a very heavy rainfall, resulting filling of dams caused flood situation in Kerala. Many people started posting twits about this and people living in that area were alerted. Administration department started their rescue operations. Here social media played key role in locating people and providing help to them. A lot of campaigns were started to collect financial aid to the affected people. Here we again felt power of social media that can positively impact the society. Twitter, a popular micro blogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. It is also extremely popular because the information gets spread more widely and rapidly. It’s important to detect anomalous events which are trending on the social media and be able to monitor their evolution and find related events. This paper talks about how to detect the anomalies in tweets.

Anomaly, Types of Anomalies, Machine Learning, Text Stream, Twitter Data, Social media analysis

[1] Weiren Yu, Member, IEEE, Jianxin LiMd Zakirul Alam Bhuiyan
Richong Zhang and Jinpeng Huai ,"Ring: Real-Time Emerging Anomaly Monitoring System over Text Streams" , IEEE Big Data 2017. 2017.2672672
[2] Chao wang, Zhen Liu , Hui Gao and Yan Fu , "Applying Anomaly Pattern Score for Outlier Detection" , IEEE 2019. 2895094
[3] Varun Chandola, Arindam Banrjee and Vipin Kumar -- "Anomaly Detection : A Survey", in ACM Computing Surveys, September 2009
[4] Jagruti D. Parmar and Prof. Jalpa T. Patel ,"Anomaly Detection in Data Mining: A Review ", International Journal of Advanced Research in Computer Science and Software Engineering ISSN: 2277 128X, April 2017
[5] Salima Omar , Asri Ngadi and Hamid H. Jebur, "Machine Learning Techniques for Anomaly Detection: An Overview ",International Journal of Computer Applications (0975 – 8887) October 2013
[6] Pranali Ratnaparkhi, Rohini Jadhaw,Prakash Kshirsagar, "Social Monitoring System for Dynamically Evolving Anomolies Over Text Stream", vol.8, issue-4, April 2018 (references)

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Shirishkumar Bari, Abhijit Patankar, "Anomaly Detection In Practice Using Python," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.241-246, 2019.

MapReduce have been acquainted with facilitate the errand of growing huge data projects and applications. This implies conveyed occupations aren’t locally composable and recyclable for resulting improvement. Additionally, it likewise hampers the capacity for applying improvements on the data stream of employment arrangements and pipelines. The Hierarchically Distributed Data Matrix (HDM) which be practical, specifically data portrayal for composing composable huge data applications. Alongside HDM, a runtime system is given to help the execution, coordination and the executives of HDM applications on distributed foundations. In light of the utilitarian data reliance diagram of HDM, numerous advancements are connected to enhance the execution of executing HDM employments. The exploratory outcomes demonstrate that our enhancements can accomplish upgrades between 10% to 30% of the Job-Completion-Time and grouping time for various kinds of uses when looked at. In this record, we address the logically Distributed Data Matrix (HDM) which is a reasonable explicitly surenesses appear for creating Composable epic facts application. Nearby HDM, a runtime structure is given to enable the execution, to blend and organization of HDM applications on coursed establishments. In perspective of the conscious data dependence chart of HDM, a few upgrades are realized to improve the execution of executing HDM livelihoods. The preliminary effects demonstrate that our upgrades can get updates among 10% to 40% of Job-Completion-Time for one of kind sorts of tasks while in examination with the bleeding edge country of compelling artwork. Programming reflection is the centre of our system, along these lines, we initially present our Hierarchically Distributed Data Matrix (HDM) which is an utilitarian, specifically meta-data deliberation for composing data-parallel projects.

Distributed systems, parallel programming, functional programming, system architecture

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G. Sirichandana Reddy, CH. Mallikarjuna Rao, "An Efficient Scheme of Big Data Processing by Hierarchically Distributed Data Matrix," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.247-251, 2019.

Searching is the process of finding a given value position in a list of values. It is fundamental operation in computer science. It is achieved by different searching methods which require number of iterations to reach at desired piece of data. In this research paper, another octonary (o) search algorithm is developed to compare & make searching even faster than algorithms like sequential, binary, ternary, & quaternary search.

Search Algorithm, Searching

[1] D Samanta, Classic Data Structure(2nd Edition), PHI Learning Private Limited ISBN - 10: 9788120337312 PP. 722-724
[2] Taranjit Khokhar, September 2016, IJIRT, Volume-3, Issue-4, PP.143908. ISSN: 2349-6002.

Bhavesh R Maheshwari, "Octonary (O) Search Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.252-256, 2019.

The widespread use of wireless sensor devices and their advancements in terms of size, deployment cost, measurement of environmental events and user friendly interface have given rise to many applications of wireless sensor networks (WSNs). WSNs are usually characterized as self-organizing networks which can be deployed without requiring any specific infrastructure in harsh and/or hostile area. In this paper are studied of different types of clustering protocol favors higher period in lieu of overall network lifetime. In cluster, a predetermined number of CHs are selected in deterministic fashion on the basis of residual energy of nodes. The focus is to balance the load among nodes and provide full network coverage.

Wireless Sensor Network, Cluster Head, Routing Protocol

[1] Nashreen Nesa and Indrajit Banerjee, “Sensor Rank: An Energy Efficient Sensor Activation Algorithm for Sensor Data Fusion in Wireless Networks”, IEEE Internet of Things Journal, IEEE 2019.
[2] Segun O. Olatinwo and Trudi-H. Joubert, “Energy Efficient Solutions in Wireless Sensor Systems for Water Quality Monitoring: A Review”, IEEE Sensors Journal, Vol. 19, Issue 5, pp no. 1596-1625, IEEE 2019.
[3] Xiuwen Fu ; Giancarlo Fortino ; Wenfeng Li, “Environment-Cognitive Multipath Routing Protocol in Wireless Sensor Networks”, IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE 2018.
[4] Nikumani Choudhury, Rakesh Matam, Mithun Mukherjee and Jaime Lloret, “A Non-Threshold –base Cluster Head Rotation Scheme for IEEE 802.15.4 Cluster -Tree Network”, Global Communications Conference (GLOBECOM), IEEE 2018.
[5] Da-Ren Chen, Ming-Yang Hsu, Hao-Yen Chang, “Context-Aware and Energy Efficient Protocol for the Distributed Wireless Sensor Network”, 28th International Telecommunication Network and Applications Conference (ITNAC), IEEE 2018.
[6] Mandeep Dhami, Vishal Garg and Navdeep Singh Randhawa, “Enhanced Lifetime with Less Energy Consumption in WSN using Genetic Algorithm based Approach”, IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), IEEE 2017.
[7] Akshay Verma, Mamta Khosla, Tarique Rashid and Arvind Kumar, “Grid and Fuzzy based Stable Energy Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Network”, 14th IEEE India Council International Conference (INDICON), IEEE 2017.
[8] Uday Kumar Rai ; Kanika Sharma, “Maximum Likelihood Estimation based Clustering Algorithm on Wireless Sensor Network-A Review”, International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), IEEE 2017.
[9] M. Devika ; S. Maflin Shaby, “Efficient route block avoiding algorithm in cluster based routing method for Wireless Sensor Network”, International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), IEEE 2017.
[10] N.G. Palan, B.V. Barbadekar and Suahs Patil, “Low Energy Adaptive Clustering Hierarchy (LEACH) Protocol: A Retrospective Analysis”, International Conference on Inventive Systems and Control, ICISC-2017.
[11] Hongjun Wang, Huiqing Chang Hui Zhao and Youjun Yue, “Research on LEACH Algorithm Based on Double Cluster Head Cluster Clustering and Data Fusion”, International conference of IEEE 2017.
[12] Anjali Bharti, Chandni Devi and Dr. Vinay Bhatia, “Enhanced Energy Efficient LEACH (EEE- LEACH) Algorithm using MIMO for Wireless Sensor Network”, International Conference on Computational Intelligence and Computing Research, IEEE 2015.

Diba Imam, Rajdeep Shrivastava, "Review Paper on Wireless Sensor Networks using Different Types of Approaches," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.257-261, 2019.

The proposed method breaks the color image into its individual color component and then fuzzy filter based canny Edge detection technique is applied. This technique depends on the fuzzy rule-based system using 2 X 2 window mask which is used to modify membership value of the image in different fuzzy sets (which means it will smoothen the image), and this filtered image is given as input to canny edge detection technique and finally after this morphological processing is used. The Performance Parameter becomes better by combining Fuzzy and Canny Edge Detection and also morphological operations. The results were compared with other edge detection techniques like interactive image segmentation by maximal similarity based region merging (MSRM) and Image segmentation using transition region. Therefore it is evident that the developed Algorithm provides Improved Performance parameters for detecting the edge against the wide range of Applications.

Image Segmentation, Fuzzy-canny Method, Morphological Operation, Misclassification Error

[1] A.G. Rudnitskii, M.A. Rudnytska, “Segmentation and Denoising of Phase Contrast MRI Image of the Aortic Lumen Via Fractal and Morphological Processing”, 37th International Conference on Electronics and Nanotechnology (ELNANO), 2017 IEEE.
[2] D. Chudasama, T. Patel, S. Joshi, G. Prajapati “Survey on Various Edge Detection Techniques on Noisy Images” , IJERT International Journal of Engineering Research & Technology ISSN: 2278-0181 Vol. 3 Issue 10, October- 2014.
[3] Maini, Raman, and Himanshu Aggarwal, "Study and comparison of various image edge detection techniques", International Journal of Image Processing (IJIP), Issue 3, no. 1, Pp. 1-11, 2009.
[4] Er. Komal Sharma, Er. Navneet Kaur, “Comparative Analysis of Various Edge Detection Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 12, December 2013.
[5] Ur Rehman Khan, K. Thakur “An Efficient Fuzzy Logic Based Edge Detection Algorithm for Gray Scale Image”, International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 8, August 2012).
[6] S. Patel, P.Trivedi, V. Gandhi and G. Prajapati, “2D Basic Shape Detection Using Region Properties” IJERT International Journal of Engineering Research & Technology, Vol. 2 Issue 5, May-2013.
[7] Mrs. A. Borkar, Mr. M.Atulkumar “Detection of Edges Using Fuzzy Inference System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 1, Issue 1, March 2013.
[8] T. Gajpal, Mr. S. Meshram “Edge Detection Technique Using Hybrid Fuzzy logic Method”, IJERT International Journal of Engineering Research & Technology, Vol. 2 Issue 2, Febuary-2013.
[9] M. L Comer, E. J. Delp “Morphological operations for color image processing” electronic imaging processing digital library.
[10] B. Baets, E. Kerre, M. Gupta “Fundamentals of Fuzzy Mathematical Morphology Part 1 Basic concepts” Overseas Publishers Association.
[11] R. Haralick and L. Shapiro Computer and Robot Vision, Vol. 1, Chap. 5, Addison-Wesley Publishing Company, 1992.

Sameer Kumar Sharma, Bharti Chourasia, "Transition Regions Based on Threshold Filter Approaches for Image Segmentation and Morphological Opertation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.262-265, 2019.

Filter bank Multicarrier (FBMC) is a novel technique evolved from OFDM which resolves most of these problems by taking a filtering approach to multicarrier communication system. FBMC signals can easily meet the Adjacent Channel Leakage Ratio (ACLR) and they do not use cyclic prefix thus improves spectral efficiency. The Filter bank Multicarrier (FBMC) transmission technique also leads to enhanced physical layer for future communication systems and it is an enabling technology for cognitive radio environment. Due to the inclusion of band-limited pulse shaping filters into the signal model in FBMC technique, the design of efficient transceiver architectures for multicarrier systems becomes a challenging task. In this paper the studied of MIMO technique for FBMC transceiver for 5G technologies is presented.

Filter Bank Multicarrier (FBMC), Filter Bank, MIMO System, 5G Technology

[1] Zongmiao He, Lingyu Zhou, Yiou Chen, Xiang Ling, “Filter Optimization of Out-of-Band Emission and BER Analysis for FBMC-OQAM System in 5G”, 2017 9th IEEE International Conference on Communication Software and Networks.
[2] Mawlawi, B., Dore, J.B., Berg, V. “Optimizing contention based access methods for FBMC waveforms, Int. Conf. on Military Commun. and Information Systems,” Cracow, Poland, May 2015, pp.1-6.
[3] Viholainen, A.,Ihalainen, T., Stitz, T.H., Renfors, M., and Bellanger “Prototype filter design for filter bank based multicarrier transmission,” 17 th Euro. Signal Process Conf., Glasgow, Scotland, August 2009, pp. 24-28.
[4] Chen, D., Qu, D.M., and Jiang, T. “Novel prototype filter design for FBMC base cognitive radio systems through direct optimization of filter coefficients,” IEEE Int. Conf. Wirel. Commun. &Signal Proc., Suzhou, China, October 2010, pp. 21-23.
[5] P. Siohan, C. Siclet, and N. Lacaille, “Analysis and design of OFDM/OQAM systems based on filter bank theory,” IEEE Trans. Signal Process., vol. 50, no. 5, pp. 1170–1183, May 2002.
[6] B. Farhang-Boroujeny, ”OFDM Versus Filter Bank Multicarrier”, IEEE Signal Processing Magazine, vol. 28, pp. 92-112, May 2011.
[7] B. Farhang-Boroujeny, “Cosine Modulated and Offset QAM Filter Bank Multicarrier Techniques A Continuous-Time Prospect,” EURASIP Journal on Advances in Signal Processing, pp. 6, Jan 2010.
[8] V. Ari, B. Maurice, and H. Mathieu, “WP5: Prototype filter and filter bank structure”, PHYDYASPHYsicallayer for Dynamic AccesS and cognitive radio, Jan 2009.
[9] B. Hirosaki, “An analysis of automatic equalizers for orthogonally multiplexed QAM systems,” IEEE Transactions on Communications, 28(1). pp. 73-83, Jan. 1980.
[10] S. Nedic and N. Popvic, ”Per-bin DFE for advanced OQAM-based multicarrier wireless data transmission systems,” Broadband Communications, 2002. Access, Transmission, Networking. 2002 International Zurich Seminar on, pp. 38-1, Feb. 2002.
[11] D. S. Waldhauser, L. G. Baltar and J. A. Nossek,”MMSE subcarrier for filter bank based multicarrier systems,” Proc. IEEE 9th Workshop on Signal Processing Advances in Wireless Communications, pp.525-529, July 2008.
[12] A. Ikhlef and J. Louveaux, ”An enhanced MMSE per subchannel equalizer for highly frequency selective channels for FBMC/OQAM systems,” Proc. IEEE 10th Workshop on Signal Processing Advances in Wireless Communications, pp. 186-190. June 2009.

Rakhi Gaday, Pooja Gupta, Bharti Gupta, "Review Paper on Filter Optimization 5G Technologies for FBMC Transceiver for High Power Amplifiers," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.266-270, 2019.

In a cellular network, the demand for high throughput and reliable transmission is increasing in large scale. One of the architectures proposed for 5G wireless communication to satisfy the demand is Massive MIMO system. The massive system is equipped with the large array of antennas at the Base Station (BS) serving multiple single antenna users simultaneously i.e., number of BS antennas are typically more compared to the number of users in a cell. The advantages of massive MIMO can be achieved only if Channel State Information (CSI) is known at BS uplink and downlink operate on orthogonal channels - TDD and FDD modes. Depending on slow/fast channel fading conditions, several authors suggested adaptive LMS, RLS and NLMS based channel estimators, which either require statistical information of the channel or are not efficient enough in terms of performance or computations. In order to overcome the above effects, the work focuses on the QR-RLS based channel estimation method for cell free Massive MIMO systems.

Massive MIMO, Channel State Information, Square Root-Recursive Least Square (QR-RLS)

[1] H. Q. Ngo A. Ashikhmin H. Yang E. G. Larsson T. L. Marzetta "Cell-free massive MIMO versus small cells" IEEE Trans. Wireless Commun. vol. 16 no. 3 pp. 1834-1850 Mar. 2017.
[2] Huang A. Burr "Compute-and-forward in cell-free massive MIMO: Great performance with low backhaul load" Proc. IEEE Int. Conf. Commun. (ICC) pp. 601-606 May 2017.
[3] Q. Huang A. Burr "Compute-and-forward in cell-free massive MIMO: Great performance with low backhaul load" Proc. IEEE Int. Conf. Commun. (ICC) pp. 601-606 May 2017.
[4] H. Q. Ngo A. Ashikhmin H. Yang E. G. Larsson T. L. Marzetta "Cell-free massive MIMO: Uniformly great service for everyone" Proc. IEEE Int. Workshop Signal Process. Adv. Wireless Commun. (SPAWC) pp. 201-205 Jun. 2015
[5] E. Nayebi A. Ashikhmin T. L. Marzetta H. Yang "Cell-free massive MIMO systems" Proc. 49th Asilomar Conf. Signals Syst. Comput. pp. 695-699 Nov. 2015.
[6] F. Rusek, D. Persson, B. K. Lau, E. G. Larsson, T. L. Marzetta, O. Edfors, and F. Tufvesson, “Scaling up mimo: Opportunities and challenges with very large arrays,” IEEE Signal Process. Mag., vol. 30, no. 1, pp. 40–60, Jan 2013.
[7] L. Lu, G. Y. Li, A. L. Swindlehurst, A. Ashikhmin, and R. Zhang, “An overview of massive MIMO: Benefits and challenges,” EEE J. Sel. Topics Signal Process., vol. 8, no. 5, pp. 742–758, Oct 2014.
[8] S. Payami and F. Tufvesson, “Channel measurements and analysis for very large array systems at 2.6 ghz,” Proc. 6th Eur. Conf. on Antennas and Propag. (EUCAP), pp. 433–437, March 2012.
[9] A. F. Molisch, V. V. Ratnam, S. Han, Z. Li, S. L. H. Nguyen, L. Li, and K. Haneda, “Hybrid beamforming for massive MIMO: A survey,” IEEE Comm. Mag., vol. 55, no. 9, pp. 134–141, 2017.
[10] F. Sohrabi and W. Yu, “Hybrid digital and analog beamforming design for large-scale antenna arrays,” IEEE Journal of Selected Topics in Signal Processing, vol. 10, no. 3, pp. 501–513, April 2016.
[11] O. E. Ayach, S. Rajagopal, S. Abu-Surra, Z. Pi, and R. W. Heath, “Spatially sparse precoding in millimeter wave MIMO systems,” IEEE Transactions on Wireless Communications, vol. 13, no. 3, pp. 1499–1513, March 2014.
[12] I. Ahmed, H. Khammari, A. Shahid, A. Musa, K. S. Kim, E. D. Poorter, and I. Moerman, “A survey on hybrid beamforming techniques in 5G: Architecture and system model perspectives,” IEEE Communications Surveys and Tutorials, vol. 20, no. 4, pp. 3060–3097, Fourthquarter 2018.
[13] S. Payami, N. Mysore Balasubramanya, C. Masouros, and M. Sellathurai, “Phase shifters versus switches: An energy efficiency perspective on hybrid beamforming,” IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 13–16, Feb
[14] Akhilesh Venkatasubramanian, Krithika.V and Partibane. B, “Channel Estimation For A Multi-User MIMO-OFDM- IDMA System”, International Conference on Communication and Signal Processing, April 6-8, 2017, India.

Swati Sharma, Sanket Choudhary, Bharti Gupta, "Review paper on Cell Free Massive MIMO Systems with FDD and TDD based Channel State Information," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.271-275, 2019.

Image Segmentation plays vital role in Computer Vision and Digital Image Processing. It is the process of separating the digital image into distinct region (s) possessing homogeneous properties. The main objective of image segmentation is to extract various features of the image that are used for analyzing, interpretation and understanding of images. Image segmentation is applied in various applications like medical imaging, shape detection, content-based image retrieval, robot vision, etc. Several techniques have been developed for image segmentation such as pixel-based segmentation, edge based segmentation and region based segmentation. In this paper, segmentation technique is defined using the edge detection and morphological operations. Edge detection is done using Fuzzy Canny method for better output. After detecting the edges of image, segmentation is done using morphological operation. This gives better results.

Dilation, Morphology, Erosion

[1] A.G. Rudnitskii, M.A. Rudnytska, “Segmentation and Denoising of Phase Contrast MRI Image of the Aortic Lumen Via Fractal and Morphological Processing”, 37th International Conference on Electronics and Nanotechnology (ELNANO), 2017 IEEE.
[2] D. Chudasama, T. Patel, S. Joshi, G. Prajapati “Survey on Various Edge Detection Techniques on Noisy Images” , IJERT International Journal of Engineering Research & Technology ISSN: 2278-0181 Vol. 3 Issue 10, October- 2014.
[3] Maini, Raman, and Himanshu Aggarwal, "Study and comparison of various image edge detection techniques", International Journal of Image Processing (IJIP), Issue 3, no. 1, Pp. 1-11, 2009.
[4] Er. Komal Sharma, Er. Navneet Kaur, “Comparative Analysis of Various Edge Detection Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 12, December 2013.
[5] Ur Rehman Khan, K. Thakur “An Efficient Fuzzy Logic Based Edge Detection Algorithm for Gray Scale Image”, International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com (ISSN 2250-2459, Volume 2, Issue 8, August 2012).
[6] S. Patel, P.Trivedi, V. Gandhi and G. Prajapati, “2D Basic Shape Detection Using Region Properties” IJERT International Journal of Engineering Research & Technology, Vol. 2 Issue 5, May-2013.
[7] Mrs. A. Borkar, Mr. M.Atulkumar “Detection of Edges Using Fuzzy Inference System”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 1, Issue 1, March 2013.
[8] T. Gajpal, Mr. S. Meshram “Edge Detection Technique Using Hybrid Fuzzy logic Method”, IJERT International Journal of Engineering Research & Technology, Vol. 2 Issue 2, Febuary-2013.
[9] M. L Comer, E. J. Delp “Morphological operations for color image processing” electronic imaging processing digital library.
[10] B. Baets, E. Kerre, M. Gupta “Fundamentals of Fuzzy Mathematical Morphology Part 1 Basic concepts” Overseas Publishers Association.
[11] R. Haralick and L. Shapiro Computer and Robot Vision, Vol. 1, Chap. 5, Addison-Wesley Publishing Company, 1992.

Sameer Kumar Sharma, Bharti Chourasia, "Review Paper on Segmentation of Color Image using Morphological Processing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.276-279, 2019.

Data Mining is an important aspect of diagnosing and predicting diseases in automatic manner. It involves developing appropriate techniques and algorithms to analyze data sets in medical field. At present, heart disease has excessively increased and heart diseases are becoming the most fatal diseases in several countries. In this paper, heart patient datasets are investigate for building classification models to predict the heart disease. This paper implements feature extraction technique construction and comparative study for improving the accuracy of predicting the heart disease. By the use of HCR-PSO (Highly Co-Related PSO) feature selection technique; a subset from whole normalized heart patient datasets is acquired which have only significant attributes. The study emphasized on finding the effective heart disease prediction construction by using various machine learning algorithms that are KNN(K-Nearest Neighbor), Random forest, SVM(Support Vector Machine), Bayesian network and MLP(Multilayer Perceptron). The research work central point is on finding the efficient classification algorithm for the prediction of heart disease in the early stage based on the accuracy using validation metrics that are Mean Absolute Error(MAE), Relative Squared Error(RSE) and Root Mean Square Error(RMSE).

HCR-PSO, Feature extraction, Bayesian and heart disease prediction model

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[8]. Cheryl Ann Alexander and Lidong Wang,” Big Data Analytics in Heart Attack Prediction”, JNurs Care, an open access journal, Volume 6 , Issue 2, ISSN:2167-1168,2017.
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Janani. S, "Enhanced Heart Disease Prediction Using HCR-PSO Based Data Analytical Model," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.280-286, 2019.

In a cellular network, the demand for high throughput and reliable transmission is increasing in large scale. One of the architectures proposed for 5G wireless communication to satisfy the demand is Massive MIMO system. The massive system is equipped with the large array of antennas at the Base Station (BS) serving multiple single antenna users simultaneously i.e., number of BS antennas are typically more compared to the number of users in a cell. This additional number of antennas at the base station increases the spatial degree of freedom which helps to increase throughput, maximize the beamforming gain, simplify the signal processing technique and reduces the need of more transmit power. The advantages of massive MIMO can be achieved only if Channel State Information (CSI) is known at BS uplink and downlink operate on orthogonal channels. The studied of non-cooperative cognitive radio network based massive MIMO systems is present in this paper.

Spectrum Sensing, Cognitive Radio, Non-Cooperative Communication, Massive MIMO

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Rishika Dubey, Vineeta Saxena Nigam, "High Spectrum Efficiency and Low BER of Massive MIMO System using Spectrum Sensing Cognitive Radio Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.7, pp.287-291, 2019.