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Over the past decades, clustering is the main issue in the wireless sensor network. To make the clustering and cluster head selection is the main issue in the wireless sensor network. In this study, the authors propose and evaluate the performance of MSEEC (Multi level stable and energy efficient clustering protocol) protocol in heterogeneous wireless sensor network based upon the parameters number of alive nodes, dead nodes , packet transferred and average Total energy and also makes the network better by using the hybrid ACO-PSO (Ant-colony optimization-particle swarm optimization) optimization technique for cluster head selection in MSEEC protocol on the parameters packet transferred and alive nodes.

Clustering, Wireless sensor network, cluster head selection, number of alive nodes, dead nodes , packet transferred and averageTotalenergy,hybridACO-PSO

[1]. Neelam, D. Khosla, "The Energy Efficient Techniques for Wireless Sensor Networks: A review", International Journal of Computer Sciences and Engineering, Vol.4(11), pp.38-41, 2016 .
[2]. A.S. Mandloi and V.Choudhary, "An Efficient Clustering Technique for Deterministically Deployed Wireless Sensor Networks", International Journal of Scientific Research in Network Security and Communication, Vol.1(1), pp.6-10, 2013
[3]. M Alnuaimi, K. Shuaib, K. A. Nuaimi, and M. A. Hafez, “Performance analysis of clustering protocols in WSN”. In Wireless and Mobile Networking Conference (WMNC), 6th Joint IFIP, IEEE,UAE, pp. 1-6, 2013.
[4]. A.M. Kishk, Nagy W. Messiha, A. Nawal El-Fishawy, Abdelrahman A. Alkafs, Ahmed H. Madian, "Proposed Jamming Removal Technique for Wireless Sensor Network", International Journal of Scientific Research in Network Security and Communication, Vol.3(2), pp.1-14, 2015.
[5]. Zhu, Jiang, C.H Lung, V. Srivastava,“H-DHAC: A hybrid clustering protocol for wireless sensor networks”. In Wireless Communications and Mobile Computing Conference (IWCMC), International Conference on IEEE,Canada , pp. 183-188, 2013.
[6]. J.S Leu, T.H Chiang, M. Yu, and K.W. Su, “Energy Efficient Clustering Scheme for Prolonging the Lifetime of Wireless Sensor Network With Isolated Nodes”, IEEE, Vol. 19(2) ,PP. 259-262, 2015.
[7]. V Farouk, Fifi, R. Rizk, F. W. Zaki, “Multi-level stable and energy-efficient clustering protocol in heterogeneous wireless sensor networks”, Wireless Sensor Systems, IET ,Vol.4(4), pp.159-169, 2014.
[8]. V. Raghavendran, N. Satish, P. S. Varma, “Intelligent routing techniques for mobile ad hoc networks using swarm intelligence”, I.J Intelligence systems and applications, Vol. 5(1), pp 81-89, 2012.
[9]. K. Sayed, A. Fathima, T. Sumitha, “To Enhance the Lifetime of WSN Network using PSO”, International Journal of Innovative Research in Computer and Engineering, Vol.2(1), 2014
[10]. A. Rana, M. Bala, Varsha, “Review Paper on Mseec: Energy Efficient Clustering Protocol in WSN”, International Journal of Computer Science and Engineering , Vol.4(5), 2016.

A. Rana, M. Bala, Varsha, "Performance Analysis of Energy Efficient Clustering Protocol in WSN," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.1-5, 2017.

In this paper, the propagation of soliton in metal-dielectric-metal (MDM) plasmonics waveguides was investigated for both nonasymmetric and asymmetric structures. Nonasymmetric effects such as Soliton are important for applications such as switching and wavelength conversion. In this paper, it was shown that field enhancement in nonasymmetric MDM waveguides can result in large enhancement of SOLITON magnitude compared to the literature values. Two different structures are considered here as plasmonics waveguide for generation of second harmonic. The first structure is a structure including of a Lithium Niobite as insulator sandwiched between two same metals. Thereafter, two different metals on both sides of the waveguide were used. Besides the structure has grating on both sides for more coupling between photons and plasmons. the wavelength The duration of grating per length unit (number of grooves) will be optimized to reach the highest second harmonic generation. To perform this optimization, the wavelength of operation of λ=458 nm is considered. It was shown that this asymmetric device results in more than two orders of magnitude enhancement in SOLITON compared to a structure with the same metals. It is also shown that the electric field of second harmonic depends on the thickness of crystal (insulator). So, its thickness is optimized to achieve the highest electric field.

Plasmonicss, Surface plasmons, Soliton

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[8]. D. S. LyGagnon, K. C. Balram, J. S. White, P. Wahl, M. L. Brongersma, and D. A. B. Miller, “Routing and photodetection in subwavelength plasmonics slot waveguides,” Journal of Nanophotonics, 1(1), 9–16, (2012).
[9]. T. Goto, Y. Katagiri, H. Fukuda, H. Shinojima, Y. Nakano, I. Kobayashi, and Y. Mitsuoka, “Propagation loss measurement for surface plasmon-polariton modes at metal waveguides on semiconductor substrates,” Applied Physics Letters, 84, 852-854, (2004).
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[12]. R. Zia, M. D. Selker, P. B. Catrysse, and M. L. Brongersma, “Geometries and materials for subwavelength surface plasmon modes,” Journal of the Optical Society of America A, 21, 2442-2446, (2004).
[13]. H. T. Miyazaki and Y. Kurokawa, “Squeezing visible light waves into a 3-nm-thick and 55-nm-long plasmon cavity,” Physical Review Letters, 96, 097401, (2006).
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M. Olyaee, M.B Tavakoli, A. Mokhtari, "Investigation of Soliton Propagation in Asymmetric metal-dielectric-metal Plasmonics Waveguide," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.6-10, 2017.

In this paper, we study experimentally the performance of VoIP on wired communication with varied combination of Routers and hosts. We concentrate on two factors of QoS – Delay and Jitter. NS3 simulator is used to study the pattern and effect on the two factors by varying the number of Routers and hosts in the given setup. By this study the user can decide on what combination is best for his application and what factors can be compromised and which factors can be enhanced. Also, we have concluded by giving a glimpse of using

TCP, UDP, VoIP, QoS, NS3

[1] Urjashee Shaw, Bobby Sharma, “A survey Paper on Voice over Internet Protocol (VoIP)”, International Journal of Computer Applications, Volume 139, pp 16 – 22, 2016.
[2]Haniyeh Kazemitabar, Sameha Ahmed, Kashif Nisar, Abas B Said, Halabi B Hasbullah, “A Survey on Voice over IP over Wireless LANs”, World Academy of Science, Engineering and Technology 71,pp 352-358, 2010,
[3] Sachin Garg, Martin Kappes, “An Experimental Study of Throughput for UDP and VoIP Traffic in IEEE 802.11b Networks”, Conference: Wireless Communications and Networking,IEEE, Volume: 3, pp 1748-1753, 2003.
[4] Ajay Kumar,” An overview of voice over internet protocol (voip)”, Rivier college online academic journal, volume 2, number 1, pp 16-22, spring 2006.
[5] T. Suda ; H. Miyahara ; T. Hasegawa, “Performance Evaluation of a Packetized Voice System--Simulation Study”, IEEE transactions on communication, pp 97-102, 2003.
[6] Atta ur Rehman Khan, Sardar M. Bilalb, Mazliza Othmana, “A Performance Comparison of Network Simulators for Wireless Network” , IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, pp 1-6, 23 - 25 Nov. 2012.
[7] Jong Min Lee, “Design of an Ns-3 Generic Application ArchitectureApplying Design Patterns”, International Journal of Modern Engineering Research, Vol. 5, Issue 12, pp – 47 to 55, Dec 15.
[8] Pallavi S. Katkar, Dr. Vijay R. Ghorpade, “Comparative Study of Network Simulator: NS2 and NS3”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 6, Iss. 3, pp – 608 to 612, March 2016.
[9] Rakesh Kumar Jha, Pooja Kharga,” Advanced Open Source Simulator: NS-3”, International Journal of Computer Science and Engineering, Volume 3, Iss. 12,pp 1-9, 2015.
[10]Kaur-Kehal, R., & Sengupta, “A comprehensive review on improving QoS for VoIP in wirelessmesh networks”. Journal of Gobal Research in Computer Science, 2, pp 32-33, 2011
[11] Petr Zach, Martin Pokorny, Jiri Balej, “Quality of Experience of Voice Services in Corporate Network”, Enterprise and the Competitive Environment March 2014 conference, ECE 2014, pp 771-779, Brno, Czech Republic, 2014.

P.N. Kadam, R.R. Gangarde, "To Evaluate the Performance based on Delay and Jitter of Wired VoIP using a network of assorted number of Routers and Hosts.," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.11-15, 2017.

In this paper the structure of Nano- Wire would be optimized to achieve high sensibility and frequency response. To perform this optimization, the length of Nano- Wire g region and the thickness of absorber layer will be optimized. Silvaco software is used for simulation and optimization. The proposed structure includes a window profile is used for Nano- Wire.

Nanowire, Doping, Grating

[1] P.S. Menon, S. Kalthom Tasirin, Ibrahim Ahmad and S. Fazlili Abdullah, “Optimization of Process Parameters for Si Lateral NANO- WIRE Nanowire” World Applied Sciences Journal 21 (Mathematical Applications in Engineering): 98-103, 2013.
[2] Souza, M., O. Bulteel, D. Flandre and M.A. Pavanello. Temperature and silicon film thickness
influence on the operation of lateral SOI NANO- WIRE Nanowires for detection of short wavelength, J.Integrated Circuits and Systems, 6(1): 107-113, 2011.
[3] Ehsan, A.A., Shaari, S., Majlis, B.Y.(2001) Silicon Planar p-i-n Nanowire for OEIC. IEEE Nat’l. Symp. on Microelectronics:316.
[4] Menon P.S., Ahmad M. H. F., Tugi A., Ehsan A. A. and Shaari S. (2003). Dark Current-Voltage(I-V) Characteristic of a Silicon NANO- WIRE Lateral Nanowire. IEEE National Symposium on Microelectronics : 207-210.
[5] Menon P.S. and Shaari S. (2003). The Effectof Intrinsic Region Width Variance on the Responsivity and Current-Voltage(IV) Characteristics of a Silicon Lateral NANO- WIRE Nanowire. IMEN – Procs. on Photonics: Planar Waveguide and Fiber Based Opt. Comm.Dev. 1: 76-79.
[6] Menon, P.S., Pembangunan diodfoto planar p-i-n silikon (Development of silicon-based p-i-n Nanowire), MSc Thesis. Universiti KebangsaanMalaysia, 2013.
[7] Menon, P.S. and S. Shaari, 2005. Surface versus lateral illumination effects on an interdigitated Si planar NANO- WIRE Nanowire. Proceedings of the SPIE Symposium on Optics and Photonics: Infrared and Photoelectronic Imagers and Detector Devices, 2005, San Diego, USA, 5881: art. no. 58810S, pp: 1-8.
[8] Jang, J.H., G. Cueva, D.C. Dumka, W.E. Hoke
P.J. Lemonias and I. Adesida, 2001. Long-Wavelength In0.53Ga0.47As Metamorphic p-i-n Nanowire on GaAs Subtrates. IEEE Photonics Technology Letters, 3(2):

M.R. Ghahri, S. SheikhHasani, "Nano- Wire structure optimization to achieve high sensibility and frequency response," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.16-19, 2017.

In this paper a ring CURRENT CONTROLLED OSCILLATOR with high frequency range and low phase noise in Nanowire transistor technology is presented. In the proposed CURRENT CONTROLLED OSCILLATOR, two techniques including control of current and body bias technique is implemented to increase the range of frequency. It is proved that body bias of control transistor cause to increase the frequency range noticeably. Moreover, by adding an inductor in the body of control transistor, the phase noise is decreased as well. The phase noise in 1 MHz offset frequency is -90 dBc/Hz and the frequency range is 2-14 GHz.

Current Controlled Oscillator, Ring, Phase Noise, Frequency Range

[1] M.-L. Sheu, Y.-S. Tiao, L.-J. Taso, "A 1-V 4-GHz wide tuning range voltage-controlled ring
oscillator in 0.18µm CMOS," Microelectronics Journal, vol. 42, pp. 897-902, 2011.
[2] J. Zhao and Y.-B. Kim, "A low-power digitally controlled oscillator for all digital phase-locked
loops," VLSI Design, vol. 2010, p. 2, 2010.
[3] W.-H. Lee, B.-J. Gu, Y. Nishida, H. Takao, K. Sawada, M. Ishida, "Oscillation-controlled CMOS ring
oscillator for wireless sensor systems," Microelectronics Journal, vol. 41, pp. 815-819, 2010.
[4] M. Frankiewicz and A. Kos, "Wide-frequency-range low-power variable-length ring oscillator in
UMC CMOS 0.18 µm," in Mixed Design of Integrated Circuits and Systems (MIXDES), 2013
Proceedings of the 20th International Conference, 2013, pp. 291-293.
[5] T. Li, B. Ye, J. Jiang, "0.5 V 1.3 GHz voltage controlled ring oscillator," in ASIC, 2009. ASICON`09.
IEEE 8th International Conference on, 2009, pp. 1181-1184.
[6] U. Guler and G. Dundar, "Modeling CMOS Ring Oscillator Performance as a Randomness Source,"
IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 61, pp. 712- 724, 2014.
[7] T. V. Cao, D.T. Wisland, T.S. Lande, F. Moradi, "A bulk-controlled ring-CURRENT CONTROLLED OSCILLATOR with 1/f-noise
reduction for frequency ∆Σ modulator," in Mixed Design of Integrated Circuits & Systems, 2009.
MIXDES`09. MIXDES-16th International Conference, 2009, pp. 187-192.
[8] A. Ramazani, S. Biabani, GH. Hadidi, "CMOS Ring Oscillator with Combined Delay Stages," AEU-International Journal of Electronics and Communications, 2014.
[9] P. Nugroho, R.K. Pokharel, H. Kanaya, K. Yoshida, "A 5.9 GHz Low Power and Wide Tuning Range
CMOS Current-controlled Ring Oscillator," International Journal of Electrical and Computer
Engineering (IJECE), vol. 2, pp. 293-300, 2012.
[10] J. Radic, A. Djugova, L. Nagy, M. Videnovic-Misic, "Body effect influence on 0.18 µ m CMOS ring
oscillator performance for IR-UWB pulse generator applications," in Embedded Computing (MECO),
2012 Mediterranean Conference on, 2012, pp. 170-173.
[11] J. Garg and S. Verma, "Design of low power Voltage Controlled Oscillator," in Emerging Technology
Trends in Electronics, Communication and Networking (ET2ECN), 2012 1st International Conference
on, 2012, pp. 1-4.
[12] R. Ashish, J.N. Vashishtha, R K sarin, "A RF Low Power 0.18-µ m based CMOS Differential ring
oscillator," Proceedings of the World Congress on Engineering, vol. 2, 2012.
[13] S.-M. Kang and Y. Leblebici, CMOS digital integrated circuits: Tata McGraw-Hill Education, 2003.
[14] A. Daghighi and A. Neshat-Niko, "CURRENT CONTROLLED OSCILLATOR Design and Simulation Using TSMC 0.18 um Process to
Meet IEEE802.11a Requirements," Majlesi Journal of Electrical Engineering, vol. 2, pp. 29-37, 2008.
[15] S. Shieh Ali Saleh and N. Masoumi, "Wide-tuning-range, low-phase-noise quadrature ring oscillator
exploiting a novel noise canceling technique," AEU - International Journal of Electronics and
Communications, vol. 66, pp. 372-379, 2012.
[9] Jian ZhangI;Guoch Huang,” SiGe V Band Wide Tuning-Range Current controlled oscillator and Frequency Divider for Phase Locked Loop”, Integrated Nonlinear Microwave and Millimetre-Wave Circuits (INMMIC), Sept. 2012,pp(1 – 3), E-ISBN :978-1-4673-2948-4

M. Khalaj, "Design of a Novel Ring Current Controlled Oscillator with low Phase Noise and High frequency range Using Nanowire Transistor," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.20-24, 2017.

The main limitations of the typical solar conversion device is that low energy photons cannot excite charge carriers to the conduction band, therefore do not contribute to the device’s current, and high energy photons are not efficiently used due to a poor match to the energy gap. Currently, Graphen based Solar Cells GBSC are one of the most active research fields in the third generation solar cells which can resolve this problem. In the present work, we are interested in modeling and simulating of both standard GaAs p-i-n solar cell and GaSb/GaAs Graphene Solar Cell. When comparing 40-layers GaSb/GaAs Graphene solar cell with standard GaAs solar cell, the conversion efficiency in simulation results increased from 16.48 % to 22.46 %, which is relatively 36.3% increase. Also, the absorption range edge of photons with low energies extended from 900 to 1200 nm. The results reveal that the GaSb/GaAs Graphene solar cell manifests much larger power conversion efficiency than that of p-i-n junction solar cells.

Solar Cell, Graphene, Grating

[1] Pablo Garcia-Linares Fontes, “Research on Intermediate Bands Solar Cells and development of experimental techniques for their characterization under concentrated illumination”, Thesis Doctoral, Universidad Politécnica de Madrid, 2012.
[2] A.Luque, A. Marti, A.Nozik, "Solar cells based on quantum dots," MRS bulletin, 2007, 32:236-241.
[3] C.Bailey, "Optical and mechanical characterization of InAs/GaAs Graphene solar cells," a dissertation for the degree of Doctor of Philosophy, Rochester Institute of Technology, January, 2012.
[4] C.Bailey, S.Polly, R.Raffaelle, et al, "Open-circuit voltage enhancementment of InAs/GaAs graphen based Solar Cells using reduced InAs coverage," IEEE Journal of Photovoltaic, Vol.2, No.3, July 2012, 269-275.
[5] P. Michalopoulos, “A novel approach for the development and optimization of state-of-the-art photovoltaic devices using Silvaco,” M.S. Thesis, Naval Postgraduate School, Monterey, California, March 2002.
[6] D.J. Roulston, N.D. Arora, and S.G. Chamberlain, “Modeling and Measurement of Minority-Carrier Lifetime versus Doping in Diffused Layers of n ±p Silicon Diodes”, IEEE Trans. on Electron Devices, ED-29, Feb. 1982, p. 284-291.
[7] Boujdaria K, Ridene S and Fishman G 2001 Luttinger-like parameter calculation Phys. Rev. B 63 235302.
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89 5815–62.
[9] MASETTI G., M.SEVERI, AND S.SOLMI, “Modeling of Carrier Mobility Against Carrier Concentration in Arsenic, Phosphorous and Boron doped Silicon”, IEEE Trans. Elec. Dev. ED-30, (1983): 764-769.

S. Shafie, M. Imanie, "Enhancement of External Quantum Efficiency of GaSb/GaAs solar cell Based on Graphene," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.25-28, 2017.

Human soft tissues are diagnosed by different imaging modalities such as Computed tomography CT, Ultrasound US, Magnetic resonance imaging MRI all these imaging modalities are applied depending on the nature of the disease. In the classification of liver related diseases each of these imaging modalities plays important role. Classifying a liver into normal liver and diseased liver (in diseased cirrhotic or fatty liver) depends completely on the texture of the liver. Texture is a combination of repeated patterns with regular or irregular frequency. Texture visualization is easier but very difficult to describe in words. Analyzing liver texture is also difficult. To classify liver into its respective diseases category it is very important to extract the Region of Interest ROI accurately by segmentation, but as liver structure has maximum disparity in intensity texture inside and along boundary which serves as a major problem in its segmentation and classification. There are different textural analysis techniques developed for liver classification over the years some of which work equally well for most of the imaging modalities. Here, an attempt is made to summarize the importance of textural analysis techniques devised for different imaging modalities.

Cirrhotic, fatty liver, Ultrasound, Computed tomography, Magnetic resonance imaging, Texture Analysis, Liver Classification

[1] Rathore S, Iftikhar M, Hussain M, Jalil A, “Texture analysis for liver segmentation and classification: a survey”, Frontiers of Information Technology (FIT), Islamabad, pp.121–126, 2011, Print ISBN 978-1-4673-0209-8
[2] Mougiakakou S G, Valavanis I, Konstantina S N, Alexandra N and D. Kelekis., “Characterization of CT liver lesions based on texture features and a multiple Neural Network classification scheme”, Engineering in Medicine and Biology Society, Mexico, pp.1287 - 1290, 2003, Print ISBN 0-7803-7789-3.
[3] Mala K, Sadasivam V., “Automatic segmentation and classification of diffused liver diseases using wavelet based texture analysis and Neural Network”, INDICON 2005 Annual IEEE, NA, pp. 216-219, 2005, Print ISBN 0-7803-9503-4.
[4] Mala K, Sadasivam V., “Wavelet based texture analysis of Liver tumor from Computed Tomography images for characterization using Linear Vector Quantization Neural Network”, Advanced Computing and Communications International conference, Surathkal, pp. 267 – 270, 2006, E-ISBN: 1-4244-0716-8.
[5] Subbiah V. B, Vijilious M.A.Leo, and Ganesan L, “Orthogonal Moments based texture analysis of CT liver images”, International Conference on Computational Intelligence and Multimedia Applications, Tamilnadu-India, pp.249-253, 2007, Print ISBN: 0-7695-3050-8
[6] Nawaz S, and Dar A.H., “Hepatic lesions classification by ensemble of SVMs using statistical features based on co-occurrence matrix”, International Conference on Emerging Technologies, IEEE-ICET- 2008, Rawalpindi-Pakistan, pp.21-26, 2008, Print ISBN: 978-1-4244-2210-4
[7] Sreeraj R, Raju. G “Automatic Detection of Liver Tumor in CT Image Using Region Growing and SVM Classifier” IJCSE Volume-4, Issue-11, 2016
[8] Ahmadian A, Mostafa A, Abolhassani M.D, and Salimpour Y, “A texture classification method for diffused liver diseases using Gabor wavelets”, 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005- Shanghai- China, pp. 1567 – 1570, 2005, Print ISBN: 0-7803-8741-4.
[9] Balasubramanian D, Srinivasan P and Gurupatham R, “Automatic classification of focal lesions in ultrasound liver images using Principal Component Analysis and Neural Networks”, 29th Annual International Conference of the IEEE Engineering and Medicine and Biology Society- 2007, Lyon-France, pp.2134-2137, 2007, Print ISBN: 978-1-4244-0787-3.
[10] Hwanga Y N, Lee JH, Kim GY, Jiang YY and Kim SM “Classification of focal liver lesions on ultrasound images by extracting hybrid textural features and using an artificial neural network”, Bio-Medical Materials and Engineering, Shanghai-China , pp. S1599–S1611, 2015.
[11] Neogi N, Adhikari A, Roy M “Classification of Ultrasonography Images of Human Fatty and Normal Livers using GLCM Textural Features”, Current Trends in Technology and Science, Vol 3(4), pp.252-259, 2014
[12] Haralik R. M, and Shanmuygam K, “Textural features for image classification”, IEEE Transactions on systems, Man, Cybernetics., Vol. SMC-3(6), pp. 610-621, 1973.
[13] Luo S, Qingmao H, Xiangjian H, Jiaming L, Jesse S.Jin, Mira P , “Automatic liver parenchyma segmentation from abdominal CT images using Support Vector Machines” , ICME International Conference on Complex Medical Engineering(2009), Tempe-USA, pp.1-5, 2009
[14] Cao G, Shi P, and Hu B, “Liver fibrosis identification based on ultrasonic images”, Engineering in Medicine and Biology Society, 2005, Shangai, pp.6317 – 6320, 2005.
[15] Virmani J, Kumar V, Kalra N, Khandelwal N. “SVM-based characterization of liver ultrasound images using wavelet packet texture descriptors”, Jounal of Digit Imaging, Vol 26(3), pp.530-543, 2013
[16] Minhas F, Sabih D, Hussain M. “Automated classification of liver disorders using ultrasound images”, Journal of Medical Systems, Vol 36(5), pp.3163–3172, 2012.
[17] Andrade A, Silva JS, Santos J, Belo-Soares P. “Classifier approaches for liver steatosis using ultrasound images”, Procedia Technology, Vol 5, pp. 763-770,2012.
[18] Huang Y, Han X, Tian X, Zhao Z, Zhao J, Hao D. “Texture analysis of ultrasonic liver images based on spatial domain methods”, Image and Signal Processing (CISP)2010, Yantai-China, pp. 562–565, 2010

R.V. Patil, S.S. Sannakki, V.S. Rajpurohit, "A Survey on Classification of Liver Diseases using Image Processing and Data Mining Techniques," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.29-34, 2017.

There is ample amount of data present in the whole world. The data is generated from various sources like companies, organizations, social networking sites, image processing, world wide web, scientific and medical etc. People have less time to look at whole data. They attended towards the precious and interested information. Data mining is technique which is used to extract meaningful information from huge databases. Extracted information is visualized in the form of statics, graphs, and tables and videos etc. There are number of data mining techniques, asymmetric clustering is one of them. Asymmetric technique is type of unsupervised learning. In this, data sets which have similarity are placed in one cluster and others are in different clusters. From, number of years various asymmetric clustering techniques are introduced which work well with datasets. These techniques do not work well with the complex and strongly coupled data sets. To reduce processing time and improve accuracy neural networks are combined with asymmetric clustering algorithms.

Backpropagation, Data mining, DBSCAN, neural network, normalization

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P.K. Dhillon, A.S. Walia, "To evaluate and improve DBSCAN algorithm with normalization in data mining: A Review," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.35-39, 2017.

This is to presents a study on performance of UCI Hungarian data sets using missing value management techniques. We used bootstrap algorithm with multiple imputation (MI), LOCF, Mean–Mode substitution and IV-for missingness on the reduct file of the dataset to use all 294 instances in the dataset for our experimental input. Five imputed files were generated from the original reduct file in MI technique where from we have taken the average result and created other input files as per requirements for each specified technique, which are studied using two most recognized but opposite in nature approaches for classification, viz. IBPLN and BBP among many of such learning algorithms in the literature [10], but the most well-known among them are back propagation [11], [12], ART [13], and RBF networks [14]. Accuracy for test cases of five imputed files varies from 89.79% to 99.00% by CCR measure, the most recognized benchmarking parameter for judging classification result and performance of the dataset.

Hungarian data sets, CARN, Amelia View, R Statistical platform, Boot strapping, Multiple imputation, LOCF, Mean–Mode substitution, IV-for missingness, online incremental back propagation, Batch back propagation, CCR.

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R. Misir, R.K. Samanta, "A study on performance of UCI Hungarian dataset using missing value management techniques," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.40-44, 2017.

An automated method for segmentation of Magnetic Resonance (MR) head images into brain and non-brain has been proposed. It combines the strategy used in intensity and morphological skull-stripping methods. The method is very fast and requires no preprocessing of MR images. It is testified on T1-Weighted MR image modality and produces accurate output.

MRI, Skull-Stripping , ROI, T1-Weighted, Windowing

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S. Sarkar, A. Mandal, K. Sarkar, "An Automated Skull-Stripping Method by Windowing The Histogram," International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.45-49, 2017.