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Pipelines are normally designed to deliver fluid at the required head and flow rate in a cost effective manner. The selection of an optimum pipe size for pumping plants and pipelines in pressurized flow pipe network should be based on careful economic analysis. Increase in conduit diameter leads to increase in annual capital costs, and decrease in operating costs. The study presents an optimized pipe diameter selection for the farm located at Hamelmalo Agricultural College (HAC). Relationships were formulated connecting theories and principles of hydraulic and economic analysis of the pipe selection process. These were developed into a computer program, written in Java language, for a high- level precision estimate of the optimum pipe diameter. The optimum pipe diameter design primarily includes three major methods available in literature namely Pipe sizing design using Jack’s Cube method, design based on Head Loss Gradient method and design based on Smit’s method.

Pressurized flow networks, Optimized Pipe diameter, Hydraulic aspects of pipe sizing, Economic aspects of pipe sizing, Java Programming Language

[1] Arunjyoti Sonowal, Sirisha Adamala, S.C.Senapati, “A mathematical model for the selection of an economical pipe size in pressurized irrigation systems”, African Journal of Agricultural Research, Vol 11(8), pp. 683-692, 25 February, 2016.
[2] Merle C.Potter, David C.Wiggert, Schaum`s Outline of Fluid Mechanics (Schaum`s Outline Series), McGraw-Hill Education, pp.40, 16 January, 2008
[3] Randall W.Whitesides, P.E., “Selecting the Optimum Pipe Size”, PDH online Course M270 (12 PDH), pp. 10-12, 2008, 2015.
[4] Swamee, P., and Jain, A., “Explicit Equations for Pipe-Flow Problems”, Journal of the Hydraulics Division-American Society Civil Engineers, pp. 657-664, 1976.
[5] Adams J.N., “Quickly Estimate Pipe Sizing with ‘Jack’s Cube’”, Chemical Engineering Progress pp. 55-58, December, 1997.
[6] Andreas P.Savva , Karen Frenken, “Irrigation Equipment for Pressurized Systems”, Irrigation manual module 10, FAO sub-regional office for East and Southern Africa, pp.5, 2002.
[7] V.E.M.G.Diniz & P.A.Souza, “ Four explicit formulae for friction factor calculations in pipe flow”, WIT Transactions on Ecology and the Environment, Vol 125, 2009.
[8] Nekrasov.B, “Hidraulica”, 2 edition, Editorial Mir: Moscu, pp. 95, 1966.
[9] Souza P.A., “Nova Formulacao Explicita para o Calculo do Fator de Atrito (f) de Escoamento Forcado”, Anais do XII Congresso Latinoamericano de Hidraulica, Sao Paulo, pp. 75-85, 1986.
[10] G.P.Merkley and R.G.Allen, “ Sprinkle& Trickle Irrigation Lecture Notes”, Biological and Irrigation department, Utah State University, Logan, Utah, BIE 5110/6110, Fall semester, pp.69, 2004.
[11] Jack Keller, Ron D.Bliesner, “ Sprinkle And Trickle Irrigation”, Springer Science + Business Media, LLC, pp. 160, 1990.
[12] Richard C.Thompson, Charles J.Moore, Frederick S.Vom Saal,Shanna H.Swan, “Plastics, the environment and human health: current consensus and future trends”, Philosophical Transactions B , The Royal Society Publishing pp. 364(1526): 2153-2166, July 27 2009.
[13] R.G. Allen, “Relating the Hazen-Williams and Darcy- Weisbach Friction Loss Equations for Pressurized Irrigation”, Applied Engineering in Agriculture, Vol. 12(6): pp.685-693, 1996.

Aanandsundar Arumugam, Gedion Habtay, Haben Kibrom, Medhanie Gebreamlak, "Comparison of Optimal Pipe Sizing Designs for Pressurized Flow System," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.520-529, 2018.

Recommender Systems are established progressively popular in now-a-days and developed in a variety of zones counting master`s associates, jokes, and eateries, articles of clothing, budgetary administrations, life coverage, emotional accomplices and Twitter pages. The proficient management of record streams assumes an essential part in abundant information filtering systems. A focal server displays the archive stream and constantly reports to every client the best k records that are most appropriate to catch phrases. By using estimated procedure client can discover top k result in light of put stock in admirable information. The approach gives perpetual best k spot brings about powerful path by engaging data mining measures. The proposed organization helps user to download trust worthy data based on only the amount of files transferred by users not based on ratings and assessments. This technique filter outs the unworthy data from the whole evidence. It coordinates rating and puts stock in data to progress the rating positioning model, which adequately augments the nature of the best k thing depressed of all clients. A development of tests on genuine datasets establishes the competence of our intention

Recommender System, Information Filtering, Top-K Algorithm, Trust worthy Data, Commendable Information

[1] P. Haghani, S. Michel, and K. Aberer, “The gist of everything new: personalized top-k processing over web 2.0 streams.” in CIKM, 2010, pp. 489–498.
[2] K. Mouratidis and H. Pang, “Efficient evaluation of continuous text search queries,” IEEE Trans. Knowl. Data Eng., vol. 23, no. 10, pp. 1469–1482, 2011.
[3] N. Vouzoukidou, B. Amann, and V. Christophides, “Processing continuous text queries featuring non-homogeneous scoring functions.” in CIKM, 2012, pp. 1065–1074.
[4] A. Hoppe, “Automatic ontology-based user profile learning from heterogeneous web resources in a big data context.” PVLDB, pp. 1428–1433, 2013.
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[6] M. Busch, K. Gade, B. Larson, P. Lok, S. Luckenbill, and J. J. Lin, “Earlybird: Real-time search at twitter,” in ICDE, 2012, pp. 1360– 1369.
[7] L. Wu, W. Lin, X. Xiao, and Y. Xu, “LSII: an indexing structure for exact real-time search on microblogs,” in ICDE, 2013, pp. 482–493.
[8] J. Zobel and A. Moffat, “Inverted files for text search engines,” ACM Comput. Surv., vol. 38, no. 2, 2006.
[9] R. Fagin, A. Lotem, and M. Naor, “Optimal aggregation algorithms for middleware,” J. Comput. Syst. Sci., vol. 66, no. 4, pp. 614–656, 2003.
[10] A. Z. Broder, D. Carmel, M. Herscovici, A. Soffer, and J. Y. Zien, “Efficient query evaluation using a two-level retrieval process.” in CIKM, 2003, pp. 426–434.
[11] S. Prabhakar, Y. Xia, D. V. Kalashnikov, W. G. Aref, and S. E. Hambrusch, “Query indexing and velocity constrained indexing: Scalable techniques for continuous queries on moving objects,” IEEE Trans. Computers, vol. 51, no. 10, pp. 1124–1140, 2002.
[12] S. E. Robertson and D. A. Hull, “The TREC-9 Filtering Track Final Report,” in Text REtrieval Conference, 2000, pp. 25–40.
[13] Y. Zhang and J. Callan, “Maximum Likelihood Estimation for Filtering Thresholds,” in SIGIR, 2001, pp. 294–302.
[14] F. Fabret, H. Jacobsen, F. Llirbat, J. L. M. Pereira, K. A. Ross, and D. Shasha, “Filtering algorithms and implementation for very fast publish/subscribe,” in SIGMOD Conference, 2001, pp. 115–126.
[15] W. Rao, L. Chen, A. W.-C. Fu, H. Chen, and F. Zou, “On efficient content matching in distributed pub/sub systems.” in INFOCOM, 2009, pp. 756–764.

E. Renuga, S. Baskaran, "An Innovative Approach for Top-K Spot Monitoring Based On Trust Worthy Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.530-533, 2018.

Opinion mining is a personal belief, view, perception, comments or expression on some topic ,person, product etc. In a current digital word every information is on Word Wide Web or digital form. With adventure of 4G mobile technology people use and share data or information on social media like twitter, face book, whatsapp application via mobile or computer. In order to take very quick and reasonable decision, it has become a common practice for decision maker to let their users review or express opinions about particular domain. A common user feels comfortable with the Internet, more and more opinions are writing. The number of opinions users receives is growing fast. Some popular products can get hundreds or thousands of reviews on some good trading venues. This makes it very difficult for a potential customer to read it to help determine if the product should be purchased or not. Therefore in this paper we introduce a simplified mobile computing opinion mining model which handles multilingual voice to text and text processing opinions. Moreover we compare accuracy result of existing Naïve bayes and SVM opinion mining algorithm result with our implemented model.

Opinion, Sentiment Analysis, Supervise Learning, Naïve Bayes, SVM, OM

[1] Rushabh Shah and Bhoomit Patel ” Procedure of Opinion Mining and Sentiment Analysis: A Study.” International Journal of Current Engineering and Technology Vol.4, No.6.
[2] Stavros Valsamidisa, Theodosios Theodosioua, Ioannis Kazanidisa, Michael Nikolaidisa”A Framework for Opinion Mining in Blogs for Agriculture. 6th International Conference on Information and Communication Technologies in Agriculture, Food and Environment (HAICTA 2013).
[3] “Samhaa R. El-Beltagy*, Ahmed Rafea_, Said Mabrouk_ and Mahmoud Rafea” An Approach for Mining Accumulated Crop Cultivation Problems and their Solutions. Association for the Advancement of Artificial Intelligence .
[4] “Pravesh Kumar Singh, Mohd Shahid Husain “Methodological Study Of Opinion Mining And Sentiment Analysis Techniques.” International Journal on Soft Computing (IJSC) Vol. 5, No. 1, February 2014.
[5] Trivedi Khushboo N, Swati K. Vekariya, Shailendra Mishra, “Mining of Sentence Level Opinion Using Supervised Term Weighted Approach of Naïve Bayesian Algorithm”, International Journal of computer Technology & Applications,Vol 3 (3), 987-991 ISSN:2229-6093,May-June 2012.
[6] Vidisha M. Pradhan ,jay vala and prem balani. “A Survey on Sentiment Analysis Algorithms for Opinion Mining” International Journal of Computer Applications (0975 – 8887) Volume 133 – No.9, January 2016.
[7] Y nikhil ,P sneha ,S prithvi raj , ajay ram ,e rajiv “sentiment analysis of mobile reviews using supervised learning methods” a dissertation submitted in partial fulfillment of the requirements for the award of the degree of bachelor of technology in computer science and engineering department of computer science and engineering university college of engineering jawaharlal nehru technological university kakinada, kakinada – 533003, a.p 2011 – 2013.
[8] Richa Sharma, Sweta Nigam , Rekha Jain. “ Polarity Detection at Sentence Level” International Journal of Computer Applications (0975 – 8887) Volume 86 – No 11, January 2014.
[9] Mr.Nayan S. Patel, Dr.D.B.Shah- “An Algorithm for Mobile Computing Opinion Mining In Multilingual Forms By Voice and Text Processing” International Journal of Scientific Research in Science and Technology ISSN: 2395-602X Volume 4 | Issue 5, 1262-1266 issue March 2018.

N.S. Patel, H.B. Bhadka, D.B. Shah, "Result Comparison Of Naive Bayesian And SVM Opinion Mining Algorithm With Mobile Computing Multilingual Opinion Mining Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.534-538, 2018.

To find out the location of unknown area, user need to call and ask some others where they are. In “Internet and computer Geo-Location tracking” the user can be tracked by the tracker by providing the IP address. But “Tracking using GPS in smart phones” helps user to find out the location of the person with the mobile number. “Tracking using GPS in smart phones” uses a mobile application based on providing Location Based Services (LBS) using Global Positioning System (GPS) as a location provider. This application offers the service to get users current location and shows the same on Google Map. The location is updated when the user moves from one place to another. The main objective of this work is to design and implement a client server system that helps users to locate their family members and receive alerts.

Location based services, Global Positioning System, Internet and computer Geo-Location tracking, Object Tracking, Google Map

[1]PrathushaPerugu“An Innovative Method using GPS Tracking,WINS Technologies for Border Security and Tracking of Vehicles”IEEE ,2008.
[2]Po-Hsuan Tseng, Student Member, IEEE, Kai-Ten Feng, Member, IEEE,Yu-Chiun Lin, and Chao-Lin Chen “Wireless Location Tracking Algorithm for Environments with Insufficient Signal Sources” IEEE Transactions on mobile computing,Vol. 8,No.12,December 2009.
[3]Christian S. Jensen Kenneth H. Pedersen Kristian Torp“ATestbed for theExploration of Novel Concepts in Mobile Service Delivery” IEEE, 2007.
[4]Shih-I Chen, Fu-Chien Kao” The Design of Embedded GPS Navigation System Based on Internet Structure” Proceedings of the 3rd workshop on positioning, Navigation and communication (WPNC’06).
[5]AlminasCCivilis, Christian S. Jensen, Senior Member, IEEE, and StardasPakalnis “Techniques for Efficient Road-Network-Based Tracking of Moving Objects Program Modules” IEEE Transactions on knowledge and data Engineering,Vol. 17,No.5,May 2005.

Albin Jose S., Anoop Sreekumar R.S., "Design and Implementation of Object Tracking System Using Smart Phones," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.539-543, 2018.

There has been an exponential growth in the number of cardiovascular diseases and cancer in the present world due to unfavorable environmental factors, faulty food, stress and erroneous lifestyle. These two account for a majority of deaths worldwide. Early detection and prevention plays a remarkable role in preventing deaths. It is not an easy task for medical practitioners to instantly come to a conclusive diagnosis. Hence we can resort to data mining techniques to extract occult, foreseeable information that can be acted upon the large set of medical data. In this survey, we have presented an overview on the symptoms, their aggravating factors in various cardiac illnesses and cancer. We have also enlisted, discussed and analyzed data mining techniques such as Decision Tree, Neural Networks, and Naïve Bayes etc. This paper summarizes various review and technical journals on cardiovascular disease and cancer diagnosis and prognosis

Cardiovascular diseases, Cancer, Diagnosis, Prognosis, Data Mining

[1] D. Tomar, S. Agarwal, “A survey on Data Mining approaches for Healthcare”, International Journal of Bio-Science and Bio-Technology, Vol 5(5),pp.241-266, 2013.
[2] K. Park , “Park’s Textbook of PREVENTIVE AND SOCIAL MEDICINE”, 23rd Edition, Bhanot publishers, India, pp 363-385,2015.
[3] Colin Ogilvie, Christopher C. Evans, “An Introduction to Medical Diagnosis”, 11th Edition,Butterworth-Heinemann, Oxford, pp 201-205.
[4] S.Vijayarani, S.Sudha, “A Study of Heart Disease Prediction in Data Mining” International Journal of Computer Science and Information Technology & Security “, (IJCSITS), ISSN: 2249-9555 Vol. 2, No.5, October 2012 .
[5] M. Hejmadi, “Introduction to Cancer Biology”,ebook 2nd Edition, Bookboon publishing, pp- 7, 2010, ISBN 978-87-7681-478-6
[6] J.F. McCARTHY, K.A. Marx, P.E.Hoffman, A.G. GEE, P O’neil, M.L.Ujwal, and J Hotchkiss, "Applications of Machine Learning and High‐Dimensional Visualization in Cancer Detection, Diagnosis, and Management." Annals of the New York Academy of Sciences1020, no. 1 (2004): pp. 239-262.
[7] Mahajan & Gupta, “Textbook of PREVENTIVE AND SOCIAL MEDICINE”,4th Edition,Jaypee brothers medical publishers, India, PP 359,2013, ISBN 978-93-5090-187-8 978-
[8] S. Palaniappan, R. Awang, “Intelligent heart disease prediction system using data mining techniques”, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.8, 343-349,August 2008
[9] N. Guru, A. Dahiya, N. Rajpal, "Decision Support System for Heart Disease Diagnosis Using Neural Network", Delhi Business Review, Vol. 8, No. 1 (January - June 2007).
[10] Ramyasri M M (14CSR151), Renuka P (14CSR158) and Rajeshkumar R (14CSL275) and Kumaravel T, “Prediction of Heart Diseases Risk through Frequent Itemsets in Data Mining “3 rd National Conference on Innovative Research Trends in Computer Science and Technology (NCIRCST) ISSN: 2454-4248 Volume: 4 Issue: 3 115 – 120, 2018
[11] I. U. Said , A. H. Adam , Dr. A. B. Garko, “ASSOCIATION RULE MINING ON MEDICAL DATA TO PREDICT HEART DISEASE”, International Journal Science Technology and Managaement Vol.No.4, Issue 08, Aug 2015
[12] D. C.Sekar,& K. R. H.Rao, Predicting the heart attack symptoms using biomedical data mining techniques. International Journal of Computer Science & Applications (TIJCSA), 1(3),(2012).
[13] S. M. Alzahani, A. Althopity, A. Alghamdi, B. Alshehri, & S. Aljuaid, (2014). An overview of data mining techniques applied for heart disease diagnosis and prediction. Lecture Notes on Information Theory Vol, 2(4),December 2014
[14] V. Chaurasia , S. Pal, “A Novel Approach For Breast Cancer Detection Using Data Mining Techniques” , International Journal Of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2, Issue 1, 2014
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[16] A Bellaachia & E. Guven, “Predicting Breast Cancer Survivability Using Data Mining Techniques”, Age, Vol. 58, Issue 13, pp: 10-110, 2006
[17] M. Karabatak & M. C. Ince, “An Expert System For Detection Of Breast Cancer Based On Association Rules And Neural Network”, Elsevier: Expert Systems With Applications, Vol. 36, Issue 2, Part 2, pp: 3465-3469, 2009
[18] N. Sharma and H. Om, “Framework For Early Detection And Prevention Of Oral Cancer Using Data Mining”, International Journal of Advances in Engineering & Technology, Vol. 4, Issue 2, pp: 302-310, 2012.
[19] C. Scully, J.V. Bagan, C. Hopper, J.B. Epstein, “Oral Cancer: Current And Future DiagnosticsTechniques – A Review Article”, American Journal of Dentistry, Vol. 21, Issue 4, pp: 199-209, 2008
[20] W. T. Tseng, W. F. Chiang, S. Y. Liu, J. Roan, & C. N. Lin, “The Application Of DataMining Techniques To Oral Cancer Prognosis”, Journal Of Medical Systems, Vol.39, Issue 5, pp: 59,2015
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[23] V.Krishnaiah, Dr.G.Narsimha, Dr.N.Subhash Chandra,“Diagnosis of lung cancer prediction system using data mining classification techniques”. International Journal of Computer Science and Information Technologies, Vol. 4, Issue 1, pp.39-45, 2013.
[24] S. Patel and H. Patel, “Survey of data Mining techniques used in healthcare Domain”, International Journal of Information Sciences and Techniques, Vol. 6,pp.53-60 , 2016.
[25] National Library of Australia Cataloguing–in–Publication data: Lifestyle and cancer: knowledge, attitudes and behavior in NSW 2009 SHPN (CI) 120203, Published by the Cancer Institute NSW, pp.1-29, 2012, ISBN 978-1-74187-760-1
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[29] E.I.Papageorgiou, P.P.Spyridonos, D. Th.Glotsos, C.D.Stylios, P.Ravazoula, G.N.Nikiforidis, P.P.Groumpo, “Brain Tumor Characterization Using The Soft Computing Technique Of Fuzzy Cognitive Maps, Elsevier: Applied Soft Computing, Vol. 8, Issue 1, pp: 820-828, 2008.
[30] R.S.Santos, S.M.F.Malheiros, S.Cavalheiro, J.M.P. Oliveira,” A Data Mining System For Providing Analytical Information On Brain Tumors To Public Health Decision Makers, Elsevier: Computer Methods And Programs In Biomedicine, Vol. 109, Issue 3, pp: 269-282, 2013
[31] E. I. Papageorgiou, P. P. Spyridonos, D. T. Glotsos, C. D. Stylios, P. Ravazoula, G. N. Nikiforidis, & P. P. Groumpos, Brain Tumor Characterization Using The Soft Computing Technique Of Fuzzy Cognitive Maps, Elsevier:Applied Soft Computing, Vol. 8, Issue 1, pp: 820-828, 2008

Deepali Kamath, Anupama Ajith, Kavita Pujari, Praveena Kumari MK, "A Survey on Data Mining Techniques Applied on Cardiovascular Diseases and Cancer, Diagnosis and Prognosis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.544-550, 2018.

Cryptography is the study of mathematical techniques related to aspects of information security such as confidentiality, data integrity, entity authentication and data origin authentication. In data and telecommunications, cryptography is necessary when communicating over any unreliable medium, which includes any network particularly the internet. AES encryption is the solution for Data Encryption Standard (DES) aging problem. Rijndanel Symmetric block cipher standard version which can encrypt and decrypt plaintext 128 bits blocks using key with 128-bit, 192-bit, or 256-bit size. The Rijndael cipher has simple structure and suitable to 8-bit and 32-bit processing. The cipher has numbers round of plaintext transformation. Key length determines how many rounds to be executed. The key with 128-bit use 10 rounds, 192-bit use 12 rounds, and 256-bit use 14 rounds. For the AES algorithm, the number of rounds to be performed during the execution of the algorithm uses a round function that is composed of four different byte-oriented transformations: Sub Bytes, Shift Rows, Mix columns and Add Round Key.

Advanced Encryption Standard, Cryptography, Decryption, Encryption

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Ajit Karki, "A Review on Advanced Encryption Standard – (AES)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.551-556, 2018.

Mobile nodes are failure prone. An efficient check pointing technique and a failure recovery scheme together can make a Wireless Sensor Network fault-tolerant. For efficient recovery, information of a mobile host should be kept in an organized manner. Efficiency of a recovery scheme can be measured in terms of time and cost. Mobile nodes move randomly causing handoff. Information of a single mobile host gets scattered over a number of mobile support stations that can be at closer or further distance. Recovery time and cost primarily depend on number of mobile support stations from which information to be collected as well as distance among them. Larger the distance, longer the time for communication through message passing. Number of mobile support stations from which information to be recovered and distance among them can be delimited by keeping a Genetic Algorithm threshold value and a distance threshold value respectively in each mobile host. Recovery scheme proposed here applies both the measures. Our work optimizes both failure-free and failure-recovery operation costs.

Checkpoint, recovery, fault tolerance, genetic algorithm,etc

[1] Shilpa Gambhir, Er. Sonia Goyal, “Reliable task allocation in distributed mobile computing system with random node movement: Replication and Load sharing Approach”, IJARECE, vol. 3, pp. 659-663, 2014.
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[3] C. Huang, C. Lee, Y Yu and C Lai, “Location-aware multimedia proxy handoff over the IPv6 mobile network environment”, Journal of Systems and Software, Volume 79 , Issue 8 (August 2006) ,Pages: 1037 - 1050 ,ISSN:0164-1212
[4] R.E. Ahmed and A Khaliq, “On the Role of Base Station In Fault-Tolerant Mobile Networks”, CCECE 2004- CCGEI 2004.
[5] TY Jiang, QH Li, “An efficient recovery scheme for mobile computing system “.International Conference on Machine Learning and Cybernetics, 2003, 2-5 Nov. 2003, page(s): 2031 – 2036, Vol.4, Print ISBN: 0-7803-8131-9
[6] TYT Juang and YS Chen, “An Efficient Rollback Recovery Algorithm for Distributed Mobile Computing System”, 19th IEEE Int`l. Peformance, Computing, and Communications Conference (IPCCC-2000), Phoenix, Arizona, USA; Feb. 20~22, 2000.
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[12] R. Koo and S. Touegt, “Checkpointing and Rollback-Recovery for Distributed Systems”, ACM Fall joint computer conference, p.1150-1158, 1996
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Shilpa, Deepak Dhadwal, "A Genetic Algorithm Based Check Pointing and Failure Recovery Scheme in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.557-562, 2018.

Textual passwords are not uncommon, which are used for authentication as account based passwords. However these passwords are facing risk in the form of phishing attack, burette force attack, social engineering attack and social surfing as well. The thing consisting of authentication, verification and recognition has been known as biometrics, which is being used for human recognition. Biometric passwords replace the techniques of textual passwords and also function as alternatives. Existence of biometric systems is commonly found by using fingerprint, face, iris, etc., on the other hand its risk to duplicating a fake (For e.g.:- “fingerprint gummy finger”). Palm vein authentication is yet another modern biometric technique, which employs the vein pattern in the human palm in order to verify the person’s identity. Palm vein on classical biometric (e.g. fingerprint, iris, face). Which have merits, are a low risk of falsification, difficulty of duplicated and stability. The recent and current method which has been proposed to detect a hand vein by using Near Infrared (NIR) Light method. The Captured vein image is used in an infrared illuminator and Radon feature techniques is used to extract the feature of hand vein and feature matching algorithm is used. This is the system implemented using MATLAB.

Infrared (IR), Near Infrared (NIR), Region of Interest (ROI), Adaptive Histogram Equalization (AHE), Feature extraction, Feature Matching

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A. Jeyakalyani, N. Suguna, "Biometric Authentication System Using Palm Vein Features," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.563-566, 2018.

The uprising innovation of nanotechnology is Quantum‐dot cellular automata (QCA). Quantum Dot Cellular Automata is a new paradigm “Molecules can act as switches“. QCA is fulfilling the gap left by the conventional memory systems in consuming power. There are various types of QCAs reported till now like Metal dot QCA, Molecular QCA and Magnetic QCA, metal Dot QCA has its own limitation that it can be operated only at low temperature. Though molecular QCA s are proved to be superior in operating condition compare to Metal- Dot QCA which can be operated at room temperature it also meets its own downside that its fabrication becomes complicated. In general most of the quantum dots include hundreds or thousands of atoms with variation in their energy and wave function. So creating quantum dots with digital reliability by eliminating the variations size, shape and arrangement remains indefinable. Since we need to go for a alternative device indeed to avoid the maximum power dissipation met with the high density ICs, let us think about the Magnetic QCA. Magnetic QCA relay on the property of alignment of spins in ferromagnetic material. The word” Quantum” implies quantum mechanical nature of short range exchange interaction which leads to alignment of spins. So Magnetic QCAs are having advantages over the previous two types that it is relatively uncomplicated. So in this paper we elaborately discuss about the QCAs, Types of QCAs and their functioning and the advantages of Magnetic QCA.

QCA, Quantum, Magnetic QCA, Metal –Dot QCA, Molecular QCA

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H. Umamahesvari, "The Role of Magnetic Quantum Dot Cellular Automata In Replacing Traditional CMOS Technology," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.567-570, 2018.

The task of image enhancement is focused on restoring and clarifying the corrupted images to improve their quality, and image enhancement methods has been widely applied to numerous image analysis techniques including pattern recognition, image fusion, image segmentation and so forth. Among the various methods used to enhance the image, algorithms created from retinex theory have received more and more attention and have been commonly used in many applications. This paper describes a retinex theory based method for contrast and illuminance enhancement in images of low light or unevenly illuminated scenes. This method firstly transformsimage from RGB color space to HSV colorspace, and decomposes the value channel using dual-tree complex wavelet transform. Then, to process the lesser frequency component of the image, an improved adaptive local tone mapping method is utilized and wavelet shrinkage method and fuzzy enhancement method are applied to denoise and enhance the reconstructed and a statistical histogram optimization method is used. After that, the enhanced value channel is the image is transformed back to RGB color space. Findings from experiments support the method suggested by this paper which performs very well with enhancement and de-noise of the corrupted images.

Retinex, dual tree complex wavelet transform, adaptive local tone mapping, wavelet shrinkage, fuzzy, histogram optimization

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Badiginchala Mahalakshmi, Shaik. Taj Mahaboob, "An Efficient Retinex Image Enhancement based on 2D DTCWT," International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.571-576, 2018.