Archive

Abstract:

Smart health care devices are slowly gaining popularity because of their many advantages over conventional health care system. In the conventional approach, a patient approaches a doctor either in the clinic or hospital. Much of time is spent in patients travel and wait period before he gets approval to meet the doctor. This is much worse for a patient who lives far away and has to spend lots of time in travelling. In general, when a patient first meets the doctor for treatment, he needs to register and then get diagnosed followed by some prescription. After that the patient routinely meets the doctor again leading to travel and wait periods. This will build up lots of stress in the patient especially if he has become weak and if the patient is quite old. The doctor maintains a record of diagnosis and prescription for each patient and this record gets updated on every visit by patient. It may also happen that the doctor may not be available for consultation on certain days due to some emergency or other reasons. This paper suggests a method of handling these issues faced by patient by developing a device and a web portal. The device consists of microcontroller connected to some bio-medical sensors like Temperature, Pulse-Oximeter, ECG, etc. This device can be used to read the patient’s health data on a regular basis and then send it to the Web Server via Wi-Fi module.A Web Portal is also being developed for viewing patient’s data regularly.

Keywords:

IoT,ECG,RFID,WSN,BAN,6LOWPAN,Wi-Fi,

Refference:

I. A J Jara, M A Zamora-Izquierdo, and A F Skarmeta, “Interconnection
Framework for mHealth and Remote Monitoring Based on the Internet of
Things”, Selected Areas in Communications,IEEE Journal on, Vol. 31, pp.
47-65, 2013.
II. C Min, S Gonzalez, V Leung, Z Qian, and L Ming, “A 2G-RFID based ehealthcare
system”, Wireless Communications, IEEE, vol. 17, pp. 37-43,
2010.
III. G Broll, E Rukzio, M Paolucci, M Wagner, A Schmidt, H Hussmann
“PERCI: Pervasive Service Interaction with the Internet Of Things”, IEEE
Internet Computing, 13(6), pp 74-81, 2009
IV. G Kortuem, F Kawsar, D Fitton, V. Sundramoorthy, “Smart objects as
building blocks for the Internet of things,” Internet Computing, IEEE, vol. 14,
pp. 44-51, 2010.
V. G Sebestyen, A Hangan, S Oniga, and Z Gal, “eHealth solutions in the
context of Internet of Things,” in Automation, Quality and Testing, Robotics,
2014 IEEE International Conference on, 2014, pp. 1-6.
VI. G Yang, L Xie, M Mantysalo, X Zhou, Z Pang, L D Xu, et al., “A Health-IoT
Platform Based on the Integration of Intelligent Packaging, Unobtrusive Bio-
Sensor, and Intelligent Medicine Box”, Industrial Informatics, IEEE
Transactions on, vol. 10, pp. 2180-2191, 2014.
VII. H Fang, X Dan, and S Shaowu, “On the Application of the Internet of Things
in the Field of Medical and Health Care”, in Green Computing and
Communications (GreenCom), 2013 IEEE and Internet of Things
(iThings/CPSCom), IEEE International Conference on and IEEE Cyber,
Physical and Social Computing, 2013, pp. 2053-2058.
VIII. J Jin, J Gubbi, S Marusic, M. Palaniswami, “An information framework for
creating a smart city through Internet of Things”, IEEE Internet of Things
Journal, vol. 1, pp. 112-121, 2014.
IX. José Rouillard “Contextual QR Codes”, The Third International IEEE Multi-
Conference on Computing in the Global Information Technology, ICCGI’08,
pp 50-55, 2008.
X. L Atzori, A Lera and G Morabito, “The Internet of Things: A survey,”
Computer Networks, Vol. 54, pp. 2787-2805, 10/28/ 2010.
XI. L Jingran, C Yulu, T Kai and L Junwen, “Remote monitoring information
system and its applications based on the Internet of Things”, in BioMedical
Information Engineering, 2009. FBIE 2009. International Conference on
Future, 2009, pp. 482-485.
XII. L Xu, L Rongxing, L Xiaohui, S Xuemin, C Jiming, and L Xiaodong, “Smart
community: an internet of things application” Communications Magazine,
IEEE, vol. 49, pp. 68-75, 2011.

XIII. Michael Rohs, Beat Gfeller “Using camera-equipped mobile phones for
interacting real-world objects”, Advances in Pervasive Computing, Austrian
Computer Society (OCG); Ferscha A, Hoertner H, Kotsis G (eds.), 2004.
XIV. O Boric-Lubecke, G Xiaomeng, E Yavari, M Baboli, A Singh and V M
Lubecke, “E-healthcare: Remote monitoring, privacy, and security”, in
Microwave Symposium (IMS), 2014 IEEE MTT-S International, 2014, pp. 1-3.
XV. P Castillejo, J F Martinez, J Rodriguez-Molina and A. Cuerva,”Integration of
wearable devices in a wireless sensor network for an Ehealth application”,
Wireless Communications, IEEE, vol. 20, pp. 38-49, 2013.
XVI. P Swiatek and A Rucinski, “IoT as a service system for eHealth”, in e-Health
Networking, Applications & Services (Healthcom), 2013 IEEE 15th
International Conference on, 2013, pp. 81-84.
XVII. Punit Gupta, Deepika Agrawal, Jasmeet Chhabra, Pulkit Kumar Dhir “IoT
based Smart Health Care Kit”, International Conference on Computational
Techniques in Information and Communication Technologies (ICCTICT), pp
237 – 242 ,1-13 March 2016.
XVIII. R S H Istepanian, S Hu, N Y Philip, and A Sungoor, “The potential of
Internet of m-health Things “m-IoT” for noninvasive glucose level sensing”
in Engineering in Medicine and Biology Society,EMBC, 2011 Annual
International Conference of the IEEE, 2011, pp. 5264-5266.
XIX. R Tabish, A M Ghaleb, R Hussein, F Touati, A Ben Mnaouer, L Khriji, et al.,
“A 3G/WiFi-enabled 6LoWPAN-based U-healthcare system for ubiquitous
real-time monitoring and data logging”, in Biomedical Engineering
(MECBME), 2014 Middle East Conference on, 2014, pp. 277-280.
XX. Roy Want “RFID – A key to automating everything”, Scientific American
290, No 1, 2004, pp 56-65.
XXI. S Chen, X Zhu, S Zhang, and J Wang, “A framework for massive data
transmission in a remote real-time health monitoring system,” in Automation
and Computing (ICAC), 2012 18th International Conference on, 2012, pp. 1-
5.
XXII. Till Quack, Herbert Bay, Luc Van Gool “Object Recognition for Internet of
Things”, Internet Of Things, pp 230-246, Springer Berlin Heidelberg, 2008.
XXIII. W Weihua, L Jiangong, W Ling and Z Wendong, “The internet of things for
resident health information service platform research”, in Communication
Technology and Application (ICCTA 2011), IET International Conference
on, 2011, pp. 631-635.
XXIV. X Boyi, X Li Da, C Hongming, X Cheng, H Jingyuan, B Fenglin,
“Ubiquitous Data Accessing Method in IoT-Based Information System for
Emergency Medical Services”, Industrial Informatics, IEEE Transactions on,
vol. 10, pp. 1578-1586, 2014
XXV. Z Wei, W Chaowei, and Y Nakahira, “Medical application on internet of
things”, in Communication Technology and Application (ICCTA 2011), IET
International Conference on, 2011, pp. 660-665.

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Abstract:

Machine Fault Diagnosis is an important domain in Mechanical Engineering which concerns about finding fault in the machine parts. Among many techniques to identify and classify the faults, this paper concerns about using machine learning algorithms to distinguish healthy machines fro mtheun healthy machines. Inordertodistinguishthestateofamachine,classificationalgorithmshas to beused.The accuracy of an algorithm depends upon the pattern, that the data set follows. The suitability of the five most commonly used classification algorithm has been discussed. Various transforms can be applied to such sensor data. Here various algorithms have been tested for wave let packet transform. Thea ccuracy of the fit has been measured for all the five algorithms. Hyper-parametertuning has been done to make the fitbetter.

Keywords:

Principal Component Analysis,Support Vector Machine,Fault Prognosis,Air Compressor,

Refference:

I. Ali J, Fnaiech N, Saidi L, Chebel-Morello B, Fnaiech F (2015)
Application of empirical mode decomposition and artificial neural
network for automatic bearing fault diagnosis based on vibration signals.
Applied Acoustics89:16– 27.
II. II. C S, B D, S, Manivannan K (2014) Bearing fault diagnosis using
wavelet packet transform, hybrid PSO and support vector machine, vol
97. https://doi.org/10.1016/j.proeng.2014.12.329.
III. C W, Y R, M, Cheng Y (2016) Fault diagnosis for rotating machinery: A
method based on image processing. PLoS. ONE 11(10):1–22
IV. DH, MSC, Song G, RenL,LiH (2015)A review of damage detection methods
forwindturbineblades.SmartMaterialsandStructures24(3):033001,https://doi.org/10.1088/0964-1726/24/3/033001
V. Desmet A, et al. (2017) Leak detection in compressed air systems using
unsupervised anomaly detection techniques pp 1–10
VI. Devendiran S(2016)Vibration Based Condition Monitoring and Fault Diagnosis Technologies
for Bearing and Gear Components AReview11(6):3966–3975
VII. F D, S S, F, Pecht M (2017) Current Noise Cancellation for Bearing Fault
DiagnosisUsingTime-Shifting.IEEETransactionsonIndustrialElectronics
46:1–1
VIII. FM. Arkkio, A. Roivainen J(2014) Electrical Fault Diagnosis for an
Induction Motor Using an Electromechanical FEModel
IX. Fengtao, Song L, Zhang L, Li H (2011) Fault Diagnosis for
Reciprocating Air Compressor Valve Using P-V Indicator Diagram and
SVM
X. G Y, T Z, T, Cao L (2018) A multiscale noise tuning stochastic
resonance for fault diagnosis in rolling element bearings. Chinese
Journal of Physics 56(1):145–157
XI. H S, Ben S, Bacha K, Zeadally S (2015) Smart wireless sensor networks
for online faults diagnosis in an induction machine. Computers and
Electrical Engineering 41:226–239
XII. H Y, Lee WS, Wu CY (2014) Automated fault classification of
reciprocating compressors from vibration data: A case study on
optimization using a genetic algorithm
XIII. HeM, HeD, Bechhoefer E(2016) Using Deep Learning Based Approaches for
Bearing Fault Diagnosis with AE Sensors
XIV. JL,MW, K,SunL (2015a) Mechanical Fault Diagnosis for HV Circuit Breakers Based on
Ensemble Empirical Mode Decomposition Energy Entropy and Support Vector Machine.
https://doi.org/10.1155/2015/101757
XV. J NV, D, Kim JM (2015b) Accelerating 2d fault diagnosis of an induction
motorusingagraphicsprocessingunit.InternationalJournalofMultimedia
and Ubiquitous Engineering 10(1), https://doi.org/10.14257/ijmue.
2015.10.1.32
XVI. K Z, C X, Fang JQ, Zheng PF, Wang J (2017) Fault Feature
Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs
Network. International Journal of Rotating Machinery 2017, URL
https://doi.org/ 10.1155/2017/9602650
XVII. Kavathekar S, Upadhyay N, Kankar PK (2016) Fault Classification of
Ball Bearing by Rotation Forest Technique. Procedia Technology
23:187–192

XVIII. M,BeloiuR(2014) Faultsdiagnosisforelectricalmachinesbasedonanalysis
of motorcurrent
XIX. M, Ushakumari S (2011) Incipient fault detection and diagnosis of
induction motor using fuzzy logic, vol 2013
XX. M A, M R, M, Ehtiwesh I (2010) A combined practical approach to
condition monitoring of reciprocating compressors using IAS and
dynamicpressure. World Academy of Science, Engineering and
Technology63(3):186–192
XXI. Omid M (2016) An intelligent approach
XXII. Prakash A (2014) A review on machine condition monitoring and fault
diagnostics using wavelet transform
XXIII. Q . A S, L S, G, Shao L (2015) Vibration sensor based intelligent fault
diagnosis system for large machine units in petrochemical industries.
InternationalJournalofDistributedSensorNetworks2015,URLhttps://doi.
org/10.1155/2015/239405
XXIV. R,Sugumaran V (2015) Fault diagnosis of automobile hydraulic brake system
using statistical features and support vector machines, vol 52
XXV. R D, S S, K R, Verma NK, Salour A (2016) Generating feature sets for
fault diagnosis using denoising stacked auto-encoder. https://doi.org/10.
1109/ICPHM.2016.7542865
XXVI. RP,S,Jennions IK (2013) Rotor dynamic faults: Recent advances in diagnosis
and prognosis. International Journal of Rotating Machinery 2013,
https://doi.org/10.1155/2013/856865
XXVII. S,Zhou D (2016) Study on a New Fault Diagnosis Method Based on Combining
Intelligent. Technologies11(6):61–72
XXVIII. S E, H J, K, Shahzad T (2017a) Vibration Feature Extraction and
Analysis for Fault Diagnosis of Rotating Machinery-A Literature Survey.
Asia Pacific Journal of Multidisciplinary Research5(51):103–110
XXIX. S G, A PJ, Kulkarni JV (2015a) Fault Diagnosis of Bearing of Electric
Motor Using Wavelet Transformand Fault Classification Based on Support Vector.
Machine2(5):41–46
XXX. S L, Z, Hu K (2017b) Traction inverter open switch fault diagnosis based
onchoice-Williamsdistributionspectralkurtosisandwavelet-packetenergy
Shannonentropy.Entropy19(9),https://doi.org/10.3390/e19090504
XXXI. S M, Tan ACC, Mathew J (2015b) A review of prognostic techniques for
non- stationary and non-linear rotating systems, vol 62
XXXII. Shaheryar A, Yin XC, Ramay WY (2017) Robust Feature Extraction on Vibration Data under Deep-Learning Framework:
An Application for Fault Identification in Rotary. Machines International Journal of Computer Applications 167(4):975–8887, URL-https://pdfs.semanticscholar.org/6866/4737a162cfacf3f51d5dd3b7435f2ef9b698.pdf
XXXIII. T, Wu Z (2015) A vibration analysis based on the wavelet entropy method of a scroll compressor
XXXIV. T L, X, Tan ACC (2017a) Fault diagnosis of rolling element bearings
based on Multiscale Dynamic Time Warping. Measurement: Journal of the International Measurement Confederation,
95355(366):10–1016
XXXV. T S, M K, P, Ramachandran KI (2014) Fault diagnosis of automobile gearbox
basedonmachinelearningtechniques,vol97.URLhttps://doi.org/10.1016/
j.proeng.2014.12.452
XXXVI. T V, AlThobiani F, Tinga T, Ball A, Niu G (2017b) Single and combined
faultdiagnosisofreciprocatingcompressorvalvesusingahybriddeepbelief
network. vol 0, URLhttps://doi.org/10.1177/0954406217740929
XXXVII. Verma NK, Sevakula RK, DixitS, Salour A (2016) Intelligent Condition Based
Monitoring Using Acoustic Signals for Air Compressors. IEEE Transactions on Reliability65(1):291–309
XXXVIII. XL,S,HuJ(2017)Improving Rolling Bearing Fault Diagnosis by DS Evidence
Theory Based FusionModel
XXXIX. Y,Al-khassaweneh M (2014) Fault Diagnosis inInternal Combustion Engines
Using Extension Neural Network. IEEE Transactions on Industry
Applica-tions61(3):1434–1443
XL. Y, Benjelloun K (2016) Sleeve Bearing Fault Diagnosis and
Classification Zhao R (2016) Deep Learning and Its Applications to
Machine Health Monitoring: A Survey. vol 14, pp 1–14

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Abstract:

User latency plays a significant role in e-commerce. This latency can be minimized by a priori predicting and fetching probable web pages for web users to run the e-commerce activities. Those prediction techniques are normally supported by clustering, classification and some association rules based on the data set of web logs of navigations, searching and attached web links with the e-commerce web pages. This paper proposes an integrated web page prediction technique by analyzing web users’ previous navigational behavior. K-means clustering and latest substring association rule are considered for developing the proposed method of ecommerce web page prediction. The proposed method is evaluated by analyzing the precisions values of the output clusters using the proposed prediction technique.

Keywords:

Web page prediction,K-Means Clustering,Latest Substring Association Rule,Subsequence Association Rule,Substring Association Rule,

Refference:

I. A. Anitha , “A New Web Usage Mining Approach for Next Page Access
Prediction”, Int. Journal of Computer Applications, 8 (11), 7-10,2010.
II. C. Gutierez-Soto, “On The Reuse of Past Searches in Information Retrieval:
Study of two Probabilistic Algorithm”, Int. Journal of Information System
Modeling and Design. 6 (2). 72-92,2015.
III. C. Ray, and P. Das, “An Outlier Detection Based on Frequent Pattern”, Int.
Journal of Engineering Research & Technology (IJERT), 2(11), 2810-2814,
2013.
IV. G. Khodabandelou, C. Hug, and C. Salinesi, “Mining User’s Intents from
logs”, Int. Journal of Information System Modeling and Design, 6(2), 43-71,
2015.

V. G. Shrivastava , K. Sharma , and A. Bawankan, “A new framework semantic
web technology based e-learning”, In Environment and Electrical
Engineering (EEEIC), 2012 11th International Conference on (pp. 1017-
1021).
VI. G. Shrivastava, and V. Bhatnagar, “Secure Association Rule Mining for
Distributed Level Hierarchy in Web”, International Journal on Computer
Science and Engineering, 3(6), 2240-2244,2011.
VII. G. Shrivastava, and V. Bhatnagar, “Analyses of Algorithms and Complexity
for Secure Association Rule Mining of Distributed Level Hierarchy in Web”,
International Journal of Advanced Research in Computer Science, 2(4), 2011.
VIII. I. Chhabra, G. Suri, “Data Science and Knowledge Discovery through Data
Mining Paradigms”, Journal of Mechanics of Continua and
Mathematical Sciences, 14(2), 167-173, 2019
IX. J. Chembath, and T. Fredrik, “Ensemble System Using Hybrid Next Web
Page Prediction Algorithm Combining Clustering, Markov Model With
Associative and Longest Common Subsequence Classifiers”, Int. Journal of
Pure and Applied Mathematics, 119(12), 5329-15339,2018.
X. K. Avrachenkov, V. Dobrynin , D. Nemirovsky, S. K. Pham and E.
Smirnova, “Pagerank based clustering of hypertext document collections ”, In
Proc. of the 31st Annual Int. ACM SIGIR Conf. on Research and
Development in Information Retrieval, 873-874, ACM,2008.
XI. K. Sharma, G. Shrivastava, and D. Singh, “Risk Impact of Electronics-
Commerce Mining: A Technical Overview”, International Conference on
Computing Communication and Information Technology, pp 96-100, 2012.
XII. K. Sharma and G. Shrivastava, “Public Key Infrastructure and Trust of Web
Based Knowledge Discovery”, International Journal of Engineering, Sciences
and Management, 4(1), 56-60,2011.
XIII. K. Sharma, G. Shrivastava, and V. Kumar, “Web mining: Today and
tomorrow”. Electronics Computer Technology (ICECT), 2011 3rd
International Conference on (Vol. 1, pp. 399-403).
XIV. K. Sharma, and V. Bhatnagar, “Private and Secure Hyperlink Navigability
Assessment in Web Mining Information System”, International Journal on
Computer Science and Engineering, 3(6), 2245-2250,2011.
XV. O. P. Mandal , and H. K. Azad, “Web Access Prediction Model Using
Clustering and Artificial Neural Network”, Int. Journal of Engineering
Research and Technology, 3(9),195-199, 2014.
XVI. P. Bhart , D. Shukla , D. S. Saini and A. Gaur, “Composite Model of Web
Page Prediction Using Page Ranking Algorithm and Markov Model”, 4th Int.
Conf. on Recent development in Engineering Science, 125-13,2017.

XVII. P. Sampath and M. Prabhavathy, “Web Page Access Prediction using Fuzzy
Clustering by Local Approximation Memberships (FLAME) Algorithm”,
ARPN Journal of Engineering and Applied Sciences, 10(7), 3217-3220,
2015.
XVIII. R. Geetharamani, P. Revathy and S. G. Jacob, “Prediction of Users Webpage
Access Behaviour Using Association Rule Mining”, Sadhana, 40(8), 2353-
2365,2015.
XIX. R. P. Chatterjee , M. Ghosh , M. K. Das , and R.Bag, “Webpage Prediction
Using Latest Substring Association Rule Mining”, In Foundations and
Frontiers in Computer, Communication and Electrical Engineering: Proc. of
the 3rdInt. Conf. C2E2, Mankundu, West Bengal, India, 15th-16th January,
2016. (p. 157), CRC Press.
XX. Sunita, V. Rana, “Improved probable clustering based on data
dissemination for retrieval of web URLs”, Journal of Mechanics of
Continua and Mathematical Sciences, 14(5), 285-294, 2019.

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Abstract:

The concepts of q-analysis has numerous applications in different subfields of science such as optimal control, ordinary fractional calculus, geometric function theory, qintegral and q-difference equations. In this paper we define certain subclasses of harmonic univalent functions in the open unit disk U  {zC : | z |  1} by utilizingqdifferential operator and obtain coefficient bounds and extreme points for the functions in this class.

Keywords:

q-differential operator,Harmonic function,Salagean operator,univalent function,

Refference:

I. J. Clunie as well as T. Sheil-Small, Harmonic univalent functionalities, Ann.
Acad. Sci. Fenn.Ser. A I Mathematics. 9 (1984 ), 3-25.
II. J. M. Jahangiri, Accordant features starlike in the device disk, J. Mathematics.
Anal. Appl. 235( 1999 ), no. 2, 470-477.
III. J. M. Jahangiri, Harmonic univalent features determined next to q-calculus
drivers, Int. J.Mathematics. Anal. and also Appl. 5 (2018 ), no. 2, 39-43.
IV. J. M. Jahangiri, G. Murugusundaramoorthy as well as K. Vijaya, Salagean-type
harmonicunivalent functionalities, South west J. Pure Appl. Arithmetic. (2002 ),
no. 2, 77-82.
V. H. Lewy, On the non-vanishing of the Jacobian in a specific one-to-one
applyings,Bull. Amer. Mathematics. Soc. 42 (1936 ), no. 10, 689-692.
VI. S. D. Purohit and R. K. Raina, Certain subclasses of analytic functions
associated with fractional q-calculus operators, Math. Scand. 109 (2011), no. 1,
55-70.
VII. B. Ravindar and R. Bharavi Sharma, On a subclass of harmonic univalent
functionsassociated with the differential operator, International Journal of
Engineering and Technology (UAE). 7 (2018), no. 3.3, 146-151.
VIII. B. Ravindar and R. Bharavi Sharma and N. Magesh, On a subclass of harmonic
univalent functions defined by Ruscheweyh q- differential operator, AIP
Conference Proceedings. 2112 (2019), 020018.
IX. R. Bharavi Sharma and B. Ravindar, On a subclass of harmonic univalent
functions defined by convolution and integral convolution, International Journal
of Pure and Applied Mathematics, 117 (7) 2017, pp. 135-145.
X. R. Bharavi Sharma and B. Ravindar, On a subclass of harmonic univalent
functions, Journal of Physics: Conf. Series 1000 (2018) 012115, doi:
10.1088/1742- 6596/1000/1/012115.

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Abstract:

Everyone around the globe would like to be linked flawlessly anytime, anywhere through the best network. The 4G network must have the capability to offer high information move rates, a premium of services and also smooth movement. In 4G, there is a sizable range of heterogeneous networks. The users for a variety of treatments would like to use different networks on the manner of their desires like a living, higher schedule and higher transmission capacity. When relationships need to shift in between various systems for performance as well as more top accessibility causes, the seamless vertical handoff is essential. To provide a systematic comparison, lately released VHD formulas have been categorized right into four significant classes depending upon the vital handover decision standard made use of, i.e. RSS located protocols, bandwidth located methods, cost feature-based algorithms, as well as the combination algorithms.

Keywords:

4G network,heterogeneous networks,handover decision,combination algorithms,

Refference:

I. Ajay BabuSriramoju, Dr. S. ShobanBabu, “Study of Multiplexing Space and
Focal Surfaces and Automultiscopic Displays for Image Processing” in
“International Journal of Information Technology and Management”Vol V, Issue
I, August 2013 [ ISSN : 2249-4510 ]
II. AnushaMedavaka, P. Shireesha, “Review on Secure Routing Protocols in
MANETs” in “International Journal of Information Technology and
Management”, Vol. VIII, Issue No. XII, May-2015 [ISSN : 2249-4510]
III. AnushaMedavaka, P. Shireesha, “Classification Techniques for Improving
Efficiency and Effectiveness of HierarchicalClustering for the Given Data Set” in
“International Journal of Information Technology and Management”, Vol. X,
Issue No. XV, May-2016 [ISSN : 2249-4510]
IV. Bria, F. Gessler, O. Queseth, R. Stridh, M. Unbehaun, J. Wu, J.
Zander, ―4th-generation wireless infrastructures: scenarios and research
challenges,‖ Personal Communications, IEEE [see also IEEE Wireless
Communications], Volume:8, Issue:6, Dec.2001, pp:25 – 31
V. Dr. ShobanBabuSriramoju, “A Review on Processing Big Data” in “International
Journal of Innovative Research in Computer and Communication Engineering”
Vol-2, Issue-1, January 2014 [ ISSN(online) : 2320-9801, ISSN(print) :
2320-9798 ]
VI. K. R. Santhi, V. K. Srivastava, G. SenthilKumaran, A. Butare, ―Goals of true
broad band’s wireless next wave (4G-5G),‖ Vehicular Technology Conference,
2003. VTC 2003-Fall. 2003 IEEE 58th , Volume: 4 , 6-9 Oct. 2003, Pages:2317 –
2321 Vol.4
VII. MauriRao. “4G wireless Technology”, NCNTE-2012AT C.R. I. T., Vashi, Navi
Mumbai, feb.24-25,2012.
VIII. Muditbhalla. &Anandbhalla. “Genaration of mobile wireless technology” : A
survey,’ IEEE Trans .(0975-8887) vol. 5- No.4, Augest 2010.
IX. Pramod Kumar P, Thirupathi V, Monica D, “Enhancements in Mobility
Management forFuture Wireless Networks”, International Journal of Advanced
Research in Computer and Communication Engineering, Vol. 2, Issue 2,
February 2013
X. Pramod Kumar P, CH Sandeep, Naresh Kumar S, “An Overview of the Factors
Affecting Handovers and EffectiveHighlights of Handover Techniques for Next
GenerationWireless Networks”, Indian Journal of Public Health Research &
Development, November 2018, Vol.9, No. 11

XI. Pramod Kumar P and Sagar K, “A Relative Survey on Handover Techniques in
Mobility Management”, IOP Conf. Series: Materials Science and Engineering
594 (2019) 012027, IOP Publishing, doi:10.1088/1757-899X/594/1/012027
XII. P. Pramod Kumar, K. Sagar, “Vertical Handover Decision Algorithm Based On
Several Specifications in Heterogeneous Wireless Networks”, International
Journal of Innovative Technology and Exploring Engineering (IJITEE),
Volume-8, Issue-9, July 2019
XIII. ShobanBabuSriramoju, Dr. Atul Kumar, “An Analysis around the study of
Distributed Data Mining Method in the Grid Environment : Technique,
Algorithms and Services” in “Journal of Advances in Science and Technology”
Vol-IV, Issue No-VII, November 2012 [ ISSN : 2230-9659 ]
XIV. SugandhiMaheshwaram, S. ShobanBabu, “An Overview towards the Techniques
of Data Mining” in “RESEARCH REVIEW International Journal of
Multidisciplinary”, Volume-04, Issue-02, February-2019 [ISSN : 2455-3085]

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Abstract:

Pulsatile flow of a Saffman’s dusty fluid through a two dimensional constricted conduit in the existence of magnetic field is investigated. Perturbation solutions have been obtained under long wave length approximation and closed form expressions have been derived for stream function, velocities of solid and fluid particles, pressure distribution and shear stress. It is found that the streamlines get altered as magnetic parameter rises. The shear stress of the fluid acting on the wall increases with magnetic parameter but the pressure decreases.

Keywords:

Pulsatile Flow,Dusty Fluid,Constricted channel,

Refference:

II. A. H. Nayfeh, Oscillating Two-Phase Flow through a Rigid Pipe, AIAA J., 4,
1868-1870,1966.
III. D. K. Wagh and U. D. Tapi, Investigation of the pulsatile flow of a suspension
in the region of mild stenosis, Ind. J. Theor. Phy., 44, 1-4, 1996.

IV. G. Ravi Kiran, G. Radhakrishnamacharya, Effect of homogeneous and
heterogeneous chemical reactions on peristaltic transport of an MHD
micropolar fluid with wall effects, Math. Meth. Appl. Sci., 39, 1349-1360,
2016.
V. D. Srinivasacharya, G. Radhakrishnamacharya and Ch. Srinivasulu, The effects
of wall properties on peristaltic transport of a dusty fluid, Turkish J. Eng. Env.
Sci., 32, 357-365, 2008.
VI. G. Radhakrishnamacharya, Pulsatile Flow of a Dusty Fluid through a
Constricted Channel, ZAMP, 29, 217-225, 1978.
VII. G. Ravi Kiran et al., An Enhanced Study of Computational Fluid Dynamics,
Ind. J. Pub. Heal. Res. Dev., 9(11), 41-48, 2018.
VIII. Kumar M, Reddy GJ, Ravi Kiran G, Aslam MMA and Beg OA, Computation
of entropy generation in dissipative transient natural convective viscoelastic
flow, Heat Trans. Asian Res., 48(3), 1067-1092, 2019.
IX. N. A. S.Afifi and N.S.Gad, Interaction of peristaltic flow with pulsatile
magneto-fluid through a porous medium, Acta Mech., 149, 229-237, 2001.
X. P. G. Saffman, On the Stability of a Laminar Flow of a Dusty Gas, J. Fluid
Mech., 13,120-128,1962.
XI. R. K. Gupta and S.C.Gupta, Flow of a Dusty Gas through a Channel with
Arbitrary Time Varying Pressure Gradient , ZAMP, 27, 119-125,1976.

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Abstract:

GANs have been commonly examined as a result of their massive prospect for uses, including picture and also perspective computer, speech and language handling, etc. In this particular assessment report, our company recap the highly developed of GANs as well as look into the future. The aim of this specific paper is actually to deliver a review of GANs for the signal handling neighborhood, making use of familiar examples and principles where possible. In addition to determining different procedures for instruction as well as constructing GANs, we also point to remaining obstacles in their theory and treatment. This paper offers a working attribute of Gan's and even short contrast of gan variants.

Keywords:

Gan,Gan variants,generative modeling,

Refference:

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Applications” Vol 1, Issue 1,Jan-Mar 2014 [ ISSN : 2349-0020 ].
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networks. IEEE TVCG, 24(1): 667–676, Jan 2018.3
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convolutional networks: Visualising image classification models and saliency maps.
In ICLR Workshop, 2014.3
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Vector Space”, International Journal of Innovative Technology and Exploring
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Abstract:

An ideal free space or an absolute free space is devoid of any mass and / or energy. According to scientific discovery on astronomy, an ideal free space or an absolute free space does not exist, thus, it is a theoretical abstraction only, can be taken as reference condition (an ideal condition) for evaluation of the nature of space – time. This paper focuses on the evaluation of space – time; space – time is linear (straight horizontal line at absolute free space) in space – time plane. Space – time is non – linear (a curvature) in space – time plane. A general space – time equation is proposed and its simulation results are analyzed with proper reasoning and conclusion is derived based on the theory proposed and simulation results outcome.

Keywords:

Absolute free space,Astronomy,Space – time plane,Linear and Non – linear,Simulation,

Refference:

I. Roger Penrose, “Cycles of Time”, Vintage Books, London, pp. 50-56.
II. Stephen Hawking, “The Beginning of Time”, A Lecture.
III. Stephen Hawking, “A Briefer History of Time”, Bantam Books, London, pp.
1-49.
IV. Stephen Hawking, “Black holes and Baby Universes and other essays”,
Bantam Press, London 2013, ISBN 978-0-553-40663-4
V. Stephen Hawking, “The Grand Design”, Bantam Books, London 2011
VI. Stephen Hawking, “A Brief History of Time”, Bantam Books, London 2011,
pp. 156-157. ISBN-978-0-553-10953-5
VII. Stephen Hawking, “The Universe in a Nutshell”, Bantam Press, London
2013, pp. 58-61, 63, 82-85, 90-94, 99, 196. ISBN 0-553-80202-X
VIII. Stephen Hawking, “A stubbornly persistent illusion-The essential scientific
works of Albert Einstein”, Running Press Book Publishers, Philadelphia,
London 2011.
IX. Stephen Hawking, “Stephen Hawking’s Universe: Strange Stuff Explained”,
PBS site on imaginary time.

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Abstract:

Metacognitive skills are the driving force behind mathematical learning. It is an element that supports the learning process and improves mathematics problemsolving skills. Metacognitive skills developments will ensure students manage their learning well. However, due to technological advancements and the need for expertise and skillful, transformations of teaching are essential to address the industrial needs. The creating and development of metacognitive skills are seen to be more significant through integrated technology teaching. This review paper will discuss teaching practices based on metacognitive strategies that can integrate with technology as an element of intervention and injection in enhancing students' understanding, mastery and achievement. Studies around 2000 and up to date have been explored based on approaches, methods, techniques, and practices of metacognitive strategies implemented. A total of 15 articles were selected through a search of databases such as Google Scholar, Eric, Science direct, Elsevier, Springer Link and more. Snowball methods are also implemented to improve article search. It can be concluded that technology elements will be excellent mediators for improving metacognitive skills while also producing meaningful learning. Thus, stakeholders should ensure that in developing a quality teaching and learning approach, metacognitive strategies cannot be overlooked and significantly integrated with technology that will further enhance student learning and achievement especially in critical subjects likes mathematics.

Keywords:

Metacognitive Skill,Integrated Technology,Metacognitive Strategies,Student’s Mastery,Mathematic Learning,

Refference:

I. A.B. Festus, “Activity -Based Learning Strategies in the Mathematics
Classrooms”. Journal of Education and Practice,Vol.4, No.13.2013.
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Creation Of Dynamic Geometry Sketches To Understand Trigonometric
Relationships”. Contemporary Issues in Technology and Teacher Education,
18(3), pp 494-507.2018.
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Students’ Metacognitive Skill Through Guided Inquiry”. IOP Publishing
.Journal of Physics: Conf. Series, 954 (2018) 012022 Doi :10.1088/1742-
6596/954/1/012022.
IV. A. Panaoura, A. Gagatsis&A. Demetriou, “An Intervention To The
Metacognitive Performance: Self-Regulation In Mathematics And
Mathematical Modeling”. ActaDidacticaUniversitatisComenianae
Mathematics, Issue 9, 2009, pp. 63−79.2009.
V. A. Suriyon, M. Inprasitha&K. Sangaroon, “Students’ Metacognitive
Strategies in the Mathematics Classroom Using Open Approach”.
Psychology.2013. Vol.4, No.7, 585-591. Published Online July 2013 in
SciRes (http://www.scirp.org/journal/psych).2013.
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10.2017.
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2470-6353.Spring.2016
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3_13.2013.

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XXVIII. R. Eyyam, &H.S. Yaratan, “Impact Of Use Of Technology In Mathematics
Lessons On Student Achievement And Attitudes”. Social Behavior and
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The Role Of Teacher Preparation Programs”. The Mathematics Educator,
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Answer” To “Understanding How To Address Current And Future
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1582.2015.

XXXV. T.O. Nelson &L. Narens, “Metamemory: A Theoretical Framework And
New Findings”. Psychology Of Learning And Motivation, Volume 26, 1990,
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32.2014.
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Abstract:

The term intelligence refers to the ability to acquire and apply different skills and knowledge to solve a given problem. The current wave of technological change based on advancements in artificial intelligence (AI) has created widespread fear of job losses and further rises in inequality. Artificial intelligence in the last two decades has greatly improved performance of the manufacturing and service systems. Study in the area of artificial intelligence has given rise to the rapidly growing technology known as expert system. This paper will explore the future predictions for artificial intelligence and based on which potential solution will be recommended to solve it within next decades.

Keywords:

AI,ML,Characteristics,

Refference:

I. Anusha Medavaka,P. Shireesha, “Optimal framework to Wireless
RechargeableSensor Network based Joint Spatial of theMobile Node” in
“Journal of Advances in Science and Technology”, Vol. XI, Issue No. XXII,
May-2016 [ISSN : 2230-9659]
II. Anusha Medavaka,“Enhanced Classification Framework on SocialNetworks” in
“Journal of Advances in Science and Technology”, Vol. IX, Issue No. XIX,
May-2015 [ISSN : 2230-9659]

III. Anusha Medavaka,P. Shireesha, “A Survey on TraffiCop Android Application”
in “Journal of Advances in Science and Technology”, Vol. 14, Issue No. 2,
September-2017 [ISSN : 2230-9659]
IV. B. M. Lake, T. D. Ullman, J. B. Tenenbaum, and S. J. Gershman, “Building
machines that learn and think like people,” Behavioral and Brain Sciences, vol.
40, 2017.
V. Charles Weddle, Graduate Student, Florida State University “Artificial
Intelligence and Computer Games”, unpublished.
VI. C.Sampada,, et al, “Adaptive Neuro-Fuzzy Intrusion Detection Systems”,
Proceedings: International Conference on Information Technology: Coding and
Computing (ITCC‟04),2004.
VII. E. Ohn-Bar and M. M. Trivedi, “Looking at humans in the age of self-driving
and highly automated vehicles,” IEEE Transactions on Intelligent Vehicles, vol.
1, no. 1, pp. 90–104, 2016.
VIII. H. Cuaya´ huitl, S. Keizer, and O. Lemon, “Strategic dialogue
management via deep reinforcement learning,” arXiv preprint
arXiv:1511.08099, 2015.
IX. J. Wei, H. Liu, G. Yan, and F. Sun, “Robotic grasping recognition using
multi-modal deep extreme learning machine,” Multidimen- sional Systems and
Signal Processing, vol. 28, no. 3, pp. 817–833, 2017.
X. Pramod Kumar P, Thirupathi V, Monica D, “Enhancements in Mobility
Management for Future Wireless Networks”, International Journal of Advanced
Research in Computer and Communication Engineering, Vol. 2, Issue 2,
February 2013
XI. Pramod Kumar P, CH Sandeep, Naresh Kumar S, “An Overview of the Factors
Affecting Handovers and Effective Highlights of Handover Techniques for
Next Generation Wireless Networks”, Indian Journal of Public Health
Research & Development, November 2018, Vol.9, No. 11
XII. Pramod Kumar P and Sagar K, “A Relative Survey on Handover Techniques in
Mobility Management”, IOP Conf. Series: Materials Science and Engineering
594 (2019) 012027, IOP Publishing, doi:10.1088/1757-899X/594/1/012027

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Abstract:

The difficulty of fusion splicing hollow-core photonic Crystal fibers (HCPCFs) and solid-core (SC-PCFs) to conventional step index single mode fiber (SMF) has severely limited the implementation of PCFs. To make PCFs morefunctional, we have developed a method for splicing HC-PCF and SC-PCF toa SMF using a commercial arc splicer. A repeatable, robust, low-loss splice between the PCFs and SMF is demonstrated. In this paper, comprehensive theoretical, simulation and empirical -MZI based on splicing PCF between two single mode fibers. Adopting of MZI based on SMF and PCF is presented. Theoretical model of computing MFD and relative hole size is used to investigate losses with respect to splicing region. In addition, modeling of MZI using Opti Bpm yields a flexible solution to investigate the splicing effects and finding the optimum point of losses. Both MZI based on SC-PCF and HC-PCF are used in this article. In this section, optimization of splice loss of joints between PCF and SMF is carried out. For the analysis, we use two solvers OptiBPM and OptiMode, and codes written by MATLAB software.

Keywords:

Fusion splicing fibers,microstructure fabrication,photonic crystal fiber,

Refference:

I. A. D. Yablon and R. T. Bise, “Low-loss high-strength microstructured fiber
fusion splices using GRIN fiberlenses,” IEEE Photon. Technol. Lett. 17(1),
118–120 (2005).
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for single-mode fiber splicing withoutcore axis alignment,” J. Lightwave
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T. A. Birks, and P. St. J. Russell,“Highly birefringent photonic crystal fibers,”
Opt. Lett. 25(18), 1325–1327 (2000).
IV. B. Bourliaguet, C. Paré, F. Emond, A. Croteau, A. Proulx, and R. Vallée,
“Microstructured fiber splicing,” Opt.Express 11(25), 3412–3417 (2003).
V. T. S. Mansour, and F. M.Abdulhussein, “Dual measurements of pressure and
temperature with fiber Bragg grating sensor,” Al-Khwarizmi Engineering
Journal11(2), 86–91 (2015).
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numerical-aperture conversion,” Opt. Lett.26(14), 1042–1044 (2001).
VII. G. Fu, W. Jin, X. Fu, and W. Bi, “Air-holes collapse properties of photonic
crystal fiber in heating process by CO2laser,” IEEE Photon. Jour. 4(3), 1028–
1034 (2012).
VIII. J. C. Knight, “Photonic crystal fibres,” Nature 424(6950), 847–851 (2003).
IX. F. Q. Mohammed, and T. S. Mansour, “Design and Implementation Tunable
Band Pass Filter based on PCF-Air Micro-cavity FBG Fabry-Perot
Resonator,” Iraqi Journal of Laser. 18(1), 13–23 (2019).
X. J. H. Chong and M. K. Rao, “Development of a system for laser splicing
photonic crystal fiber,” Opt. Express11(12), 1365–1370 (2003).
XI. T. S. Mansour, and D. H.Abbass, “Chemical Sensor Based on a Hollow-Core
Photonic Crystal Fiber,” Iraqi Journal of Laser. 12(A), 37–42 (2013).
XII. J. Lægsgaard and A. Bjarklev, “Reduction of coupling loss to photonic
crystal fibers by controlled hole collapse: anumerical study,” Opt. Commun.
237(4-6), 431–435 (2004).

XIII. J. T. Kristensen, A. Houmann, X. M. Liu, and D. Turchinovich, “Low-loss
polarization-maintaining fusion splicingof single-mode fibers and hollowcore
photonic crystal fibers, relevant for monolithic fiber laser
pulsecompression,” Opt. Express 16(13), 9986–9995 (2008).
XIV. J. T. Lizier and G. E. Town, “Splice losses in holey optical fiber,” IEEE
Photon. Technol. Lett. 13(3), 466–467(2001).
XV. L. M. Xiao, M. S. Demokan, W. Jin, Y. P. Wang, and C. L. Zhao, “Fusion
splicing photonic crystal fibers andconventional single-mode fibers:
microhole collapse effect,” J. Lightwave Technol. 25(11), 3563–3574 (2007).
XVI. L. Xiao, W. Jin, and M. S. Demokan, “Fusion splicing small-core photonic
crystal fibers and single mode fibers byrepeated arc discharges,” Opt. Lett.
32(2), 115–117 (2007).
XVII. M. L. V. Tse, H. Y. Tam, L. B. Fu, B. K. Thomas, L. Dong, C. Lu, and P. K.
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of hollow-core photonic bandgapfibers for gas-filled fiber cells,” Opt.
Express 14(21), 9576–9583 (2006).
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XXII. Z. Xu, K. Duan, Z. Liu, Y. Wang, and W. Zhao, “Numerical analyses of
splice losses of photonic crystal fibers,”Opt.Commun. 282(23), 4527–4531
(2009).

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Abstract:

In recent days the fast-growing disease in most of the world's is breast cancer especially in women and, according to global statistics, represents a different level of cases that are hitting cancer and illnesses associated with related diseases, rendering it a major public health issue currently in the community. The diagnosis and treatment for this significantly contributed by the machine learning techniques that can be applied for patient data to detect the cancer stage at earlier stages can help patients receive appropriate medical treatment. In this paper, four classification methods have been used in the context of Bayes Net, Adaboost, Simple Logistic and Stochastic Gradient Descent, successfully. The primary goal is to test in terms of accuracy, uncertainty matrix, MAE and RMSE, consistency in the identification of information concerning efficiency and effectiveness of each algorithm.

Keywords:

Classification,Machine learning,Stochastic Gradient Descent,Breast cancer,

Refference:

I. B.Kranthi kiran, Padmaja.Pulicherla, Classification and Enrichment of Unlabeled Feedback Data using Machine Learning. International Journal of Engineering and Advanced Technology,ISSN: 2249 – 8958, Volume-9 Issue-1, October 2019.
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VI. Padmaja.Pulicherla, Image Map: Alternative for Password Based Authentication, International Journal of Recent Technology and Engineering, ISSN: 2277-3878, Volume-8 Issue-3, September 2019.
VII. Padmaja.Pulicherla, Retrieving Songs By Lyrics Query Using Information Retrieval, International Journal of Engineering and Advanced Technology,ISSN: 2249 – 8958, Volume-8 Issue-6S, August 2019.
VIII. Stéfan van der Walt, S Chris Colbert, and Gael Varoquaux. 2011. The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering 13, 2 (2011), 22–30.
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Abstract:

Cancer is a disease which is usually happens among the individuals everywhere throughout the world. There are numerous reasons to happen the malignant growth like as various habitats, environmental disorders and so forth. Cancer growth being identified at beginning periods can saves a large number of peoples, if viable cure is specified. It can make harm any piece of body. Generally the cancer occurs in breast of ladies. When a breast cells divide rapidly, it creates a group of mass which is called tumor . It is very difficult to detect the breast cancer tumor, it is very challenging task. Also the structure of the cancer cells are very complicated. In this article a prediction of breast cancer is present. In this a deep learning support-vector-method (D-SVM) is used to identify the breast cancer tumor. Also, In a early stages of an mammographic cancer a segmentation to threshold method is used. For the classification and for the feature extraction purpose this DSVM method is used. In this method we integrates conventional support vector machine (SVM) & classifier deep-neural-network. Likewise, probability of the lump to differentiate its sort is additionally taken in this paper for example amiable, suspicious or harmful.

Keywords:

Malignant,Image Processing,Support Vector Machine,Feature Extraction,Deep Neural Network,

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Abstract:

The present work reports an experimental investigation for the convective boiling heat transfer of R-134a in vertical tube filled with metal foam. High porosity (0.95-0.98) with PPI (40-80) metal foams (open-cell) are being considered to improve heat transfer process. Both of hydrodynamic and heat thermal performance are investigated. The results indicate that the metal foams significantly increases both heat transfer coefficient and Nusselt number but at the expense of increasing the pressure drop with mass flux rang 3-40 kg/m2.s. New correlations are proposed to predict the pressure drop and Nusselt number and show good agreements with previous experimental and numerical works.

Keywords:

Metal Foam,Forced Convection,Boiling,Experimental Study,Pressure Drop,Vertical Tube,

Refference:

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Abstract:

The concentration of carbon monoxide (CO) gas was measured at different traffic stressed areas. This study aims to find out the air quality CO concentration in the city of Karachi, Pakistan from 2002 to 2018. More than 300 sites were observed in the year 2002 and 2018. Those observations were segregated with respect of type of the day, time of the day and, at different elevations. Type of the day is then categories on weekdays and weekends. Time of the day considered as morning, afternoon and, evening. Elevations of observation were taken as 3.0 feet and 4.5 feet above the ground. A CO Index was also checked for every combination. Geographic Information System (GIS) maps were also crafted for every combination of days, times and, heights to visualize the situation. At, 3.0 feet height for both cases of working and weekdays it is observed that CO concentration is nearly half of that of 2002. At the elevation of 4.5 feet it is also going down but about 10% as compared to 2002. Even after having a decrement trend the area under study is unhealthy for living. CO concentration was then predicted for years 2020, 2022 and 2025. Even have a decrement trend, the living condition was not good for any of the projected year for time of the day and type of the day. The main reason for having a decrement pattern is changing fuel type and removal of old carriage buses.

Keywords:

CO Concentration,Karachi Metropolis,Air Quality Index,Trafficrelated air Pollution,

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