Download - ijcer

More documents

Recommendations

Info

Ni-Based Cr Alloys and Grain Boundaries Characterization III. CONCLUSIONS The study shows grain boundary morphology can be related to the amount of carbon present in nickel base alloys. Grain boundary serration becomes more pronounced with increasing carbon content. With increasing carbon content, grain boundary morphology changed from planar in Hastelloy C22 to serrate with rounded features in Inconel 600 alloy and to serrated with saw-teeth like appearance in Inconel 601. REFERENCES [1] K.J. Kim, J. Ginsztler, S.W. Nam, The Role of Carbon on the Occurrence of Grain Boundary Serration in an AISI 316 Stainless Steel during Aging Heattreatment, Materials Letters, 59:1439–1443, 2005. [2] J.M. Larson and S. Floreen, Metallurgical Factors Affecting the Crack Growth Resistance of a Superalloy, Metallurgical Transactions A, 8: 51-53, 1977. [3] T.M. Maccagno, A.K. Koul, J.-P. Immarigeon, L. Cutter, and R. Allem, Microstructure, Creep Properties, and Rejuvenation of Service-Exposed Alloy 713C Turbine Blades, Metallurgical Transactions A. 21: 3115-3125, 1990. [4] M. Yamazaki and S. Hori, On the Baryon Mass Levels II, Progress of Theoretical Physics, 36:1035-1043:1966. [5] M. Enomoto, H. Harada, M. Yamazakii , Calculation of γ′/γ Equilibrium Phase Compositions in Nickel-Base Superalloys by Cluster Variation Method, Calphad. 15:143–158, 1991. [6] M. Tanaka, H. Iizuka, and F. Ashihara, Effects of Serrated Grain Boundaries on the Crack Growth in Austenitic Heat-Resisting Steels during high-temperature creep, Journal of Materials Science, 23:3827-3832, 1988. [7] K.J. Kim, H. Hong, K. Min, S.W. Nam, Correlation between the Carbide Morphology and Cavity Nucleation in an Austenitic Stainless Steels under Creep-Fatigue, Materials Science and Engineering A, 387–389:531–535, 2004. [8] K. S. Min, and S. W. Nam, Correlation between Characteristics of Grain Boundary Carbides and Creep–Fatigue Properties in AISI 321 Stainless Steel, Journal of Nuclear Materials, 322:91–97, 2003. [9] H.U. Hong, S.W. Nam, Z. Crystallography Investigation on the Serrated Grain Boundary in an AISI 316 Stainless Steel, Journal of Nuclear Materials, 393:249–253, 2009. [10] A.K. Koul and R. Thamburaj, Serrated Grain Boundary Formation Potential of Ni-Based Superalloys and Its Implications, Metallurgical Transactions A, 16:17-25, 1985. [11] A.M. Donald, L.M. Brown, Grain Boundary Faceting in Cu-Bi Alloys, Acta Metallurgica, 27:59–61, 1979. [12] T.E. Hsieh, R.W. Balluffi, Experimental Study of Grain Boundary Melting in Aluminum, Acta Metallurgica, 37:1637–1644, 1989. [13] D.E. Luzzi, High-Resolution Electron Microscopy Observations of Faceted Grain Boundaries and Twins in Bismuth-Doped Copper, Ultramicroscopy, 37:180–181, 1991. [14] T.G. Ference, R.W. Balluffi, Observation of a Reversible Grain Boundary Faceting Transition Induced by Changes of Composition, Technical Report, Massachusetts Inst. of Tech., Cambridge (USA), 22:1929–1930, 1988. [15] M. Menyhard, B. Blum, C.J. McMahon, Grain Boundary Segregation and Transformations in Bi-doped Polycrystalline Copper, Acta Metallurgica, 37:549–557, 1989. [16] J.R. Rellick, C. McMahon, H. Marcus, P. Palmberg, The Effect of Tellurium on lntergranular Cohesion in Iron, Metallurgical Transactions, 2:1492-1493, 1971. [17] Chantal Loier and Jean-Yves Boos C. Loier, J.Y. Boos, The Influence of Grain Boundary Sulfur Concentration on the Intergranular Brittleness of Different Purities, Metallurgical Transactions A, 12:1222—1223, 1981. [18] Donna Sue Plickert, Steve Starr, Karen Skiba, Patricia Eland, Jeff Fenstermaker, ASM Handbook, Alloy Phase Diagrams, 3:345– 347, 1992. www.<strong>ijcer</strong>online.com ||May ||2013|| Page 72
International Journal of Computational Engineering Research||Vol, 03||Issue5|| Comparative Study of Available Technique for Detection in Sentiment Analysis Miss. Siddhi Patni 1 ,Prof. Avinash Wadhe 2 1, M.E (CSE) 2 nd Semester G.H.Raisoni College of Engineering&Management Amravati, India 2, ME (CSE) G.H.Raisoni College of Engineering & Management Amravati, India ABSTRACT: Our day-to-day life has always been influenced by what people think. Ideas and opinions of others have always affected our own opinions. As the Web plays an increasingly significant role in people's social lives, it contains more and more information concerning their opinions and sentiments. The distillation of knowledge from this huge amount of unstructured information, also known as opinion mining and sentiment analysis. It has recently raised growing interest for purposes such as customer service, financial market prediction, public security monitoring, election investigation, health related quality of life measure, etc. Sentiment Analyzer (SA) that extracts sentiment (or opinion) about a subject from online text documents. Instead of classifying the sentiment of an entire document about a subject, SA detects all references to the given subject, and determines sentiment in each of the references using natural language processing (NLP) techniques. There are various machine learning algorithms that attempt to predict the sentiment or opinions of some documents or information of particular data and organize data, such as finding positive and negative reviews while diminishing the need for human effort to classify the information. This paper compares the NLP and machine learning methods of sentiment analysis and determines which one is better. Keywords: Sentiment Analyzer, Natural Language Processing, Machine Learning. I. INTRODUCTION A vital part of the information era has been to find out the opinions of other people. In the pre-web era, it was customary for an individual to ask his or her friends and relatives for opinions before making a decision. Organizations conducted opinion polls, surveys to understand the sentiment and opinion of the general public towards its products or services. In the past few years, web documents are receiving great attention as a new medium that describes individual experiences and opinions. With explosion of Web 2.0 [1] applications such as blogs, forums and social networks came. The rise of blogs and social networks has fueled a market in personal opinion, reviews, ratings, recommendations and other forms of online expression. For computer scientists, this fast-growing mountain of data is opening a tantalizing window onto the collective consciousness of Internet users. An emerging field known as sentiment analysis is taking shape around one of the computer world‟s unexplored frontiers, translating the vagaries of human emotion into hard data. This is more than just an interesting programming exercise. For many businesses, online opinion has turned into a kind of virtual currency that can make or break a product in the marketplace. Therefore organizations have evolved and now look at review sites to know the public opinion about their products instead of conducting surveys. However, gathering all this online information manually is time consuming. Therefore automatic sentiment analysis is important. To do so, the main task is to extract the opinions, facts and sentiments expressed in these reviews. Sentiment analyzer that extracts sentiment about a given topic using NLP techniques consists of 1) a topic specific feature term extraction, 2) sentiment extraction, and 3) (subject, sentiment) association by relationship analysis. SA utilizes two linguistic resources for the analysis: the sentiment lexicon and the sentiment pattern database. Using machine learning algorithm for sentiment analysis is to extract human emotions from text or documents. Metrics such as accuracy of prediction and precision/recall are presented to gauge the success of these different algorithms. A system is there to process the documents and to predict human reactions, as well as provide results. www.<strong>ijcer</strong>online.com ||May ||2013|| Page 73
Page 1 and 2:
International Journal of computatio
Page 3 and 4:
Meisam Mahdavi Qualification: Phd E
Page 5 and 6:
12. 13. 14. 15. 16. 17. 18. 19. 20.
Page 7 and 8:
15. 16. 17. 18. 19. 20. Traditional
Page 9 and 10:
19. A Study on Security in Sensor N
Page 11 and 12:
International Journal of Computatio
Page 13 and 14:
Study Of The Energy Potential Of So
Page 15 and 16:
Page 17 and 18:
Page 19 and 20:
Neural network approach to power sy
Page 21 and 22:
Neural network approach to power sy
Page 23 and 24:
Strength, Corrosion correlation of
Page 25 and 26:
Strength, Corrosion correlation of
Page 27 and 28: Strength, Corrosion correlation of
Page 29 and 30: International Journal of Computatio
Page 31 and 32: Mutual Funds and SEBI Regulations S
Page 33 and 34: REFERENCES Mutual Funds and SEBI Re
Page 35 and 36: Integral solution of the biquadrati
Page 37 and 38: Integral solution of the biquadrati
Page 39 and 40: Experimental Studies on Effect of C
Page 47 and 48: Simulation studies on Deep Drawing
Page 51 and 52: Radial strain Radial Strain Hoop St
Page 55 and 56: Text Extraction in Video II. MAIN C
Page 57 and 58: Text Extraction in Video good idea.
Page 59 and 60: Text Extraction in Video Fig: 5.1 T
Page 61 and 62: Improved Performance for “Color t
Page 67 and 68: Chain code based handwritten cursiv
Page 69 and 70: Chain code based handwritten cursiv
Page 71 and 72: Strength of Ternary Blended Cement
Page 73 and 74: Strength of Ternary Blended Cement
Page 75 and 76: International Journal of Computatio
Page 77: Ni-Based Cr Alloys and Grain Bounda
Page 81 and 82: Comparative Study of Available Tech
Page 83 and 84: Comparative Study of Available Tech
Page 85 and 86: Analyzing massive machine data main
Page 87 and 88: Analyzing massive machine data main
Page 89 and 90: Implementation Of An Algorithmic To
Page 97 and 98: Model Order Reduction By Mixed... a
Page 99 and 100: Amplitude The unit time step respon
Page 101 and 102: Enterprise Management Information S
Page 103 and 104: Enterprise Management Information S
Page 105 and 106: A Novel Approach For Filtering Unre
Page 107 and 108: A Novel Approach For Filtering Unre
Page 109 and 110: Current Routing Strategies To Adapt
Page 111 and 112: Energy Based Routing Protocol for W
Page 113 and 114: Energy Based Routing Protocol for W
Page 115 and 116: Cfd Analysis of Convergent- Diverge
Page 127 and 128: Observation on the Ternary Cubic Eq
Page 129 and 130:
(2) n n 3n n x (2 ,1) y(2 ,1) 3[
Page 131 and 132:
Observation on the Ternary Cubic Eq
Page 133 and 134:
Integral Solutions of the Homogeneo
Page 135 and 136:
Integral Solutions of the Homogeneo
Page 137 and 138:
Page 139 and 140:
Page 141 and 142:
Page 143 and 144:
Page 145 and 146:
Dividing expression (a) by (b), P =
Page 147 and 148:
Highly Efficient Motorized…. Torq
Page 149 and 150:
Highly Efficient Motorized…. = =
Page 151 and 152:
Page 153 and 154:
A comparative study of Broadcasting
Page 155 and 156:
A comparative study of Broadcasting
Page 157 and 158:
Recommendation Systems: a review Co
Page 159 and 160:
Recommendation Systems: a review 1.
Page 161 and 162:
Recommendation Systems: a review V.
Page 163 and 164:
Using Fast Fourier Extraction Metho
Page 165 and 166:
Page 167 and 168:
Page 169 and 170:
Page 171 and 172:
P= Number of Poles. d) Fourth Objec
Page 173 and 174:
Using Genetic Algorithm Minimizing
Page 175 and 176:
Using Genetic Algorithm Minimizing
Page 177 and 178:
Literature review: Iris Segmentatio
Page 179 and 180:
Literature review: Iris Segmentatio
Page 181 and 182:
Effects of Agreement on Trims on In
Page 183 and 184:
Page 185 and 186:
Page 187 and 188:
A Novel Approach to Mine Frequent I
Page 189 and 190:
A Novel Approach to Mine Frequent I
Page 191 and 192:
Time(ms) A Novel Approach to Mine F
Page 193 and 194:
Page 195 and 196:
Similarly, reactions at bearings (1
Page 197 and 198:
Modeling and Analysis of the Cranks
Page 199 and 200:
Page 201 and 202:
Efficient Model for OFDM based IEEE
Page 203 and 204:
Page 205 and 206:
Page 207 and 208:
Page 209 and 210:
Traditional Uses Of Plants By The T
Page 211 and 212:
Traditional Uses Of Plants By The T
Page 213 and 214:
Page 215 and 216:
Content Based Video Retrieval Using
Page 217 and 218:
Content Based Video Retrieval Using
Page 219 and 220:
Effective and secure content retrie
Page 221 and 222:
Page 223 and 224:
Page 225 and 226:
speed rad/sec Master-Slave Speed Co
Page 227 and 228:
speed rad/sec speed rad/sec speed r
Page 229 and 230:
Cd-Hmm For Normal Sinus Rhythm 1 0
Page 231 and 232:
Cd-Hmm For Normal Sinus Rhythm III.
Page 233 and 234:
Develop A Electricity Utility… In
Page 235 and 236:
Develop A Electricity Utility… Ex
Page 237 and 238:
Develop A Electricity Utility… Fi
Page 239 and 240:
Page 241 and 242:
Implementing CURE to Address Scalab
Page 243 and 244:
Page 245 and 246:
Page 247 and 248:
Design of Uniform Fiber Bragg grati
Page 249 and 250:
Design of Uniform Fiber Bragg grati
Page 251 and 252:
Reflectivity (%) Reflectivity (%) D
Page 253 and 254:
Effect of Alkaline Activator on Wor
Page 255 and 256:
comp. strengthin Mpa Comp. strength
Page 257 and 258:
Effect of Alkaline Activator on Wor
Page 259 and 260:
Priority Based Service Composition
Page 261 and 262:
Priority Based Service Composition
Page 263 and 264:
Page 265 and 266:
Formation of pseudo-random sequence
Page 267 and 268:
Page 269 and 270:
Page 271 and 272:
Page 273 and 274:
Block mathematical coding method of
Page 275 and 276:
Block mathematical coding method of
Page 277 and 278:
Page 279 and 280:
Fast Encryption Algorithm for Strea
Page 281 and 282:
Page 283 and 284:
Area and Speed wise superior Vedic
Page 285 and 286:
Area and Speed wise superior Vedic
Page 287 and 288:
Page 289 and 290:
Effect of nano-silica on properties
Page 291 and 292:
Effect of nano-silica on properties
Page 293 and 294:
Page 295 and 296:
Load forecasting for practical powe
Page 297 and 298:
In scalar notation, the fitted mode
Page 299 and 300:
Mape load in MW Load forecasting fo
Page 301 and 302:
Load forecasting for practical powe
Page 303 and 304:
Page 305 and 306:
Efirstaid medical services for acci
Page 307 and 308:
Page 309 and 310:
Comparative study of capacitance of
Page 311 and 312:
Comparative study of capacitance of
Page 313 and 314:
Increasing the Comprehensibility of
Page 315 and 316:
Increasing the Comprehensibility of
Page 317 and 318:
Page 319 and 320:
Smart Message Communication for War
Page 321 and 322:
Page 323 and 324:
In Mobile Ad hoc Networks: Issues a
Page 325 and 326:
In Mobile Ad hoc Networks: Issues a
Page 327 and 328:
Page 329 and 330:
Comparison Of UPS Inverter Using PI
Page 331 and 332:
Page 333 and 334:
Page 335 and 336:
Page 337 and 338:
Ageing Behaviour of Epdm/Pvc Compos
Page 339 and 340:
Ageing Behaviour of Epdm/Pvc Compos
Page 341 and 342:
Page 343 and 344:
On Α Locally Finite In Ditopologic
Page 345 and 346:
Page 347 and 348:
A Study On Security In Sensor… IE
Page 349 and 350:
A Study On Security In Sensor… Th
Page 351 and 352:
Page 353 and 354:
Design and Simulation of Nonisolate
Page 355 and 356:
Page 357 and 358:
Page 359 and 360:
Page 361 and 362:
Evaluating The Privacy Measure Of T
Page 363 and 364:
Evaluating The Privacy Measure Of T
Page 365 and 366:
Work Done On Avoidance… n New RA
Page 367 and 368:
Work Done On Avoidance… Probabili
Page 369 and 370:
Data Aggregation Protocols In Wirel
Page 371 and 372:
Page 373 and 374:
Page 375 and 376:
Page 377 and 378:
Development Of Virtual Backbone…
Page 379 and 380:
Development Of Virtual Backbone…
Page 381 and 382:
Page 383 and 384:
Domain Driven Data Mining: An… To
Page 385 and 386:
Domain Driven Data Mining: An… X
Page 387 and 388:
Domain Driven Data Mining: An… [5
Page 389 and 390:
The Rhythm Of Omission Of Articles
Page 391 and 392:
All of them are from Rural backgrou
Page 393 and 394:
Page 395 and 396:
Page 397 and 398:
Page 399 and 400:
Page 401 and 402:
Image Segmentation using RGB… gra
Page 403 and 404:
Image Segmentation using RGB… and
Page 405 and 406:
Image Segmentation using RGB… [8]
Page 407 and 408:
Performance Comparison Of Rayleigh
Page 409 and 410:
Page 411 and 412:
Page 413 and 414:
Page 415 and 416:
Walking the talk in training future
Page 417 and 418:
Page 419 and 420:
Page 421 and 422:
Page 423 and 424:
Experimental Investigation Of Multi
Page 425 and 426:
Page 427 and 428:
Page 429 and 430:
A Firm Retrieval Of Software… Cla
Page 431 and 432:
A Firm Retrieval Of Software… hop
Page 433 and 434:
A Firm Retrieval Of Software… tab
Page 435 and 436:
2.1. Introduction to Service Matchi
Page 437 and 438:
Reduced Complexity Of Service Match
Page 439 and 440:
Page 441 and 442:
Pulsed Electrodeposition Of Nano…
Page 443 and 444:
Pulsed Electrodeposition Of Nano…
show all

Download - ijcer

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?