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i-manager's Journal on Software Engineering (JSE)

Current Issue Vol. 19 Issue 2

Most Cited

Volume 11 Issue 3 January - March 2017

Research Paper

A Comparative Study of Machine Learning Algorithms using Feature Selection Methods for Movie Review Analysis

Rajwinder Kaur* , Prince Verma**

* M.Tech Scholar, Department of Computer Science and Engineering, CT Institute of Engineering Management and Technology, Punjab, India.

** Assistant Professor, Department of Computer Science and Engineering, CT Institute of Engineering Management and Technology, Punjab, India.

Kaur, R., and Verma, P. (2017). A Comparative Study of Machine Learning Algorithms using Feature Selection Methods for Movie Review Analysis. i-manager’s Journal on Software Engineering, 11(3), 1-9. https://doi.org/10.26634/jse.11.3.13620

Abstract

Nowadays, the analysis of social sites, such as movie reviews’ sites, facebook, news feeds, and online shopping sites has been a broad area of research and customers post a large number of reviews in the form of comments to reveal their feelings as well as opinions as positive, negative, or neutral about a particular movie, product, pictures etc. To predict the reviews of users of such websites is a complex decision making process. These types of sites help the people to take decision about products. This paper proposes a Random Forest classifier with Information Gain based feature selection method for classification of movie review datasets. The results show that Information Gain method with Random Forest classifier has better performance in terms of Accuracy, Precision, and Recall.

References

[1]. Ahmad, A. S., Hassan, M. Y., Abdullah, M. P., Rahman, H. A., Hussin, F., Abdullah, H., & Saidur, R. (2014). A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renewable and Sustainable Energy Reviews, 33, 102-109.

[2]. Batrinca, B., & Treleaven, P. C. (2015). Social media analytics: A survey of techniques, tools and platforms. AI & Society, 30(1), 89-116.

[3]. Bhojani, S. H., & Bhatt, N. (2016). Data Mining Techniques and Trends - A Review. Global Journal For Research Analysis, 5(5), 252-254.

[4]. Chikersal, P., Poria, S., & Cambria, E. (2015, June). SeNTU: Sentiment Analysis of Tweets by combining a Rulebased Classifier with Supervised Learning. In SemEval@ NAACL-HLT (pp. 647-651).

[5]. Choubey, D. K., & Paul, S. (2016). Classification techniques for diagnosis of diabetes: review. International Journal of Biomedical Engineering and Technology, 21(1), 15-39.

[6]. Chu, C. H., Wang, C. A., Chang, Y. C., Wu, Y. W., Hsieh, Y. L., & Hsu, W. L. (2016, November). Sentiment analysis on Chinese movie review with distributed keyword vector representation. In Technologies and Applications of Artificial Intelligence (TAAI), 2016 Conference on (pp. 84- 89). IEEE.

[7]. Giatsoglou, M., Vozalis, M. G., Diamantaras, K., Vakali, A., Sarigiannidis, G., & Chatzisavvas, K. C. (2017). Sentiment analysis leveraging emotions and word embeddings. Expert Systems with Applications, 69, 214- 224.

[8]. Gupta, P., Sharma, A., & Grover, J. (2016, September). Rating based mechanism to contrast abnormal posts on movies reviews using MapReduce paradigm. In Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2016 5^th International Conference on (pp. 262-266). IEEE.

[9]. Hernández-Pereira, E., Bolón-Canedo, V., Sánchez- Maroño, N., Álvarez-Estévez, D., Moret-Bonillo, V., & Alonso-Betanzos, A. (2016). A comparison of performance of K-complex classification methods using feature selection. Information Sciences, 328, 1-14.

[10]. Kannvdiya, M., Patidar, K., & Kushwaha, R. S. (2016). A Survey on: Different Techniques and Features of Data Classification. International Journal of Research in Computer Applications and Robotics, 4(6), 1-6.

[11]. Kaur, S., & Grewal, A. K. (2016). A Review paper on Data Mining Classification Techniques for Detection of Lung Cancer. International Research Journal of Engineering and Technology (IRJET), 3(11), 1334-1338.

[12]. Khan, F. H., Qamar, U., & Bashir, S. (2016). SentiMI: Introducing point-wise mutual information with SentiWordNet to improve sentiment polarity detection. Applied Soft Computing, 39, 140-153.

[13]. Liao, S. H., Chu, P. H., & Hsiao, P. Y. (2012). Data mining techniques and applications–A decade review from 2000 to 2011. Expert Systems with Applications, 39(12), 11303-11311.

[14]. Modha, J. S., Pandi, G. S., & Modha, S. J. (2013). Automatic sentiment analysis for unstructured data. International Journal of Advanced Research in Computer Science and Software Engineering, 3(12), 91- 97.

[15]. Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., & Coello, C. A. C. (2014). A survey of multiobjective evolutionary algorithms for data mining: Part I. IEEE Transactions on Evolutionary Computation, 18(1), 4-19.

[16]. Nehra, N. (2014). A Survey on Sentiment Analysis of Movie Reviews. International Journal of Innovative Research In Technology (IJIRT), 1(7), 36-40.

[17]. Raghuvanshi, N., & Patil, J. M. (2016, March). A brief review on sentiment analysis. In Electrical, Electronics, and Optimization Techniques (ICEEOT), International Conference on (pp. 2827-2831). IEEE.

[18]. Sahin, H., & Subasi, A. (2015). Classification of the cardiotocogram data for anticipation of fetal risks using machine learning techniques. Applied Soft Computing, 33, 231-238.

[19]. Sahu, T. P., & Ahuja, S. (2016, January). Sentiment analysis of movie reviews: A study on feature selection & classification algorithms. In Microelectronics, Computing and Communications (MicroCom), 2016 International Conference on (pp. 1-6). IEEE.

[20]. Sharma, P., & Mishra, N. (2016, October). Feature level sentiment analysis on movie reviews. In Next Generation Computing Technologies (NGCT), 2016 2^nd International Conference on (pp. 306-311). IEEE.

[21]. Singh, V. K., Piryani, R., Uddin, A., & Waila, P. (2013, March). Sentiment analysis of movie reviews: A new feature-based heuristic for aspect-level sentiment classification. In Automation,Computing , Communication, Control and Compressed Sensing (iMac4s), 2013 International Multi-Conference on (pp. 712-717). IEEE.

[22]. Teng, Z., Vo, D. T., & Zhang, Y. (2016). Context- Sensitive Lexicon Features for Neural Sentiment Analysis. In EMNLP (pp. 1629-1638).

[23]. Tripathy, A., Agrawal, A., & Rath, S. K. (2016). Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications, 57, 117-126.

[24]. Vaghela, V. B., & Jadav, B. M. (2016). Analysis of Various Sentiment Classification Techniques. Analysis, 140(3), 22-27.

[25]. Yao, D., Yang, J., & Zhan, X. (2013). An improved random forest algorithm for class-imbalanced data classification and its application in PAD risk factors analysis. The Open Electrical & Electronic Engineering Journal, 7, 62-70.

[26]. You, Y. S., Lee, S., & Kim, J. (2016, October). Design and development of visualization tool for movie review and sentiment analysis. In Proceedings of the Sixth International Conference on Emerging Databases: Technologies, Applications, and Theory (pp. 117-123). ACM.

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Research Paper

Robust Waste Collection: Exploiting IoT Potentiality in Smart Cities

Sakshi Goenka* , R. S. Mangrulkar**

* PG Scholar, Department of Computer Science and Engineering, BDCE, Wardha, India.

** Associate Professor, Department of Computer Engineering, SVKM's D.J. Sanghvi College of Engineering, Mumbai, India.

Goenka, S., and Mangrulkar, R. S. (2017). Robust Waste Collection: Exploiting IoT Potentiality in Smart Cities. i-manager’s Journal on Software Engineering, 11(3), 10-18. https://doi.org/10.26634/jse.11.3.13622

Abstract

The Internet of Things (IoT) is an internetworking of physical devices, vehicles, buildings, and other items- embedded with electronics, software, sensors, and network connectivity that enables these objects to collect and exchange data. Civil habitation in the near future will move to large urban areas thus forming vast cities. These cities will incorporate IoT components in their backbone infrastructure, thus transforming into smart cities. One major component of smart cities is smart environment, a countermeasure for growing pollution. Waste management can be treated as a potential IoT service which exploits robustness and efficiency. IoT provides a novel solution for waste collection service. This paper presents one such solution. Smart bins are designed. These are incorporated with hardware containing microcontroller, sensor and GSM modem. The sensors will sense the level of bin filled and send its status to the server via GSM. At the server application, bin status along with its number and area will be displayed. The server will then notify ward member, driver or officer about bin status through a text message. Also an android application is developed for driver and officer to supervise their respective works and enable remote system handling.

References

[1]. Adewumi, A. O., & Adeleke, O. J. (2016). A New Model for Optimizing Waste Disposal based on Customers' Time Windows and Road Attributes. Applied Mathematical Sciences, 10(42), 2051-2063.

[2]. Anagnostopoulos, T., Zaslavsky, A., & Medvedev, A. (2015, April). Robust waste collection exploiting cost efficiency of IoT potentiality in Smart Cities. In Recent Advances in Internet of Things (RIoT), 2015 International Conference on (pp. 1-6). IEEE.

[4]. Bing, X., de Keizer, M., Bloemhof-Ruwaard, J. M., & van der Vorst, J. G. (2014). Vehicle routing for the ecoefficient collection of household plastic waste. Waste Management, 34(4), 719-729.

[5]. Buhrkal, K., Larsen, A., & Ropke, S. (2012). The waste collection vehicle routing problem with time windows in a city logistics context. Procedia-Social and Behavioral Sciences, 39, 241-254.

[6]. Fooladi, S., Fazlollahtabar, H., & Mahdavi, I. (2015). Waste collection vehicle routing problem considering similarity pattern of trashcan and garbage unloading. International Journal of Engineering and Management, 5, 187-198.

[7]. Gaur, A., Scotney, B., Parr, G., & McClean, S. (2015). Smart city architecture and its applications based on IoT. Procedia Computer Science, 52, 1089-1094.

[8]. Glouche, Y., & Couderc, P. (2013, June). A smart waste management with self-describing objects. In The Second International Conference on Smart Systems, Devices and Technologies (SMART'13).

[9]. Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29(7), 1645-1660.

[10]. Gutierrez, J. M., Jensen, M., Henius, M., & Riaz, T. (2015). Smart Waste Collection System Based on Location Intelligence. Procedia Computer Science, 61, 120-127.

[11]. Han, H., & Ponce Cueto, E. (2015). Waste collection vehicle routing problem: Literature review. PROMET-Traffic & Transportation, 27(4), 345-358.

[12]. Hemmelmayr, V., Doerner, K. F., Hartl, R. F., & Rath, S. (2013). A heuristic solution method for node routing based solid waste collection problems. Journal of Heuristics, 19(2), 129-156.

[13]. Jin, J., Gubbi, J., Marusic, S., & Palaniswami, M. (2014). An information framework for creating a smart city through Internet of Things. IEEE Internet of Things Journal, 1(2), 112-121.

[14]. Kasliwal Manasi, H., & Suryawanshi Smithkumar, B. (2016). A Novel approach to Garbage Management Using Internet of Things for smart cities. International Journal of Current Trends in Engineering & Research, 2(5), 348-353.

[15]. Kodwani, G., Mehta, M., & Shaikh, A. S. (2016). Smart Container-An Automated Alerting System. International Journal of Computer Applications, 155(5), 45-47.

[16]. Medvedev, A., Fedchenkov, P., Zaslavsky, A., Anagnostopoulos, T., & Khoruzhnikov, S. (2015, August). Waste management as an IoT-enabled service in smart cities. In Conference on Smart Spaces (pp. 104-115). Springer International Publishing.

[17]. Meghana, K. C., & Nataraj, K. R. (2016). IoT based Intelligent Bins for Smart Cities. International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC). 4(5), 225-229.

[18]. Mes, M. (2012). Using simulation to assess the opportunities of dynamic waste collection. In Use Cases of Discrete Event Simulation (pp. 277-307). Springer Berlin Heidelberg.

[19]. Navghane, S. S., Killedar, M. S., & Rohokale, V. M. (2016). IoT based smart garbage and waste collection bin. Int. J. Adv. Res. Electron. Commun. Eng, 5(5), 1576- 1578.

[20]. Pratheep, P., & Hannan, M. A. (2011). Solid waste bins monitoring system using RFID technologies. Journal of Applied Sciences Research, 7(7), 1093-1101.

[21]. Reed, M., Yiannakou, A., & Evering, R. (2014). An ant colony algorithm for the multi-compartment vehicle routing problem. Applied Soft Computing, 15, 169-176.

[22]. Sruthi, K. V., & Manjunath, K. N. A Novel approach to design a Smart bin using through IoT. 1^st International Conference on Innovations in Computing & Networking (ICICN16) (pp. 483-485).

[23]. Wy, J., Kim, B. I., & Kim, S. (2013). The rollon–rolloff waste collection vehicle routing problem with time windows. European Journal of Operational Research, 224(3), 466-476.

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Research Paper

Designing of Scientific Calculator with Case Structure Using NI Labview Software

Shamili A.R* , Lakshmi Ravi Teja M, Nehru. K*, Abhinaya. R****

, UG Scholar, Electronics and Communication Engineering, Institute of Aeronautical Engineering, Hyderabad, India.

Assistant Professor, Institute of Aeronautical Engineering, India.

Bachelor Degree, Erode Sengunthar Engineering College, Anna University, India.

Shamili, A. R., Teja, L. R. M., Nehru, K., and Abhinaya, R. (2017). Designing of Scientific Calculator with Case Structure Using NI Labview Software. i-manager’s Journal on Software Engineering, 11(3), 19-30. https://doi.org/10.26634/jse.11.3.13631

Abstract

The main objective of this paper is to simulate scientific calculator using NI LabVIEW software, a graphical programming language to automate the calculation procedure making less paper work for problem solving. In this work, the authors build logics by wiring icons in block diagram which is a graphical source code instead of line of text and the front panel acts as a user interface of the VI to create fully featured scientific calculator. It enables execution highlighting on block diagram and makes the flow of data visible at run-time engine and real-time operating system. This software enables the user to correct the errors with an indication of broken arrow (Run button) on the pull down menu.

References

[1]. Bitter, R., Mohiuddin, T., & Nawrocki, M. (2000). LabVIEW: Advanced Programming Techniques. CRC Press.

[2]. Corvallis Division, (1987). HP-28S Advanced Scientific Calculator. Reference Manual, 352 pages.

[4]. Hewlett Packard, (1989). Advanced Scientific Calculator. Reference Manual, HP-28S.

[5]. Johnson, G. W. (1997). LabVIEW Graphical Programming. Tata McGraw-Hill Education.

[6]. Kissane, B. (1997a). Mathematics with a Graphics Calculator. Casio fx-7400G, Published by MAWA.

[7]. Kissane, B. (1997b). More Mathematics with a Graphics Calculator. Casio cfx-9850G, Published by MAWA.

[8]. Kissane, B. (2000). Programming your Calculator. Casio fx-7400G PLUS, Shriro Australia Pty Limited.

[9]. Kissane, B., & Kemp, M. (2014a). Learning Mathematics with ES PLUS Series Scientific Calculator. CASIO, Japan.

[10]. Kissane, B., & Kemp, M. (2014b). Learning Mathematics with Graphics Calculators. CASIO, Japan.

[11]. Kissane, B., Bradley, J., & Kemp, M. (1997). Exploring Mathematics using a TI-80 Graphics Calculator. Published by MAWA 1997.

[12]. Martin, H. T., & Martin, M. L. (2000). LabView for Automotive, Telecommunications, Semiconductor, Biomedical, and Other Applications. Prentice Hall.

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Research Paper

Multi-Temporal SAR Image Change Detection Technique Depending On NSCT Transformation and SFCM

Shaik Shamimoon* , Chandra Mohan Reddy Sivappagari**

* M.Tech Student, Department of Electronics and Communication Engineering, JNTUACEP, Pulivendula, A.P, India..

** Associate Professor, Department of Electronics and Communication Engineering, JNTUACEP, Pulivendula, A.P, India.

Shaik, S., and Sivappagari, C. M. R. (2017). Multi-Temporal SAR Image Change Detection Technique Depending On NSCT Transformation and SFCM. i-manager’s Journal on Software Engineering, 11(3), 31-39. https://doi.org/10.26634/jse.11.3.13632

Abstract

In this paper, an unsupervised change detection technique of multispectral images based on Non-Subsampled Contourlet Transform (NSCT) has been proposed. The proposed method fuses absolute difference and change vector analysis image using fusion rules. A fuzzy local-information C- means clustering algorithm will be proposed for classifying changed and unchanged regions in the fused difference image. The differencing, changes are measured by subtracting the intensity values pixel by pixel between the considered couple of temporal images. In the case of SAR images, the ratio operator is typically used instead of the subtraction operator since the image differencing technique is not adapted to the statistics of SAR images. The results will be proven that rationing generates better difference image for change detection using spatial fuzzy clustering approach and efficiency of this algorithm will be exhibited by sensitivity and correlation evaluation.

References

[1]. Aiazzi, B., Baronti, S., & Selva, M. (2007). Improving component substitution pansharpening through multivariate regression of MS + Pan data. IEEE Transactions on Geoscience and Remote Sensing, 45(10), 3230-3239.

[2]. Akgun, T., Altunbasak, Y., & Mersereau, R. M. (2005). Super-resolution reconstruction of hyperspectral images. IEEE Transactions on Image Processing, 14(11), 1860- 1875.

[4]. Chavez, P., Sides, S. C., & Anderson, J. A. (1991). Comparison of three different methods to merge multiresolution and multispectral data- Landsat TM and SPOT panchromatic. Photogrammetric Engineering and Remote Sensing, 57(3), 295-303.

[5]. Do, M. N., & Vetterli, M. (2005). The contourlet transform: an efficient directional multiresolution image representation. IEEE Transactions on Image Processing, 14(12), 2091-2106.

[6]. Gu, Y., Zhang, Y., & Zhang, J. (2008). Integration of spatial–spectral information for resolution enhancement in hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 46(5), 1347-1358.

[7]. Jung, F. (2004). Detecting building changes from multitemporal aerial stereopairs. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3), 187-201.

[8]. Laben, C. A., & Brower, B. V. (2000). Process for enhancing the Spatial Resolution of Multispectral Imagery using Pan-Sharpening. U.S. Patent No. 6,011,875. Washington, DC: U.S. Patent and Trademark Office.

[9]. Loncan, L., de Almeida, L. B., Bioucas-Dias, J. M., Briottet, X., Chanussot, J., Dobigeon, N., et al. (2015). Hyperspectral pansharpening: A review. IEEE Geoscience and Remote Sensing Magazine, 3(3), 27-46.

[10]. Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693.

[11]. Matikainen, L., Hyyppä, J., Ahokas, E., Markelin, L., & Kaartinen, H. (2010). Automatic detection of buildings and changes in buildings for updating of maps. Remote Sensing, 2(5), 1217-1248.

[12]. Petrovic, V. (2007). Subjective tests for image fusion evaluation and objective metric validation. Information Fusion, 8(2), 208-216.

[13]. Simões, M., Bioucas-Dias, J., Almeida, L. B., & Chanussot, J. (2015). A convex formulation for hyperspectral image superresolution via subspacebased regularization. IEEE Transactions on Geoscience and Remote Sensing, 53(6), 3373-3388.

[14]. Starck, J. L., Fadili, J., & Murtagh, F. (2007). The undecimated wavelet decomposition and its reconstruction. IEEE Transactions on Image Processing, 16(2), 297-309.

[15]. Zhao, Y., Yang, J., Zhang, Q., Song, L., Cheng, Y., & Pan, Q. (2011). Hyperspectral imagery super-resolution by sparse representation and spectral regularization. EURASIP Journal on Advances in Signal Processing, 2011(1), 87.

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Research Paper

A fast algorithm for the weighted interval scheduling problem

Ruwanthini Siyambalapitiya*

Senior Lecturer, Department of Statistics & Computer Science, University of Peradeniya, Srilanka.

Siyambalapitiya, R. (2017). A fast algorithm for the weighted interval scheduling problem. i-manager’s Journal on Software Engineering, 11(3), 40-44. https://doi.org/10.26634/jse.11.3.13633

Abstract

Placing a set of activities or jobs along a timeline is known as scheduling. The interval scheduling problem arises when each job is specified by an interval with starting and finishing times of each job. It will become a weighted interval scheduling problem when a weight or a value is associated with each job. In this paper, the aim is to maximize the total weight of the jobs that could be scheduled without overlapping. A fast algorithm is presented to solve this problem. An upper bound for the total weight is computed and it is shown that the gap between the solutions to the test problems and the upper bound is extremely small.

References

[1]. Arkin, E. M., & Silverberg, E. B. (1987). Scheduling jobs with fixed start and end times. Discrete Applied Mathematics, 18(1), 1-8.

[2]. Bar-Noy, A., Guha, S., Naor, J. S., & Schieber, B. (2001). Approximating the throughput of multiple machines in real time scheduling. Journal of Combinatorial Optimization, 4, 307-323.

[4]. Chen, B., Hassin, R., & Tzur, M. (2002). Allocation of bandwidth and storage. IIE Transactions, 34(5), 501-507.

[5]. Crama, Y., Flippo, O. E., Van de Klundert, J., & Spieksma, F. C. (1997). The assembly of printed circuit boards: A case with multiple machines and multiple board types. European Journal of Operational Research, 98(3), 457-472.

[6]. Darmann, A., Pferschy, U., & Schauer, J. (2010). Resource allocation with time intervals. Theoretical Computer Science, 411(49), 4217-4234.

[7]. Erlebach, T., & Spieksma, F. C. (2003). Interval selection: Applications, algorithms, and lower bounds. Journal of Algorithms, 46(1), 27-53.

[8]. Kolen, A. W., Lenstra, J. K., Papadimitriou, C. H., & Spieksma, F. C. (2007). Interval scheduling: A survey. Naval Research Logistics (NRL), 54(5), 530-543.

[9]. Kovalyov, M. Y., Ng, C. T., & Cheng, T. E. (2007). Fixed interval scheduling: Models, applications, computational complexity and algorithms. European Journal of Operational Research, 178(2), 331-342.

[10]. Kroon, L. G., Salomon, M., & Van Wassenhove, L. N. (1995). Exact and approximation algorithms for the operational fixed interval scheduling problem. European Journal of Operational Research, 82(1), 190-205.

[11]. Torng, E. (1998). A unified analysis of paging and caching. Algorithmica, 20(2), 175-200.

i-manager's Journal on Software Engineering (JSE)

Current Issue Vol. 19 Issue 2

Most Read

Most Cited

Volume 11 Issue 3 January - March 2017

A Comparative Study of Machine Learning Algorithms using Feature Selection Methods for Movie Review Analysis

Rajwinder Kaur* , Prince Verma**

* M.Tech Scholar, Department of Computer Science and Engineering, CT Institute of Engineering Management and Technology, Punjab, India. ** Assistant Professor, Department of Computer Science and Engineering, CT Institute of Engineering Management and Technology, Punjab, India.

Kaur, R., and Verma, P. (2017). A Comparative Study of Machine Learning Algorithms using Feature Selection Methods for Movie Review Analysis. i-manager’s Journal on Software Engineering, 11(3), 1-9. https://doi.org/10.26634/jse.11.3.13620

Abstract

References

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Robust Waste Collection: Exploiting IoT Potentiality in Smart Cities

Sakshi Goenka* , R. S. Mangrulkar**

* PG Scholar, Department of Computer Science and Engineering, BDCE, Wardha, India. ** Associate Professor, Department of Computer Engineering, SVKM's D.J. Sanghvi College of Engineering, Mumbai, India.

Goenka, S., and Mangrulkar, R. S. (2017). Robust Waste Collection: Exploiting IoT Potentiality in Smart Cities. i-manager’s Journal on Software Engineering, 11(3), 10-18. https://doi.org/10.26634/jse.11.3.13622

Abstract

References

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Designing of Scientific Calculator with Case Structure Using NI Labview Software

Shamili A.R* , Lakshmi Ravi Teja M**, Nehru. K***, Abhinaya. R****

*,**** UG Scholar, Electronics and Communication Engineering, Institute of Aeronautical Engineering, Hyderabad, India. ** Assistant Professor, Institute of Aeronautical Engineering, India. *** Bachelor Degree, Erode Sengunthar Engineering College, Anna University, India.

Shamili, A. R., Teja, L. R. M., Nehru, K., and Abhinaya, R. (2017). Designing of Scientific Calculator with Case Structure Using NI Labview Software. i-manager’s Journal on Software Engineering, 11(3), 19-30. https://doi.org/10.26634/jse.11.3.13631

Abstract

References

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Multi-Temporal SAR Image Change Detection Technique Depending On NSCT Transformation and SFCM

Shaik Shamimoon* , Chandra Mohan Reddy Sivappagari**

* M.Tech Student, Department of Electronics and Communication Engineering, JNTUACEP, Pulivendula, A.P, India.. ** Associate Professor, Department of Electronics and Communication Engineering, JNTUACEP, Pulivendula, A.P, India.

Shaik, S., and Sivappagari, C. M. R. (2017). Multi-Temporal SAR Image Change Detection Technique Depending On NSCT Transformation and SFCM. i-manager’s Journal on Software Engineering, 11(3), 31-39. https://doi.org/10.26634/jse.11.3.13632

Abstract

References

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A fast algorithm for the weighted interval scheduling problem

Ruwanthini Siyambalapitiya*

Senior Lecturer, Department of Statistics & Computer Science, University of Peradeniya, Srilanka.

Siyambalapitiya, R. (2017). A fast algorithm for the weighted interval scheduling problem. i-manager’s Journal on Software Engineering, 11(3), 40-44. https://doi.org/10.26634/jse.11.3.13633

Abstract

References

Full Article (HTML)

Pdf

* M.Tech Scholar, Department of Computer Science and Engineering, CT Institute of Engineering Management and Technology, Punjab, India.

** Assistant Professor, Department of Computer Science and Engineering, CT Institute of Engineering Management and Technology, Punjab, India.

* PG Scholar, Department of Computer Science and Engineering, BDCE, Wardha, India.

** Associate Professor, Department of Computer Engineering, SVKM's D.J. Sanghvi College of Engineering, Mumbai, India.

Shamili A.R* , Lakshmi Ravi Teja M, Nehru. K*, Abhinaya. R****

, UG Scholar, Electronics and Communication Engineering, Institute of Aeronautical Engineering, Hyderabad, India.

Assistant Professor, Institute of Aeronautical Engineering, India.

Bachelor Degree, Erode Sengunthar Engineering College, Anna University, India.

* M.Tech Student, Department of Electronics and Communication Engineering, JNTUACEP, Pulivendula, A.P, India..

** Associate Professor, Department of Electronics and Communication Engineering, JNTUACEP, Pulivendula, A.P, India.