i-manager Publications

i-manager's Journal on Mobile Applications and Technologies (JMT)

Current Issue Vol. 11 Issue 1

Most Cited

Volume 8 Issue 1 January - June 2021

Research Paper

A Sign Translation App to Convert to ASL to Auditory English Language

Ayaan Farooqui * , Aayush Hatekar , Prateek Angadi *, Khyaati Shrikant ****

*-**** Department of Computer Engineering, Thakur College of Engineering and Technology, Maharashtra, India.

Farooqui, A., Hatekar, A., Angadi, P., and Shrikant, K. (2021). A Sign Translation App to Convert to ASL to Auditory English Language. i-manager's Journal on Mobile Applications and Technologies, 8(1), 1-7. https://doi.org/10.26634/jmt.8.1.18433

Abstract

Sign language is an optical communication language used by the people with speech and hearing disabilities for communication in their day-to-day conversation. Sign Language Recognition (SLR) system, which is required to recognize sign languages, has been widely studied for the past few years. Several input sensors, gesture segmentation, feature extraction and classification methods were used for the study. To eliminate the barrier in the communication between abled and disabled people is the main aim of this paper. This paper proposes a method to translate ASL in real time by mobile camera such that it can serve as a conversation medium between normal and deaf/dumb people.

References

[1]. Cao, Z., Hidalgo, G., Simon, T., Wei, S. E., & Sheikh, Y. (2019). OpenPose: Realtime Multi-person 2D Pose Estimation using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 172-186. https://doi.org/10.1109/TPAMI.2019.2929257

[2]. Koller, O., Zargaran, O., Ney, H., & Bowden, R. (2016). Deep sign: Hybrid CNN-HMM for Continuous Sign Language Recognition. In Proceedings of the British Machine Vision Conference, 1-12.

[3]. Maxwell, C. (1892). A Treatise on Electricity and Magnetism (3^rd ed). Oxford: Clarendon.

[4]. Parton, B. S. (2006). Sign Language Recognition and Translation: A Multidisciplined Approach from the Field of Artificial Intelligence. Journal of Deaf Studies and Deaf Education, 11(1), 94-101. https://doi.org/10.1093/deafed/ enj003

[5]. Rajaganapathy, S., Aravind, B., Keerthana, B., & Sivagami, M. (2015). Conversation of Sign Language to Speech with Human Gestures. Procedia Computer Science, 50, 10-15. https://doi.org/10.1016/j.procs.2015. 04.004

[6]. Sahoo, A. K., & Ravulakollu, K. K. (2014). Vision Based Indian Sign Language Character Recognition. Journal of Theoretical & Applied Information Technology, 67(3), 43(1), 172-186. https://doi.org/10.1109/TPAMI.2019.2929257

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Research Paper

Turistas: Trip Planner and Recommender

Rohit Gupta * , Parth Shah , Raihan Siddiqui , Karan Prajapati , Sagar D. Korde

*-***** K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, India.

Gupta, R., Shah, P., Siddiqui, R., Prajapati, K., and Korde, S. D. (2021). Turistas: Trip Planner and Recommender. i-manager's Journal on Mobile Applications and Technologies, 8(1), 8-13. https://doi.org/10.26634/jmt.8.1.18409

Abstract

Turistas, is an expert system that guides with itinerary planning. Turistas will help with creating routes from source to destination by passing through the point of interest of the user and thereafter providing navigation. System will also be able to locate various other break points that users would need such as ATM, Petrol Pumps, Restaurants, etc. System would also recommend various other tourist locations based on the user's visited tourist destinations. The system focuses on two aspects: the recommender system and the route planning algorithm.

References

[1]. Agarwal, J., Sharma, N., Kumar, P., Parshav, V., Srivastava, A., & Goudar, R. H. (2013, January). Intelligent search in ETourism services using Recommendation System: Perfect guide for tourist. In 2013, 7^th International Conference on Intelligent Systems and Control (ISCO) (pp. 410-415). IEEE. https://doi.org/10.1109/ISCO.2013.6481190

[2]. Coelho, A., & Rodrigues, A. (2011, November). Personalized travel suggestions for tourism websites. In 2011, 11^th International Conference on Intelligent Systems Design and Applications (pp. 118-123). IEEE. https://doi. org/10.1109/ISDA.2011.6121641

[3]. Fang, S. H., Lu, E. H. C., & Tseng, V. S. (2014, July). Trip recommendation with multiple user constraints by integrating point-of-interests and travel packages. In 2014, IEEE 15^th International Conference on Mobile Data Management (Vol. 1, pp. 33-42). IEEE. https://doi.org/10. 1109/MDM.2014.10

[4]. Gao, R., Li, J., Du, B., Li, X., Chang, J., Song, C., & Liu, D. (2018). Exploiting geo-social correlations to improve pairwise ranking for point-of-interest recommendation. China Communications, 15(7), 180-201. https://doi.org/ 10.1109/CC.2018.8424613

[5]. Smirnov, A., Kashevnik, A., Ponomarev, A., Shchekotov, M., & Kulakov, K. (2015, July). Application for e-tourism: th intelligent mobile tourist guide. In 2015, IIAI 4^th International Congress on Advanced Applied Informatics (pp. 40-45). IEEE. https://doi.org/10.1109/IIAI-AAI.2015.190

[6]. Wahurwagh, R. A., & Chouragade, P. M. (2019, February). Personalized POI Travel Recommendation With Multiple Tourist Information. In 2019, IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp. 1-3). IEEE. https://doi.org/10.11 09/ICECCT.2019.8869387

[7]. Ying, J. C., Lu, E. H. C., Huang, C. M., Kuo, K. C., Hsiao, Y. H., & Tseng, V. S. (2013, March). A framework for cloudst based POI search and trip planning systems. In 2013, 1^st International Conference on Orange Technologies (ICOT) (pp. 274-277). IEEE. https://doi.org/10.1109/ICOT.2013.65 21211

[8]. Zhang, H., Ganchev, I., Nikolov, N. S., & O'Droma, M. (2017, September). Weighted item ranking for pairwise matrix factorization. In 2017, South Eastern European Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) (pp. 1-5). IEEE. https://doi.org/10.23919/SEEDA-CECNSM. 2017.8089996

[9]. Zhang, H., Ganchev, I., Nikolov, N. S., Ji, Z., & O'Droma, M. (2017, July). Weighted matrix factorization with Bayesian personalized ranking. In 2017, Computing Conference (pp. 307-311). IEEE. https://doi.org/10.1109/SAI.2017.8252119

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Research Paper

Data Plane Resource Optimization for Multipath Flows

Muharram Charkari*

Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.

Charkari, M. (2021). Data Plane Resource Optimization for Multipath Flows. i-manager's Journal on Mobile Applications and Technologies, 8(1), 14-25. https://doi.org/10.26634/jmt.8.1.18449

Abstract

Many contemporary networks, such as datacenters, optical networks, and MultiProtocol Label Switching (MPLS) make it impossible or inefficient to supply a traffic flow with a certain bandwidth need via a single network link. In these instances, it is often feasible to increase network bandwidth utilization by distributing traffic over numerous efficient channels. While employing numerous pathways for the same data flow improves network efficiency, it uses costly forwarding resources from network nodes, such as TCAM, Ethernet/MPLS switch entries and optical switch wavelengths/lightpaths. In this work, we identify many challenges related to dividing a traffic flow across several pathways while using as few forwarding resources as possible, and we offer efficient methods for handling these difficulties.

References

[1]. Baier, G., Köhler, E., & Skutella, M. (2005). The ksplittable flow problem. Algorithmica, 42(3), 231-248. https://doi.org/10.1007/s00453-005-1167-9

[2]. Banner, R., & Orda, A. (2003). Efficient Multipath Routing Schemes for Congestion Minimization. Technion- IIT, Department of Electrical Engineering. CCIT Report. Retrieved from https://vee.technion.ac.il/wp-content/uploads/sites/36/2016/12/publication_429.pdf

[3]. Barabási, A. L., Albert, R., & Jeong, H. (2000). Scalefree characteristics of random networks: the topology of the world-wide web. Physica A: Statistical Mechanics and its Applications, 281(1-4), 69-77. https://doi.org/10.1016/S0378-4371(00)00018-2

[4]. Cadger, F., Curran, K., Santos, J., & Moffet, S. (2016). Location and mobility-aware routing for improving multimedia streaming performance in MANETs. Wireless Personal Communications, 86(3), 1653-1672. https://doi.org/10.1007/s11277-015-3012-z

[5]. Curtis, A. R., Mogul, J. C., Tourrilhes, J., Yalagandula, P., Sharma, P., & Banerjee, S. (2011, August). DevoFlow: Scaling flow management for high-performance networks. In Proceedings of the ACM SIGCOMM 2011 Conference (pp. 254-265). https://doi.org/10.1145/2018436.2018466

[6]. Edmonds, J., & Karp, R. M. (1972). Theoretical improvements in algorithmic efficiency for network flow problems. Journal of the ACM (JACM), 19(2), 248-264. https://doi.org/10.1145/321694.321699

[7]. Gavalas, D., Konstantopoulos, C., Mamalis, B., & Pantziou, G. (2011). Mobility prediction in mobile ad-hoc networks. In Next Generation Mobile Networks and Ubiquitous Computing (pp. 226-240). IGI Global. https://doi.org/10.4018/978-1-60566-250-3.ch021

[8]. Gerstel, O., Ramaswami, R., & Sasaki, G. H. (2000). Cost-effective traffic grooming in WDM rings. IEEE/ACM Transactions on Networking, 8(5), 618-630. https://doi.org/10.1109/90.879348

[9]. Li, X., Mitton, N., & Simplot-Ryl, D. (2011, May). Mobility prediction based neighborhood discovery in mobile ad hoc networks. In International Conference on Research in Networking (pp. 241-253). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20757-0_19

[10]. Makhlouf. (2018). Mobility Prediction Using Virtual Map for MANET Networks, elektrorevue.

[11]. Nakibly, G., Cohen, R., & Katzir, L. (2013). Optimizing data plane resources for multipath flows. IEEE/ACM Transactions on Networking, 23(1), 138-147. https://doi.org/10.1109/TNET.2013.2292895

[12]. Raiciu, C., Barre, S., Pluntke, C., Greenhalgh, A., Wischik, D., & Handley, M. (2011). Improving datacenter performance and robustness with multipath TCP. ACM SIGCOMM Computer Communication Review, 41(4), 266- 277. https://doi.org/10.1145/2043164.2018467

[13]. Rathore, N. K. (2018). An Efficient Dynamic & Decentralized Load Balancing Technique for Grid, Scholars\' Press.

[14]. Rathore, N. K., Rawat, U., & Kulhari, S. C. (2020). Efficient hybrid load balancing algorithm. National Academy Science Letters, 43(2), 177-185. https://doi.org/10.1007/s40009-019-00834-w

[15]. Zaidi, Z. R., & Mark, B. L. (2004, December). Mobility estimation for wireless networks based on an autoregressive model. In IEEE Global Telecommunications Conference, 2004. GLOBECOM'04 (Vol. 6, pp. 3405-3409). IEEE. https://doi.org/10.1109/GLOCOM.2004.1378999

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Research Paper

Diabetic Retinopathy Based on GUI

Sreenidhi B.* , Thamotharan G. , Lucija Majcen*, Lara Mervar****

*-**** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kancheepuram, Tamil Nadu, India.

Sreenidhi, B., Thamotharan, G., Lalitha, A., and Thanuprasanth, C. (2021). Diabetic Retinopathy Based on GUI. i-manager's Journal on Mobile Applications and Technologies, 8(1), 26-31. https://doi.org/10.26634/jmt.8.1.18159

Abstract

Blood vessel disease has emerged as a major global threat. If blood vessel disease is not diagnosed and treated promptly, it might lead to other illnesses in the patient's body. One such ailment has to do with the retina of the human eye and how it affects the retina and retinal anatomy. Retinopathy is a screening method for detecting such abnormalities in the retina. Auto identification of retinopathy based on feature extractions is now possible, thanks to recent technological advances in image processing. This analysis not only aids in the diagnosis of the condition, but also in determining its severity. We employ SVM-based machine learning algorithms.

References

[1]. Bandara, A. M. R. R., & Giragama, P. W. G. R. M. P. B. (2017, December). A retinal image enhancement technique for blood vessel segmentation algorithm. In 2017, IEEE International Conference on Industrial and Information Systems (ICIIS) (pp. 1-5). IEEE. https://doi.org/10. 1109/ICIINFS.2017.8300426

[2]. Kaur, T., Singh, J., Rehani, V., Sharma, A., & John, A. (2017). Diabetic retinopathy detection system (DRDS); A novel GUI based approach for diabetic retinopathy detection. International Journal of Computational Engineering Research (IJCER), 7(11), 19-29.

[3]. Lopes, A. P., Ribeiro, A., & Silva, C. A. (2019, February). Dilated convolutions in retinal blood vessels segmentation. In 2019, IEEE 6^thPortuguese Meeting on Bioengineering (ENBENG) (pp. 1-4). IEEE. https://doi.org/10.1109/ENBENG. 2019.8692520

[4]. Mondal, R., Chatterjee, R. K., & Kar, A. (2017, December). Segmentation of retinal blood vessels using adaptive noise island detection. In 2017, Fourth International Conference on Image Information Processing (ICIIP) (pp. 1-5). IEEE. https://doi.org/10.1109/IC IIP.2017.8313673

[5]. Park, K. B., Choi, S. H., & Lee, J. Y. (2020). M-gan: Retinal blood vessel segmentation by balancing losses through stacked deep fully convolutional networks. IEEE Access, 8, 146308-146322. https://doi.org/10.1109/ACCESS.2020.30 15108

[6]. Ratanapakorn, T., Daengphoonphol, A., Eua-Anant, N., Yospaiboon, Y., & Yospaiboon Y. (2019). Digital image processing software for diagnosing diabetic retinopathy from fundus photograph. Clinical Ophthalmology, 13, 641- 648. https://doi.org/10.2147/OPTH.S195617

[7]. Subhashini, R., Nithin, T. N. R., & Koushik, U. M. S. (2019). Diabetic retinopathy detection using image processing (GUI). International Journal of Recent Technology and Engineering, 8(2S3), 538-542. https://doi.org/10.35940/ijr te.B1097.0782S319

[8]. Venkatalakshmi, B., Saravanan, V., & Niveditha, G. J. (2011, May). Graphical user interface for enhanced retinal image analysis for diagnosing diabetic retinopathy. In 2011, IEEE 3^rd International Conference on Communication Software and Networks (pp. 610-613). IEEE. https://doi.org/ 10.1109/ICCSN.2011.6014967

[9]. Wankhede, P. R., & Khanchandani, K. B. (2018, December). Retinal Blood Vessel Segmentation in Fundus Images using Improved Graph Cut Method. In 2018, International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 555-558). IEEE. https://doi.org/10. 1109/ICSSIT.2018.8748531

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Research Article

Evolution of Bluetooth Technology

Satish Reddy*

Aditya Engineering College, East Godavari, Andhra Pradesh, India.

Reddy, S. (2021). Evolution of Bluetooth Technology. i-manager's Journal on Mobile Applications and Technologies, 8(1), 32-40. https://doi.org/10.26634/jmt.8.1.18461

Abstract

Special Interest Group (SIG) manages the Bluetooth short-range wireless technology network. Bluetooth technology began with the replacement of cables and progressed to Low Energy and subsequently Mesh Technology. Different specifications are accessible in the market based on the needs of Application Development. Every standard has its own set of advantages and disadvantages. Low Energy Device is the Bluetooth 5.0 specification. The study of BLE 5.0 exhibits the development of BLE 5.0's design and performance at the Link Layer and PHY. In the controller, the BLE 5.0 architecture's Link Layer and Physical Layer (Radio channels) are employed. There has been a reduction in device complexity and power usage. The Link Layer and PHY design languages are synthesizable Verilog.

References

[1]. BLE. (2018). Bluetooth Low Energy (BLE) overview. Global Tag. Retrieved form https://www.global-tag.com/ bluetooth -low-energy-ble-overview/

[2]. Bluetooth Mesh Profile. (2019). Overview of mesh operation (Ver 1.0).

[3]. Bluetooth Mesh Profile. (2019a). Architectural concepts (Ver 1.0.).

[4]. Bluetooth Specification. (2010a). General description (Ver 4.0: Vol. 6. Part B).

[5]. Bluetooth Specification. (2010b). Overview of Bluetooth Low Energy Operation (Ver. 4.0: Vol. 1. Part A.).

[6]. Bluetooth Specification. (2010c). Park state (Ver 4.0: Vol 2. Part B).

[7]. Bluetooth Specification. (2010d). Active mode (Ver. 4.0: Vol. 2. Part B).

[8]. Bluetooth Specification. (2013). Overview of States (Ver 4.1: Vol 2. Part B).

[9]. Bluetooth Specification. (2016). Core system Package (Ver 5.0: Vol 0. Part C).

[10]. Bluetooth Specification. (2019). Link Layer States (Ver 5.1: Vol 6. Part B).

[11]. Bluetooth. (2007). Develop with Bluetooth Technology. Retrieved from https://www.bluetooth.org

[12]. Bray, J., & Sturman, C. F. (2001). Bluetooth, Connect Without Cables (1^st ed.).Prentice Hall PTR.

[13]. Eagle, N., & Pentland, A. S. (2006). Reality Mining: Sensing Complex Social Systems. Personal and Ubiquitous Computing, 10(4), 255-268. https://doi.org/10.1007/s007 79-005-0046-3

[14]. Franklin, C., & Pollette, C. (2005). How Bluetooth works. How Stuff Works, Inc. Retrieved from https:// electronics.how stuffworks.com/bluetooth.htm

[15]. Kolderup, K., & Marcel, J. (2017). The Case for Bluetooth Mesh. Bluetooth SIG, 1(1), 1-10.

[16]. Miller, B. A., & Chatschik, B. (2001). Bluetooth Revealed (1^st ed.). PrenticeHall PTR.

[17]. Rysavy, P. (2004). Brush up on Bluetooth. Network Computing. Retrieved from https://www.network computing.com/wireless-infrastructure/brush-bluetooth/ page/0/5?piddl_msgorder=

[18]. Schwartz, S. M. (2001). FHSS vs. DSSS in the Broadband Wireless Access and WLAN Arenas.

[19]. Tech Encyclopedia-Bluetooth. (2007). The Tech Web Network. Retrieved from https://www.techweb.com/ encyclopedia/defineterm.jhtml?term=bluetooth

[20]. Woolley, M. (2019). Bluetooth Mesh Networking. An Introduction for Developers. Retrieved from https://www. bluetooth.com/bluetooth-resources/bluetooth-meshnetworking- an-introduction-for-developers/

i-manager's Journal on Mobile Applications and Technologies (JMT)

Current Issue Vol. 11 Issue 1

Most Read

Most Cited

Volume 8 Issue 1 January - June 2021

A Sign Translation App to Convert to ASL to Auditory English Language

Ayaan Farooqui * , Aayush Hatekar **, Prateek Angadi ***, Khyaati Shrikant ****

*-**** Department of Computer Engineering, Thakur College of Engineering and Technology, Maharashtra, India.

Farooqui, A., Hatekar, A., Angadi, P., and Shrikant, K. (2021). A Sign Translation App to Convert to ASL to Auditory English Language. i-manager's Journal on Mobile Applications and Technologies, 8(1), 1-7. https://doi.org/10.26634/jmt.8.1.18433

Abstract

References

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Turistas: Trip Planner and Recommender

Rohit Gupta * , Parth Shah **, Raihan Siddiqui ***, Karan Prajapati ****, Sagar D. Korde *****

*-***** K. J. Somaiya College of Engineering, Vidyavihar, Mumbai, India.

Gupta, R., Shah, P., Siddiqui, R., Prajapati, K., and Korde, S. D. (2021). Turistas: Trip Planner and Recommender. i-manager's Journal on Mobile Applications and Technologies, 8(1), 8-13. https://doi.org/10.26634/jmt.8.1.18409

Abstract

References

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Data Plane Resource Optimization for Multipath Flows

Muharram Charkari*

Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.

Charkari, M. (2021). Data Plane Resource Optimization for Multipath Flows. i-manager's Journal on Mobile Applications and Technologies, 8(1), 14-25. https://doi.org/10.26634/jmt.8.1.18449

Abstract

References

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Diabetic Retinopathy Based on GUI

Sreenidhi B.* , Thamotharan G. **, Lucija Majcen***, Lara Mervar****

*-**** Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kancheepuram, Tamil Nadu, India.

Sreenidhi, B., Thamotharan, G., Lalitha, A., and Thanuprasanth, C. (2021). Diabetic Retinopathy Based on GUI. i-manager's Journal on Mobile Applications and Technologies, 8(1), 26-31. https://doi.org/10.26634/jmt.8.1.18159

Abstract

References

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Evolution of Bluetooth Technology

Satish Reddy*

Aditya Engineering College, East Godavari, Andhra Pradesh, India.

Reddy, S. (2021). Evolution of Bluetooth Technology. i-manager's Journal on Mobile Applications and Technologies, 8(1), 32-40. https://doi.org/10.26634/jmt.8.1.18461

Abstract

References

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Ayaan Farooqui * , Aayush Hatekar , Prateek Angadi *, Khyaati Shrikant ****

Rohit Gupta * , Parth Shah , Raihan Siddiqui , Karan Prajapati , Sagar D. Korde

Sreenidhi B.* , Thamotharan G. , Lucija Majcen*, Lara Mervar****