Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Rajanarayanan, S.
- Evaluation of the Ability of Mouse to Detect Estrus in Buffalo Urine
Abstract Views :189 |
PDF Views:0
Authors
Affiliations
1 Reproductive Endocrinology and Pheromone Biochemistry Lab, Department of Animal Science, Bharathidasan University, Tiruchirappalli-620024, IN
1 Reproductive Endocrinology and Pheromone Biochemistry Lab, Department of Animal Science, Bharathidasan University, Tiruchirappalli-620024, IN
Source
Journal of Endocrinology and Reproduction, Vol 7, No 1&2 (2003), Pagination: 78-79Abstract
The present investigation was carried out to discriminate die buffalo urinary odours of different stages of estrous cycle. Experimental evidences suggest that rodents are competent and detecting the odour differences.
- Energy Efficient Radio Access Technologies and Networking Wireless Access Network
Abstract Views :186 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science Engineering, Arba Minch University, ET
2 Department of Business and Information Technology, Arba Minch University, ET
3 Department of Computer Science Engineering, Hanseo University, KR
1 Department of Computer Science Engineering, Arba Minch University, ET
2 Department of Business and Information Technology, Arba Minch University, ET
3 Department of Computer Science Engineering, Hanseo University, KR
Source
ICTACT Journal on Communication Technology, Vol 9, No 3 (2018), Pagination: 1846-1857Abstract
LEACH (Low Energy Adaptive Clustering Hierarchy) is the first network protocol that uses hierarchical routing for Wireless Sensor Networks (WSN) to increase the life time of network. Research on WSN has recently received much attention as they offer an advantage of monitoring various kinds of environment by sensing physical phenomenon, such as in-hospitable terrain, it is expected that suddenly active to gather the required data for some times when something is detected, and then remaining largely inactive for long periods of time. So, efficient energy saving schemes and corresponding algorithms must be developed and designed in order to provide reasonable energy consumption and to improve the network lifetime for WSN. WSN are networks consist of large number of tiny battery powered sensor nodes having limited on-board storage, processing, and radio capabilities. Nodes sense and send their reports toward a processing center which is called sink node or Base Station (BS). Since the transmission and reception process consumes lots of energy for data dispensation, it is necessary to designing protocols and applications for such networks has to be energy aware in order to prolong the lifetime of the network. The proposed, LEACH-PR (Low Energy Adaptive Clustering Hierarchy - Power Resourceful) protocol includes clustering, routing and radio propagation technique by balancing the energy consumption of sensor nodes to improve the efficiency of data transmission and prolonging the network lifetime. The goals of this scheme are, increase the stability period of network, and minimize the energy consumption. The performance analysis of proposed LEACH-PR is compared with I-LEACH (Improved LEACH), EHE-LEACH (Enhanced Heterogeneous LEACH), and EEM-LEACH (Energy Efficient Multi-hop LEACH) protocols and concluded that, the LEACH-PR has significant improvement over in terms of lifetime of network, both in homogeneous and heterogeneous environments.Keywords
LEACH, Network Lifetime, Wireless Sensor Networks, Radio Capabilities.References
- Z. Abate, “WiMAX RF Systems Engineering”, Artech House, 2009.
- D.P. Agarwal and A. Manjeshwar, “TEEN: A Routing Protocol for Enhanced Efficiency in Wireless Sensor Networks”, Proceedings of 1st International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing, pp. 1-6, 2009.
- A. Ahson and T. Borko Furht Syed, “Long Term Evolution: 3GPP LTE Radio and Cellular Technology”, CRC Press, 2009.
- L.F. Akyildiz, Y. Sankara Subramaniam and E. Cayirci, “A Survey on Sensor Networks”, IEEE Communications Magazine, Vol. 40, No. 8, pp. 114-124, 2002.
- Ali Norouzi and Abdul Halim Zaim, “An Integrative Comparison of Energy Efficient Routing Protocols in Wireless Sensor Network”, Proceedings of 1st International Conference on Scientific Research of Wireless Sensor Network, pp. 65-67, 2012.
- Anamika Chauhan and Amit Kaushik, “TADEEC: Threshold Sensitive Advanced Distributed Energy Efficient Clustering Routing Protocol for Wireless Sensor Networks”, International Journal of Computer Applications, Vol. 96, No. 23, pp. 23-28, 2014.
- Ashlyn Antoo and Rameez Mohammed, “EEM-LEACH: Energy Efficient Multi-Hop Leach Routing Protocol for Clustered WSNs”, Proceedings of International Conference on Control, Instrumentation, Communication and Computational Technologies, pp. 341-349, 2014.
- Aruna Pathak, D.K.L. Zaheeruddin and Manoj Tiwari, “Improvement of Lifetime of Wireless Sensor Network by Jointly Effort of Exponential Node Distribution and Mixed Routing”, Proceedings of International Conference on Communication Systems and Network Technologies, pp. 275-284, 2012.
- S. Bapat et al., “A Wireless Sensor Network for Target Detection, Classification, and Tracking”, Computer Network, Vol. 46, No. 5, pp. 605-634, 2004.
- Simranjit Kaur, “Improved Energy-Efficient BBO-Based PEGASIS Protocol in Wireless Sensors Network”, International Journal of Engineering Research and Applications, Vol. 4, No. 3, pp. 470-474, 2014.
- N. Blaunstein, “Radio Propagation in Cellular Networks”, Artech House, 2009.
- A. Braman and G.R. Umapathi, “A Comparative Study on Advances in LEACH Routing Protocol for Wireless Sensor Networks: A Survey”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, No. 2, pp. 5683-5690, 2014.
- A. Buratti, D. Conti and R. Verdone, “An Overview on Wireless Sensor Networks Technology and Evolution”, Sensors, Vol. 9, No. 9, pp. 6869-6896, 2009.
- A. Depedri, A. Zanella and R. Verdone, “An Energy Efficient Protocol for Wireless Sensor Networks”, Proceedings of 62nd International Conference on Vehicular Technology, pp 1-6, 2003.
- A. Ephremides, J.E. Wieselthier and D.J. Baker, “A Design Concept for Reliable Mobile Radio Networks with Frequency Hopping Signaling”, Proceedings of IEEE, Vol. 75, No. 1, pp. 56-73, 2003.
- Guangjie Han et al., “A Comparative Study of Routing Protocols of Heterogeneous Wireless Sensor Networks”, The Scientific World Journal, Vol. 14, pp. 1-15, 2014.
- Jia Xu et al., “Improvement of LEACH protocol for WSN”, Proceedings of 9th International Conference on Fuzzy Systems and Knowledge Discovery, pp. 29-31, 2012.
- R. Kaur, D. Sharma and N. Kaur, “Comparative Analysis of Leach and Its Descendant Protocols in Wireless Sensor Network”, International Journal of P2P Network Trends and Technology, Vol. 3, No. 1, pp. 51-55, 2013.
- J. Kiran and D. Vishal, “Comparative Analysis of Path Loss Propagation Model in Radio Communication”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 3, No. 2, pp. 12-201, 2015.
- Dilip Kumar et al., “EEHC: Energy Efficient Heterogeneous Clustered Scheme for Wireless Sensor Networks”, Computer Communications, Vol. 32, No. 4, pp. 662-667, 2009.
- Recognition of Pathogens using Image Classification based on Improved Recurrent Neural Network with LSTM
Abstract Views :173 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science Engineering, Arba Minch University, ET
1 Department of Computer Science Engineering, Arba Minch University, ET
Source
ICTACT Journal on Soft Computing, Vol 9, No SP 2 (2019), Pagination: 1856-1861Abstract
In this paper, a technique is proposed on Recurrent Neural Network (RNN) with the end goal to group pathogen with five Deep learning stages: preparing dataset images, RNN training, testing the RNN model with collected images, Apply RNN created show on testing information lastly and evaluate the performance of the proposed method. RNN can enhance the precision in pathogens determination that are centered around hand-tuned include extraction suggesting some human oversights. For our examination, we consider cholera affected images i.e. Vibrio cholera pathogen image for minute images classification with a significant RNN. Image classification is the responsibility of consideration the image information and obtaining perfect likelihood of classes that best portrays the image. In spite of the fact that this archive tends to the order of pandemic pathogen Images utilizing a RNN demonstrate, the hidden standards apply to alternate fields of science and innovation, as a result of its execution and its capacity to deal with a larger number of layers than the past customary neural networks.Keywords
Images Classification, Deep Learning, Recurrent Neural Networks, LSTM.References
- A.A. Abkar, M.A. Sharifi and N.J. Mulder, “Likelihood-based Image Segmentation and Classification: A Framework for the Integration of Expert Knowledge in Image Classification Procedures”, International Journal of Applied Earth Observation and Geoinformation, Vol. 2, No. 2, pp. 104-119, 2000
- S. Bahrampour et al., “Comparative study of Deep Learning Software Frameworks”, Available at: https://arxiv.org/pdf/1511.06435.pdf, 2015.
- J.G.A. Barbedo, “Digital Image Processing Techniques for Detecting, Quantifying and Classifying Plant Diseases”, SpringerPlus, Vol. 2, pp. 660-667, 2013.
- H.G. Davies, C. Bowman, S.P. Luby, “Cholera-Management and Prevention”, Journal of Infection, Vol. 74, pp. 66-73, 2017.
- El Hatri and C., Boumhidi, “Fuzzy Deep Learning based Urban Traffic Incident Detection”, Cognitive Systems Research, Vol. 50, pp. 206-213, 2017.
- G.L. Grinblat et al., “Deep Learning for Plant Identification using Vein Morphological Patterns”, Computers and Electronics in Agriculture, Vol. 127, pp. 418-424, 2016.
- Y. Guo et al., “Deep Learning for Visual Understanding: A Review”, Neurocomputing, Vol. 187, pp. 27-48, 2016.
- G.E. Hinton, “A Fast Learning Algorithm for Deep Belief Nets”, Neural Computing, Vol. 18, pp. 1527-1554, 2006.
- I.I. Hirschman, I.I. Widder, “The Convolution Transform”, Courier Corporation, 2012.
- A. Karpathy et al., “Largescale Video Classification with Recurrent Neural Networks”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1725-1732, 2014.
- A. Krizhevsky, I. Sutskever and G.E. Hinton, “ImageNet Classification with Deep Recurrent Neural Networks”, Proceedings of the 25th International Conference on Neural Information Processing Systems, Vol. 1, pp. 1097-1105, 2012.
- Y. Le Cun et al., “Handwritten Digit Recognition with a Back-Propagation Network”, Advances in Neural Information Processing Systems, Morgan Kauffmann Publishers Inc., pp. 396-404, 1990.
- The MNIST Database, Available at: http://yann.lecun.com/exdb/mnist/.
- S. Mallat, “Understanding Deep Recurrent Networks”, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 374, 2016.
- M. Lin, Q. Chen and S. Yan, “Network in Network”, Proceedings of International Conference on Learning Representations, 2014.
- G. Litjens et al., “A Survey on Deep Learning in Medical Image Analysis”, Medical Image Analysis, Vol. 42, pp. 60-88, 2017.
- Y. Liu and L. Wu, “Geological Disaster Recognition on Optical Remote Sensing Images Using Deep Learning”, Procedia Computer Science, Vol. 91, pp. 566-575, 2016.
- Y. Lu et al., “Identification of Rice Diseases using Deep Recurrent Neural Networks”, Neurocomputing, Vol. 267, pp. 378-384, 2017.
- F. Marechal, “Satellite Imaging and Vector-Borne Diseases: the Approach of the French National Space Agency (CNES)”, Geospatial Health, Vol. 3, No. 1, pp. 1-5, 2008.
- Analysis of Link Utilization Using Traffic Engineering Technique in Data Center Network
Abstract Views :147 |
PDF Views:0
Authors
Affiliations
1 Department of Business Administration and Information Systems, Arba Minch University, ET
1 Department of Business Administration and Information Systems, Arba Minch University, ET
Source
ICTACT Journal on Communication Technology, Vol 10, No 4 (2019), Pagination: 2079-2083Abstract
Data center networks are driven by intensive application like data mining, web searching and cloud computing. Nowadays, topologies in data center networks applied multi-ischolar_mained tree like Canonical Tree, and Fat-Tree. In multipath routing Equal cost multiple path (ECMP) forwarding is used in most of the current data center networks. However, it fails to increased path diversity. Modified data centric routing (DCR) algorithm in a data center environment based on the topologies such as canonical and fat-tree to increase the path diversity over unequal cost link. In proposed new algorithm to increase the link utilization ,efficient load balancing and the effects of increased packet reordering on application performance with MTCP and Packet scatter, can further reduce MLU, increase link utilization through DCR routing and better load balancing, finally increase the overall network performance.Keywords
Data Center Routing, Data Center Topology, Multipath Routing, Traffic Engineering.References
- F.P. Tso and D.P. Pezaros, “Improving Data Center Network Utilization using Near-Optimal Traffic Engineering”, IEEE Transactions on Parallel and Distributed Systems, Vol. 24, No. 6, pp. 1139-1148, 2012.
- A. Greenberg, P. Lahiri, D. A. Maltz, P. Patel and S. Sengupta, “Towards A Next Generation Data Center Architecture: Scalability and Commoditization”, Proceedings of ACM Workshop on Programmable Routers for Extensible Services of Tomorrow, pp. 57-62, 2008.
- Haijun Geng, Xingang Shi, Xia Yin, Zhiliang Wang and Shaoping Yin, “Algebra and Algorithms for Multipath QoS Routing in Link State Networks”, Journal of Communications and Networks, Vol. 19, No. 2, pp. 1-5, 2017.
- Alberto Dainotti, Antonio Pescape and Kimberly C. Claffy, “Issues and Future Directions in Traffic Classification”, IEEE Network, Vol. 26, No. 1, pp. 35-40, 2012.
- B. Heller, S. Seetharaman and P. Mahadevan, “ElasticTree: Saving Energy in Data Center Networks”, Proceedings of 7th USENIX Conference on Networked Systems Design and Implementation, pp. 1-17, 2010.
- Jochen W. Guck, Amaury Van Bemten, Martin Reisslein, Wolfgang Kellerer, “Unicast QoS Routing Algorithms for SDN: A Comprehensive Survey and Performance Evaluation”, IEEE Communications Surveys and Tutorials, Vol. 20, No. 1, pp. 388-415, 2017.
- Walid Khallef, Miklos Molnar, Abderrahim Bensliman and Sylvain Durand, “On the QoS Routing with RPL”, Proceedings of International Conference on Performance Evaluation and Modeling in Wired and Wireless Networks, pp. 1-5, 2017.
- A. Callado, C. Kamienski, S.N. Fernandes and D. Sadok, “A Survey on Internet Traffic Identification and Classification”, IEEE Communications Surveys and Tutorials, Vol. 11, No. 3, pp. 37-52, 2009.
- T. Karagiannis, A. Broido and M. Faloutsos, “Transport Layer Identification of P2P Traffic”, Proceedings of 4th ACM International Conference on Internet Measurement, pp. 121-134, 2004
- Shuangyin Ren, Wenhua Dou and Yu Wang, “A Deterministic Network Calculus Enabled QoS Routing on Software Defined Network”, Proceedings of IEEE 9th International Conference on Communication Software and Networks, pp. 1-5, 2017.
- O. Yeremenko, O. Lemeshko, O. Nevzorova and Ahmad M. Hailan, “Method of Hierarchical QoS Routing based on Network Resource Reservation”, Proceedings of IEEE 1st Ukraine Conference on Electrical and Computer Engineering, pp. 971-976, 2017.
- D. Xu, M. Chiang and J. Rexford, “Link-State Routing with Hop-by-Hop Forwarding Can Achieve Optimal Traffic Engineering”, Proceedings of 27th Conference on Computer Communications, pp. 1717-1730, 2011.
- M. Al-Fares, S. Radhakrishnan, B. Raghavan, N. Huang and A. Vahdat, “Hedera: Dynamic Flow Scheduling for Data Center Networks”, Proceedings of 27th USENIX Symposium on Networked Systems Design and Implementation, pp. 1-7, 2010.
- S. Sen, O. Spatscheck and D. Wang, “Accurate, Scalable in-Network Identification of P2P Traffic using Application Signatures”, Proceedings of 13th International Conference on World Wide Web, pp. 512-521, 2004.
- P. Haffner, S. Sen, O. Spatscheck and D. Wang, “ACAS: Automated Construction of Application Signatures”, Proceedings of International Conference and Workshop on Mining Network Data, pp. 197-202, 2005.
- O. Lemeshko, O. Yeremenko and Ahmad M. Hailan, “Design of QoS-Routing Scheme under the Timely Delivery Constraint”, Proceedings of 14th International Conference the Experience of Designing and Application of CAD Systems in Microelectronics, pp. 97-99, 2017.
- J.D.M. Bezerra, A.J. Pinheiro, C.P. De Souza and D.R. Campelo, “Performance Evaluation of Elephant Flow Predictors in Data Center Networking”, Future Generation Computer Systems, Vol. 102, pp. 952-964, 2020
- A. Alaa, A. Mohamed and T. Elfouly, “Energy-Cost-Distortion Optimization for Delay-Sensitive M-Health Applications”, Proceedings of International Symposium on Wireless Telecommunications, pp. 1-5, 2015.
- T. Bujlow, M.T. Riaz and J.M. Pedersen, “A Method for Assessing Quality of Service in Broadband Networks”, Proceedings of 14th International Conference on Advanced Communication Technology, pp. 826-831, 2012.
- Erico N. De Souza, Stan Matwin and Stenio Fernandes, “Traffic Classification with On-Line Ensemble Method”, Proceedings of International Conference on Global Information Infrastructure and Networking, pp. 1-4, 2013.