Browsing by Author "Farah Sarwar"

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    Critical analysis of hopfield's neural network model and heuristic algorithm for shortest path computation for routing in computer networks
    (IEEE, 2012) Farah Sarwar; Abdul Aziz Bhatti
    Shortest path routing and its computation is a crucial point in computer networks, and has significant impact on overall network's performance. Being an issue of salient importance, many algorithms were proposed for shortest path computation and are still under research for more enhancements. Hopfield proposed a neural network based architecture for such optimization problems. Mehmet and Park Keum suggested improved energy functions for this neural network to implement it for routing in computer networks. A * search algorithm is a heuristic based approach, with the properties of Dijikstra algorithm and is used for same purpose. Performances of both approaches are compared and results are analyzed.
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    Critical analysis of hopfield's neural network model for tsp and its comparison with heuristic algorithm for shortest path computation
    (IEEE, 2012) Farah Sarwar; Abdul Aziz Bhatti
    For shortest path computation, Travelling-Salesman problem is NP-complete and is among the intensively studied optimization problems. Hopfield and Tank's proposed neural network based approach, for solving TSP, is discussed. Since original Hopfield's model suffers from some limitations as the number of cities increase, some modifications are discussed for better performance. With the increase in the number of cities, the best solutions provided by original Hopfield's neural network were considered to be far away from those provided by Lin and Kernighan using Heuristic algorithm. Results of both approaches are compared for different number of cities and are analyzed properly
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    Efficient shortest path routing in computer communication networks using neural network and heuristic algorithm
    (University of Management and Technology, 2011) Farah Sarwar
    Computer Networks are a breakthrough in current communication technologies providing an always-on and ubiquitous connectivity to virtually millions of users with the score still mounting up. This increased usage strongly requires enhancement in network infrastructure and improvements in management to facilitate users with better quality services. As the rate of users, who are transmitting data through a network link, increases the quality of service has to be compromised if not improved. Routing, which is a major aspect of this domain, has significant impact on the effectiveness of Communication Systems. Enhanced technologies should be introduced to let the system assist problems in a better way, preventing data loss and degradation in quality. Efficient routing algorithm should strive for the most appropriate and shortest path to route data through a network. Prior information of routers and data links, which helps to create paths, is required in almost every technique. A search algorithm, exploiting the properties of mathematical routing algorithm as well as of heuristics, can compute shortest path between given pair of routers more efficiently. On the other hand artificial intelligence can be very helpful in this domain. Artificial Neural networks, a domain of artificial intelligence, have found their way in engineering as well as Medical Sciences. The particular adaptive ability of neural networks for dynamic situations becomes a strongest feature which renders it best suitable for dynamic systems. John Hopfield used this feature and found their application in NP-complete optimization problems. Therefore, neural networks similar to Hopfield's can also help to enhance shortest path computational techniques in routing. A* search algorithm and neural network are used to find shortest routes for unicast routing problems. Computer simulations are used to analyze and compare the results for different network sizes. A* search algorithm outperforms the neural network so far; however, limitations of neural networks are discussed.
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    Efficient shortest path routing in computer communication networks using neural network and heuristic algorithm
    (UMT.Lahore, 2011) Farah Sarwar
    Computer Networks are a breakthrough in current communication technologies providing an always-on and ubiquitous connectivity to virtually millions of users with the score still mounting up. This increased usage strongly requires enhancement in network infrastructure and improvements in management to facilitate users with better quality services. As the rate of users, who are transmitting data through a network link, increases the quality of service has to be compromised if not improved. Routing, which is a major aspect of this domain, has significant impact on the effectiveness of Communication Systems. Enhanced technologies should be introduced to let the system assist problems in a better way, preventing data loss and degradation in quality. Efficient routing algorithm should strive for the most appropriate and shortest path to route data through a network. Prior information of routers and data links, which helps to create paths, is required in almost every technique. A search algorithm, exploiting the properties of mathematical routing algorithm as well as of heuristics, can compute shortest path between given pair of routers more efficiently. On the other hand artificial intelligence can be very helpful in this domain. Artificial Neural networks, a domain of artificial intelligence, have found their way in engineering as well as Medical Sciences. The particular adaptive ability of neural networks for dynamic situations becomes a strongest feature which renders it best suitable for dynamic systems. John Hopfield used this feature and found their application in NP-complete optimization problems. Therefore, neural networks similar to Hopfield’s can also help to enhance shortest path computational techniques in routing. A* search algorithm and neural network are used to find shortest routes for unicast routing problems. Computer simulations are used to analyze and compare the results for different network sizes. A* search algorithm outperforms the neural network so far; however, limitations of neural networks are discussed.
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    Efficient shortest path routing in computer communication networks using neural network and heuristic algorithm
    (University of Management and Technology Lahore, 2011) Farah Sarwar
    Computer Networks are a breakthrough in current communication technologies providing an always-on and ubiquitous connectivity to virtually millions of users with the score still mounting up. This increased usage strongly requires enhancement in network infrastructure and improvements in management to facilitate users with better quality services. As the rate of users, who are transmitting data through a network link, increases the quality of service has to be compromised if not improved. Routing, which is a major aspect of this domain, has significant impact on the effectiveness of Communication Systems. Enhanced technologies should be introduced to let the system assist problems in a better way, preventing data loss and degradation in quality. Efficient routing algorithm should strive for the most appropriate and shortest path to route data through a network. Prior information of routers and data links, which helps to create paths, is required in almost every technique. A search algorithm, exploiting the properties of mathematical routing algorithm as well as of heuristics, can compute shortest path between given pair of routers more efficiently. On the other hand artificial intelligence can be very helpful in this domain. Artificial Neural networks, a domain of artificial intelligence, have found their way in engineering as well as Medical Sciences. The particular adaptive ability of neural networks for dynamic situations becomes a strongest feature which renders it best suitable for dynamic systems. John Hopfield used this feature and found their application in NP-complete optimization problems. Therefore, neural networks similar to Hopfield's can also help to enhance shortest path computational techniques in routing. A* search algorithm and neural network are used to find shortest routes for unicast routing problems. Computer simulations are used to analyze and compare the results for different network sizes. A* search algorithm outperforms the neural network so far; however, limitations of neural networks are discussed.
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    Use of optimal homotopy asymptotic method for fractional order nonlinear fredholm integro-differential equations
    (Science International Lahore, 2015) Farah Sarwar; Shaukat Iqbal
    In this study, optimal homotopy asymptotic method (OHAM), a semi-numerical technique is formulated for solving nonlinear Fredholm integro-differential equations of fractional order to check the effectiveness and performance of the method. It is observed that the formulation is easy to implement, quite valuable to handle fractional applications and yield tremendous results at minimum computational cost. The computational results of some of the test problems reveal that OHAM is well-organized, very effective, and simple and are in excellent agreement with exact solutions.