Pembandingan Kompleksitas Algoritma Pada Penyelesaian Permasalahan Graph Isomorphism

Graf adalah sebuah model yang direpresentasikan sebagai kumpulan titik atau simpul dan beberapa garis yang menghubungkan antar titik atau yang disebut sebagai edge. Graf bisa digunakan sebagai model berbagai macam relasi dalam berbagai macam bidang seperti fisika, biologi, dan teknologi informasi. Salah satu masalah yang muncul di graf adalah masalah isomorphism.Graf A dan graf B bisa dikatakan isomorphic jika semua simpul di graf A bisa dipetakan ke simpul di graf B secara bijeksi. Untuk bisa mengetahui apakah kedua graf bersifat isomorphic ada beberapa pilihan algoritma yang bisa digunakan seperti VF2, Schmidt & Druffel fast backtracking dan lain lain.Pada tugas akhir ini, akan diselesaian permasalahan dengan judul “ISOMORPH” pada situs penilaian daring SPOJ. Pada permasalahan tersebut akan terdapat beberapa graf yang harus dicari pasangan isomorphic nya. Permasalahan tersebut akan diselesaikan dengan menggunakan 2 macam algoritma yaitu algoritma VF2 dan algoritma Schmidt & Druffel fast Backtracking

Genetic Algorithm-based Mapper to Support Multiple Concurrent Users on Wireless Testbeds

Communication and networking research introduces new protocols and standards with an increasing number of researchers relying on real experiments rather than simulations to evaluate the performance of their new protocols. A number of testbeds are currently available for this purpose and a growing number of users are requesting access to those testbeds. This motivates the need for better utilization of the testbeds by allowing concurrent experimentations. In this work, we introduce a novel mapping algorithm that aims to maximize wireless testbed utilization using frequency slicing of the spectrum resources. The mapper employs genetic algorithm to find the best combination of requests that can be served concurrently, after getting all possible mappings of each request via an induced sub-graph isomorphism stage. The proposed mapper is tested on grid testbeds and randomly generated topologies. The solution of our mapper is compared to the optimal one, obtained through a brute-force search, and was able to serve the same number of requests in 82.96% of testing scenarios. Furthermore, we show the effect of the careful design of testbed topology on enhancing the testbed utilization by applying our mapper on a carefully positioned 8-nodes testbed. In addition, our proposed approach for testbed slicing and requests mapping has shown an improved performance in terms of total served requests, about five folds, compared to the simple allocation policy with no slicing.Comment: IEEE Wireless Communications and Networking Conference (WCNC) 201

Expert System for Crop Disease based on Graph Pattern Matching: A proposal

Para la agroindustria, las enfermedades en cultivos constituyen uno de los problemas más frecuentes que generan grandes pérdidas económicas y baja calidad en la producción. Por otro lado, desde las ciencias de la computación, han surgido diferentes herramientas cuya finalidad es mejorar la prevención y el tratamiento de estas enfermedades. En este sentido, investigaciones recientes proponen el desarrollo de sistemas expertos para resolver este problema haciendo uso de técnicas de minería de datos e inteligencia artificial, como inferencia basada en reglas, árboles de decisión, redes bayesianas, entre otras. Además, los grafos pueden ser usados para el almacenamiento de los diferentes tipos de variables que se encuentran presentes en un ambiente de cultivos, permitiendo la aplicación de técnicas de minería de datos en grafos, como el emparejamiento de patrones en los mismos. En este artículo presentamos una visión general de las temáticas mencionadas y una propuesta de un sistema experto para enfermedades en cultivos, basado en emparejamiento de patrones en grafos.For agroindustry, crop diseases constitute one of the most common problems that generate large economic losses and low production quality. On the other hand, from computer science, several tools have emerged in order to improve the prevention and treatment of these diseases. In this sense, recent research proposes the development of expert systems to solve this problem, making use of data mining and artificial intelligence techniques like rule-based inference, decision trees, Bayesian network, among others. Furthermore, graphs can be used for storage of different types of variables that are present in an environment of crops, allowing the application of graph data mining techniques like graph pattern matching. Therefore, in this paper we present an overview of the above issues and a proposal of an expert system for crop disease based on graph pattern matching