Graph based gene/protein prediction and clustering over uncertain medical databases.

Clustering over protein or gene data is now a popular issue in biomedical databases. In general, large sets of gene tags are clustered using high computation techniques over gene or protein distributed data. Most of the traditional clustering techniques are based on subspace, hierarchical and partitioning feature extraction. Various clustering techniques have been proposed in the literature with different cluster measures, but their performance is limited due to spatial noise and uncertainty. In this paper, an improved graph-based clustering technique is proposed for the generation of efficient gene or protein clusters over uncertain and noisy data. The proposed graph-based visualization can effectively identify different types of genes or proteins along with relational attributes. Experimental results show that the proposed graph model more effectively clusters complex gene or protein data when compared with conventional clustering approaches

Improving the family orientation process in Cuban Special Schools trough Nearest Prototype classification

Cuban Schools for children with Affective – Behavioral Maladies (SABM) have as goal to accomplish a major change in children behavior, to insert them effectively into society. One of the key elements in this objective is to give an adequate orientation to the children’s families; due to the family is one of the most important educational contexts in which the children will develop their personality. The family orientation process in SABM involves clustering and classification of mixed type data with non-symmetric similarity functions. To improve this process, this paper includes some novel characteristics in clustering and prototype selection. The proposed approach uses a hierarchical clustering based on compact sets, making it suitable for dealing with non-symmetric similarity functions, as well as with mixed and incomplete data. The proposal obtains very good results on the SABM data, and over repository databases

SIMULASI PERILAKU JAMA’AH SAAT MENGELILINGI KA’BAH MENGGUNAKAN ALGORITMA FLOCKING DAN A*

Abstrak. Simulasi kerumunan dan gerak manusia merupakan sebuah animasi yang lagi trend dalam produksi film dan game. Mensimulasikan kerumunan manusia seperti di dunia nyata akan menjadi sebuah pemodelan yang interaktif dan realisitis. Banyak hal yang dapat disimulasikan untuk simulasi kerumunan, seperti kerumunan orang menaiki atau menuruni anak tangga, kerumunan orang menonton pertandingan di stadion olahraga, evakuasi bencana, bahkan kegiatan ritual haji/umrah. Pada ritual haji/umrah, ada beberapa kegiatan yang selalu dipenuhi dengan kerumunan orang atau jama’ah. Para ja-ma’ah mengelilingi Ka’bah sebanyak tujuh kali yang dikenal dengan thawaf. Ritual tersebut melibatkan jama’ah yang besar selama kegiatan haji/umrah. Karena jumlah jama’ah nya yang besar penting sekali untuk memahami, memodelkan perilaku dan pergerakan orang banyak untuk meningkatkan teknik manajemen kerumunan dan keamanan bagi para jama’ah. Pada penelitian ini bertujuan untuk mensimulasikan perilaku jamaa’ah saat thawaf.  Simulasi ini menggunakan metode flocking yang diterapkan pada jama’ah untuk menjaga agar jama’ah tidak saling bertabrakan antar lainnya, menghindari hambatan statis, dan mengikuti arah leader dan metode A* yang diterapkan pada jama’ah digunakan untuk pencarian target berupa waypoint yang ada disekitar Ka’bah sehingga jama’ah dapat bergerak sesuai dengan alur thawaf. Hasil penelitian menunjukkan banyaknya jumlah leader dapat mempengaruhi meningkatnya frekuensi tabrakan jama’ah saat melakukan thawaf. Pemilihan waktu jeda munculnya jama’ah dan kecepatan tiap leader mempengaruhi waktu jama’ah menyelesaikan thawaf. Simulasi ini diaplikasikan menggunakan Game Engine Unity 3D dan berbasis tiga dimensi.   Kata Kunci: Simulasi Kerumunan, Thawaf, Perilaku, Flocking, penentuan jalur, A

Adaptive firefly algorithm for hierarchical text clustering

Text clustering is essentially used by search engines to increase the recall and precision in information retrieval. As search engine operates on Internet content that is constantly being updated, there is a need for a clustering algorithm that offers automatic grouping of items without prior knowledge on the collection. Existing clustering methods have problems in determining optimal number of clusters and producing compact clusters. In this research, an adaptive hierarchical text clustering algorithm is proposed based on Firefly Algorithm. The proposed Adaptive Firefly Algorithm (AFA) consists of three components: document clustering, cluster refining, and cluster merging. The first component introduces Weight-based Firefly Algorithm (WFA) that automatically identifies initial centers and their clusters for any given text collection. In order to refine the obtained clusters, a second algorithm, termed as Weight-based Firefly Algorithm with Relocate (WFAR), is proposed. Such an approach allows the relocation of a pre-assigned document into a newly created cluster. The third component, Weight-based Firefly Algorithm with Relocate and Merging (WFARM), aims to reduce the number of produced clusters by merging nonpure clusters into the pure ones. Experiments were conducted to compare the proposed algorithms against seven existing methods. The percentage of success in obtaining optimal number of clusters by AFA is 100% with purity and f-measure of 83% higher than the benchmarked methods. As for entropy measure, the AFA produced the lowest value (0.78) when compared to existing methods. The result indicates that Adaptive Firefly Algorithm can produce compact clusters. This research contributes to the text mining domain as hierarchical text clustering facilitates the indexing of documents and information retrieval processes

Auto Organization and Optimization in Heterogeneous Wireless Mesh Networks

Abstract-

Unsupervised mining of activities for smart home prediction

This paper addresses the problem of learning the Activities of Daily Living (ADLs) in smart home for cognitive assistance to an occupant suffering from some type of dementia, such as Alzheimer's disease. We present an extension of the Flocking algorithm for ADL clustering analysis. The Flocking based algorithm does not require an initial number of clusters, unlike other partition algorithms such as K-means. This approach allows us to learn ADL models automatically (without human supervision) to carry out activity recognition. By simulating a set of real case scenarios, an implementation of this model was tested in our smart home laboratory, the LIARA

Modelo de un sistema manejador de comunidades autorganizativo para un sistema operativo web multiagente

A model of the simulation and functional evaluation for a Community Manager System (CMS), on a Multiagent Web Operating System (MWOS) is presented. Following a reference design and concepts associated with swarm intelligence, the evaluation focused on auto-organization and emergent community management requirements using a gathering algorithm based on the behavioral emergent patterns of ant colonies. Both, Intra and Inter-wise CMS resources were searched using a dynamic auto-organizative management approach according to CMS requirements and the SWOM’s objectives.Este artículo describe la simulación y evaluación funcional de un Sistema Manejador de Comunidades (SMC) para un Sistema Operativo WEB Multiagente (SOWM), siguiendo un diseño de referencia y los conceptos asociados al área de Inteligencia de Enjambre (“Swarm Intelligence”) evaluando así, la autorganización y emergencia en la gestión de comunidades que requiere este tipo de sistemas. Para ello, se ha empleado un algoritmo de agrupamiento basado en el comportamiento emergente de las hormigas, para recoger y depositar cadáveres y formar pilas de ellos. Siguiendo un esquema de gestión de comunidades dinámica, autorganizativa y emergente, se realizó la búsqueda de servicios y recursos a nivel Intra e Inter en el SMC de acuerdo con los requerimientos y objetivos del SOWM y sus subsistemas

A flocking-like technique to perform semi-supervised learning

We present a nature-inspired semi-supervised learning technique based on the flocking formation of certain living species like birds and fishes. Each data item is treated as an individual in the flock. Starting from random directions, each data item moves according to its surrounding items, by getting closer to them (but not too much close) and taking the same direction of motion. Labeled items play special roles, ensuring that data from different classes will belong to different, distant flocks. Experiments on both artificial and benchmark datasets were performed and show its classification accuracy. Despite the rich behavior, we argue that this technique has a sub-quadratic asymptotic time complexity, thus being feasible to be used on large datasets. In order to achieve such performance, a space-partitioning technique is introduced. We also argue that the richness behind this dynamic, self-organizing model is quite robust and may be used to do much more than simply propagating the labels from labeled to unlabeled data. It could be used to determine class overlapping, wrong labeling, etc.The State of São Paulo Research Foundation (FAPESP)Brazilian National Research Council (CNPq

Simulasi Pergerakan Evakuasi Bencana Tsunami Menggunakan Algoritma Boids Dan A Star

Musibah bencana alam tsunami yang terjadi menerjang Indonesia khususnya Aceh dan sekitarnya yang memakan banyak korban, telah memberikan gambaran perlunya evakuasi dini pada saat terjadi suatu musibah, khususnya Tsunami. Penelitian ini mensimulasikan pergerakan kerumunan orang untuk bergerak menuju suatu titik evakuasi pada saat terjadinya gempa bumi yang diperkirakan menimbulkan bahaya tsunami. Simulasi ini menentukan jarak terdekat/terpendek dari posisi individu, meminimalkan terjadinya tabrakan dalam menghindari segala hambatan yang ditemui baik hambatan statis maupun dinamis yang ditemui pada saat melewati rute jalan yang telah ditentukan. Pada penelitian ini, penerapan model kerumunan diselesaikan dengan menggunakan algoritma boids, yang didalamnya terdiri dari algoritma flocking, obstancle avoidance, collition detection, dengan ditambahkan dengan algoritma A Star pathfinding. Setelah diadakan simulasi dan penelitian, maka pergerakan kerumunan manusia yang tersebar didalam area evakuasi, dengan menggunakan algoritma boids dan A Star dapat menghindari halangan dinding dan dapat bergerak menuju titik evakuasi tanpa terjebak di area jalur tusnami.Pertambahan jumlah kerumunan membutuhkan selang waktu yang lebih menuju target yang diinginkan. ================================================================================================= Natural disasters tsunami hit Indonesia in particular Aceh and its surroundings which claimed many victims, has provided an overview the need for early evacuation in the event of a disaster, especially tsunami. This study simulates the movement of a crowd of people to move towards an evacuation point at the time of the earthquake were estimated pose a danger of tsunami. This simulation determines the shortest distance or shortest of the individual positions, minimizing collisions in avoiding any obstacles encountered both static and dynamic obstacles encountered in as it passes through the predetermined path. In this study, the application of the crowd solved by using boids algorithm, which involves a series of flocking algorithms, obstancle avoidance, collition detection , to be added to the algorithm A Star pathfinding. After held simulation and research, the movement of crowd human spread in evacuation areas, using algorithms boids and A Star can avoid obstacles and walls can move towards a point evacuation without being stuck in a crowd path area tsunami. Increase the number of crowd requires more intervals towards the desired target