Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

EXPLOITING HIGHER ORDER UNCERTAINTY IN IMAGE ANALYSIS

Soft computing is a group of methodologies that works synergistically to provide flexible information processing capability for handling real-life ambiguous situations. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning, and partial truth in order to achieve tractability, robustness, and low-cost solutions. Soft computing methodologies (involving fuzzy sets, neural networks, genetic algorithms, and rough sets) have been successfully employed in various image processing tasks including image segmentation, enhancement and classification, both individually or in combination with other soft computing techniques. The reason of such success has its motivation in the fact that soft computing techniques provide a powerful tools to describe uncertainty, naturally embedded in images, which can be exploited in various image processing tasks. The main contribution of this thesis is to present tools for handling uncertainty by means of a rough-fuzzy framework for exploiting feature level uncertainty. The first contribution is the definition of a general framework based on the hybridization of rough and fuzzy sets, along with a new operator called RF-product, as an effective solution to some problems in image analysis. The second and third contributions are devoted to prove the effectiveness of the proposed framework, by presenting a compression method based on vector quantization and its compression capabilities and an HSV color image segmentation technique

PENENTUAN JUMLAH UKURAN PAKAIAN OPTIMAL SEBAGAI RANCANGAN SISTEM UKURAN PAKAIAN ANAK LAKI-LAKI DI INDONESIA DENGAN ANALISIS KESEIMBANGAN DAN FUZZY C MEANS BERBASIS ARTIFICIAL BEE COLONY

Jumlah ukuran adalah salah satu hal terpenting dalam merancang sistem ukuran pakaian. Semakin banyak jumlah ukuran pakaian maka akan semakin pas dengan bentuk tubuh konsumen sehingga kepuasan konsumen dapat tercapai dari sisi ketepatan ukuran pakaian dengan ukuran tubuh. Namun dari sisi produsen, semakin besar jumlah ukuran pakaian akan berdampak pada biaya setup ataupun penambahan lini produksi akibat penambahan variasi jumlah ukuran pakaian. Penelitian ini akan mengembangkan sistem ukuran pakaian baru dimana akan melihat titik titik seimbang antara biaya produksi dengan jumlah ukuran maksimal. Titik seimbang itu adalah jumlah ukuran optimal yang dapat memenuhi kebutuhan konsumen dan produsen secara bersama-sama. Metode FCM ABC akan digunakan untuk mengelompokkan ukuran tubuh menjadi beberapa kelompok. Sampel menggunakan 106 anak laki-laki umur 8-10 tahun. Penelitian terdiri dari tahapan yaitu Analisis faktor, Penentuan jumlah kelompok optimal, dan Evaluasi. Tujuh kelompok ukuran pakaian yang optimal dihasilkan. Nilai aggregate loss memenuhi syarat validasi sehingga dapat dikatakan pengembangan sistem ukuran baru dapat digunakan sebagai teknik untuk mendapatkan jumlah ukuran pakaian yang optimal

Cloud Service Provider Evaluation System using Fuzzy Rough Set Technique

Cloud Service Providers (CSPs) offer a wide variety of scalable, flexible, and cost-efficient services to cloud users on demand and pay-per-utilization basis. However, vast diversity in available cloud service providers leads to numerous challenges for users to determine and select the best suitable service. Also, sometimes users need to hire the required services from multiple CSPs which introduce difficulties in managing interfaces, accounts, security, supports, and Service Level Agreements (SLAs). To circumvent such problems having a Cloud Service Broker (CSB) be aware of service offerings and users Quality of Service (QoS) requirements will benefit both the CSPs as well as users. In this work, we proposed a Fuzzy Rough Set based Cloud Service Brokerage Architecture, which is responsible for ranking and selecting services based on users QoS requirements, and finally monitor the service execution. We have used the fuzzy rough set technique for dimension reduction. Used weighted Euclidean distance to rank the CSPs. To prioritize user QoS request, we intended to use user assign weights, also incorporated system assigned weights to give the relative importance to QoS attributes. We compared the proposed ranking technique with an existing method based on the system response time. The case study experiment results show that the proposed approach is scalable, resilience, and produce better results with less searching time.Comment: 12 pages, 7 figures, and 8 table

Solutions to decision-making problems in management engineering using molecular computational algorithms and experimentations

制度:新 ; 報告番号:甲3368号 ; 学位の種類:博士(工学) ; 授与年月日:2011/5/23 ; 早大学位記番号:新568

Advances in Data Mining Knowledge Discovery and Applications

Advances in Data Mining Knowledge Discovery and Applications aims to help data miners, researchers, scholars, and PhD students who wish to apply data mining techniques. The primary contribution of this book is highlighting frontier fields and implementations of the knowledge discovery and data mining. It seems to be same things are repeated again. But in general, same approach and techniques may help us in different fields and expertise areas. This book presents knowledge discovery and data mining applications in two different sections. As known that, data mining covers areas of statistics, machine learning, data management and databases, pattern recognition, artificial intelligence, and other areas. In this book, most of the areas are covered with different data mining applications. The eighteen chapters have been classified in two parts: Knowledge Discovery and Data Mining Applications