Information Aggregation in Intelligent Systems Using Generalized Operators

Aggregation of information represented by membership functions is a central matter in intelligent systems where fuzzy rule base and reasoning mechanism are applied. Typical examples of such systems consist of, but not limited to, fuzzy control, decision support and expert systems. Since the advent of fuzzy sets a great number of fuzzy connectives, aggregation operators have been introduced. Some families of such operators (like t-norms) have become standard in the field. Nevertheless, it also became clear that these operators do not always follow the real phenomena. Therefore, there is a natural need for finding new operators to develop more sophisticated intelligent systems. This paper summarizes the research results of the authors that have been carried out in recent years on generalization of conventional operators

Inferring Group Processes from Computer-Mediated Affective Text Analysis

Political communications in the form of unstructured text convey rich connotative meaning that can reveal underlying group social processes. Previous research has focused on sentiment analysis at the document level, but we extend this analysis to sub-document levels through a detailed analysis of affective relationships between entities extracted from a document. Instead of pure sentiment analysis, which is just positive or negative, we explore nuances of affective meaning in 22 affect categories. Our affect propagation algorithm automatically calculates and displays extracted affective relationships among entities in graphical form in our prototype (TEAMSTER), starting with seed lists of affect terms. Several useful metrics are defined to infer underlying group processes by aggregating affective relationships discovered in a text. Our approach has been validated with annotated documents from the MPQA corpus, achieving a performance gain of 74% over comparable random guessers

Inferring Group Processes from Computer-Mediated Affective Text Analysis

Political communications in the form of unstructured text convey rich connotative meaning that can reveal underlying group social processes. Previous research has focused on sentiment analysis at the document level, but we extend this analysis to sub-document levels through a detailed analysis of affective relationships between entities extracted from a document. Instead of pure sentiment analysis, which is just positive or negative, we explore nuances of affective meaning in 22 affect categories. Our affect propagation algorithm automatically calculates and displays extracted affective relationships among entities in graphical form in our prototype (TEAMSTER), starting with seed lists of affect terms. Several useful metrics are defined to infer underlying group processes by aggregating affective relationships discovered in a text. Our approach has been validated with annotated documents from the MPQA corpus, achieving a performance gain of 74% over comparable random guessers

Troubleshooting Ink Jet Printing Of Cotton Substrates Using A Knowledge-based Expert System

(Doktora) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2012(PhD) -- İstanbul Technical University, Institute of Science and Technology, 2012Bu çalışmada, pamuklu kumaşların ink jet baskısında karşılaşılan hataların belirlenmesi ve çözülmesine yönelik uzman sistem geliştirilmesi amaçlanmıştır. Bu amaçla literatür detaylı olarak taranmış, bununla birlikte baskıda uzman kişilerle de tartışılarak ink jet baskıda sık karşılaşılan on üç hata belirlenmiştir. Bunlar, kafa sürtmesi, renksiz bölge, yanlış renkli bölge mürekkep damlası(mürekkep lekesi), enine çizgilenme, çarpıklık, boya akması, desen kayması, kumaş sararması, düşük sürtme haslığı, düşük yıkama haslığı, renk şiddeti farklılığı/ton farklılığı, düşük keskinlik, beyaz veya solgun bölge olarak adlandırılmıştır. Daha sonra bu hataların nedeni olabilecek altmış bir adet sebep, detaylı literatür taraması ve yine uzman kişilerle yapılan görüşmeler sonunda belirlenmiştir. Bu hatalarla sebepler arasındaki ilişkiyi belirleyebilmek amacıyla uzmanlara sormak üzere anket hazırlanmıştır. Ankete katılan uzmanların her bir hata ile her bir sebebin ilişkisini beşli likert skalası kullanarak belirlemeleri istenmiştir. Ankete katılan bütün uzmanların cevap sayıları dikkate alınarak her bir hata ve altmış bir sebep arasında sayısal bir ilişki kurulabilmesi amacıyla çeşitli istatistik yöntemlerinden yararlanılmıştır. Sonuç olarak, sisteme entegre edilen çıkarım motorunun hataların sebeplerini ortak sebeplerden başlayarak belirlemesiyle iyi bir performans ortaya koymuştur. Ayrıca, sistem ink jet baskıda karşılaşılan problemlerin çözümünde iyi bir araç olarak kullanılabileceği gösterilmiştir. Bununla birlikte, yapılan anket değerlendirmeleri sonucunda, ankete katılan uzmanların ortak paydada da buluşamadığı ortaya çıkmıştır. Bu sebeple böyle bir sistemin geliştirilmesi hatalara objektif bir çözüm sunabilmesi açısından önemlidir. Ayrıca sistem, bu alanda yeni çalışmaya başlayanlar için iyi bir başvuru kaynağı ve eğitim aracı olarak da kullanılabilmektedir. Bundan sonraki aşamalarda, sistemin gerçek üretim hatalarıyla denemeleri yapılarak, uzman kişilerin hatayı çözerken ortaya koydukları yaklaşımla karşılaştırılmasının yapılması gerekmektedir. Bu şekilde, üretim esnasında daha efektif kullanılabilen bir başvuru kaynağı olarak da kullanılabilecektir.In this study, it is aimed to develop an expert system for troubleshooting of faults encountered in ink jet printing of cotton substrates. The possible faults may be observed during ink jet printing, prior to printing, such as fabric production or preparation, and after printing, such as fixation. Hence, at the analysis and selection of the most encountered faults in ink jet printing, the processes, which start with cotton production and end at fixation, are examined. After the detailed review of the literature and interviews with the experts, thirteen symptoms are selected as the most encountered problems in ink jet printing of cotton substrates. In addition, sixty-one causes are suggested as the possible causes of thirteen symptoms. Fifteen experts are asked to match each symptoms with sixty-one causes by using a five point likert scale, including most likely, likely, not sure, least likely and not related. In addition, the knowledge acquired from the survey and literature is embodied to the system. A different approach is adopted for the inference of the system in order to solve the problems that are selected by the users of software. The system demonstrates a good performance with embodied inference engine, which starts to solve problem from the common cause in the case of multiple selection of the faults. Moreover, the system shows that, it can be used as a tool for troubleshooting of ink jet printing of the cotton substrates. In addition, it is also possible to use the system as a training tool for the people who are new at ink jet printing.DoktoraPh

NeutroAlgebra Theory, volume I

Neutrosophic theory and its applications have been expanding in all directions at an astonishing rate especially after of the introduction the journal entitled “Neutrosophic Sets and Systems”. New theories, techniques, algorithms have been rapidly developed. One of the most striking trends in the neutrosophic theory is the hybridization of neutrosophic set with other potential sets such as rough set, bipolar set, soft set, hesitant fuzzy set, etc. The different hybrid structures such as rough neutrosophic set, single valued neutrosophic rough set, bipolar neutrosophic set, single valued neutrosophic hesitant fuzzy set, etc. are proposed in the literature in a short period of time. Neutrosophic set has been an important tool in the application of various areas such as data mining, decision making, e-learning, engineering, medicine, social science, and some more

Development of Multiple Linear Regression Model and Rule Based Decision Support System to Improve Supply Chain Management of Road Construction Projects in Disaster Regions

Supply chain operations of construction industry including road projects in disaster regions results in exceeding project budget and timelines. In road construction projects, supply chain with poor performance can affect efficiency and completion time of the project. This is also the case of the road projects in disaster areas. Disaster areas consider both natural and man-made disasters. Few examples of disaster zones are; Pakistan, Afghanistan, Iraq, Sri Lanka, India, Japan, Haiti and many other countries with similar environments. The key factors affecting project performance and execution are insecurity, uncertainties in demand and supply, poor communication and technology, poor infrastructure, lack of political and government will, unmotivated organizational staff, restricted accessibility to construction materials, legal hitches, multiple challenges of hiring labour force and exponential construction rates due to high risk environment along with multiple other factors. The managers at all tiers are facing challenges of overrunning time and budget of supply chain operations during planning as well as execution phase of development projects. The aim of research is to develop a Multiple Linear Regression Model (MLRM) and a Rule Based Decision Support System by incorporating various factors affecting supply chain management of road projects in disaster areas in the order of importance. This knowledge base (KB) (importance / coefficient of each factor) will assist infrastructure managers (road projects) and practitioners in disaster regions in decision making to minimize the effect of each factor which will further help them in project improvement. Conduct of Literature Review in the fields of disaster areas, supply chain operational environments of road project, statistical techniques, Artificial Intelligence (AI) and types of research approaches has provided deep insights to the researchers. An initial questionnaire was developed and distributed amongst participants as pilot project and consequently results were analysed. The results’ analysis enabled the researcher to extract key variables impacting supply chain performance of road project. The results of questionnaire analysis will facilitate development of Multiple Linear Regression Model, which will eventually be verified and validated with real data from actual environments. The development of Multiple Linear Regression Model and Rule Based Decision Support System incorporating all factors which affect supply chain performance of road projects in disastrous regions is the most vital contribution to the research. The significance and novelty of this research is the methodology developed that is the integration of those different methods which will be employed to measure the SCM performance of road projects in disaster areas