Fuzzy Case-Based Reasoning: Implementasi Logika Fuzzy pada Case-Based Reasoning

Penggunaan logika fuzzy untuk menangani masukan yang berupa linguistik telah dieksplorasi dan memberikan beberapa hasil uji terhadap data yang digunakan. Penelitian ini menggunakan data standar sebanyak 958 data dan masing-masing data memiliki 14 atribut yang kemudian dapat dibentuk menjadi Case-Based Reasoning (CBR). Nilai kemiripan diperoleh dari dua teknik similaritas, yaitu similaritas Fuzzy dan similaritas Nearest-Neighbour. Pengujian menggunakan sebanyak 81 data dengan rata-rata akurasi kemiripan sekitar 77% untuk similaritas Fuzzy dan 78,5% untuk Nearest-Neighbour. Tingkat akurasi similaritas Fuzzy lebih rendah dari Nearest-Neighbour, tetapi terdapat kelebihan jika menggunakan masukan bersifat linguistik, seperti lebih sederhana dan fleksibel dalam memasukkan data yang berbentuk linguistik (kata-kata)

Fuzzy Case-Based Reasoning: Implementasi Logika Fuzzy pada Case-Based Reasoning

Fuzzy Case-Based Reasoning: Implementasi Logika Fuzzy pada Case-Based Reasonin

A CASE-BASED REASONING SYSTEM FOR THE DIAGNOSIS OF INDIVIDUAL SENSITIVITY TO STRESS IN PSYCHOPHYSIOLOGY

Abstract Stress is an increasing problem in our present world. Especially negative stress could cause serious health problems if it remains undiagnosed/misdiagnosed and untreated. In stress medicine, clinicians' measure blood pressure, ECG, finger temperature and breathing rate during a number of exercises to diagnose stressrelated disorders. One of the physiological parameters for quantifying stress levels is the finger temperature measurement which helps the clinicians in diagnosis and treatment of stress. However, in practice, it is difficult and tedious for a clinician to understand, interpret and analyze complex, lengthy sequential sensor signals. There are only few experts who are able to diagnose and predict stress-related problems. A system that can help the clinician in diagnosing stress is important, but the large individual variations make it difficult to build such a system. This research work has investigated several artificial Intelligence techniques for the purpose of developing an intelligent, integrated sensor system for establishing diagnosis and treatment plan in the psychophysiological domain. To diagnose individual sensitivity to stress, case-based reasoning is applied as a core technique to facilitate experience reuse by retrieving previous similar cases. Furthermore, fuzzy techniques are also employed and incorporated into the case-based reasoning system to handle vagueness, uncertainty inherently existing in clinicians reasoning process. The validation of the approach is based on close collaboration with experts and measurements from twenty four persons used as reference. 39 time series from these 24 persons have been used to evaluate the approach (in terms of the matching algorithms) and an expert has ranked and estimated the similarity. The result shows that the system reaches a level of performance close to an expert. The proposed system could be used as an expert for a less experienced clinician or as a second option for an experienced clinician to their decision making process in stress diagnosis. Sammanfattning Den ökande stressnivån i vårt samhälle med allt högre krav och högt tempo har ett högt pris. Stressrelaterade problem och sjukdom är en stor samhällskostnad och speciellt om negativ stress förblir oupptäckt, eller ej korrekt identifierad/diagnostiserad och obehandlad under en längre tid kan den få alvarliga hälsoeffekter för individen vilket kan leda till långvarig sjukskrivning. Inom stressmedicinen mäter kliniker blodtryck, EKG, fingertemperatur och andning under olika situationer för att diagnostisera stress. Stressdiagnos baserat fingertemperaturen (FT) är något som en skicklig klinker kan utföra vilket stämmer med forskningen inom klinisk psykofysiologi. Emellertid i praktiken är det mycket svårt, och mödosamt för att en kliniker att i detalj följa och analysera långa serier av mätvärden och det finns endast mycket få experter som är kompetent att diagnostisera och/eller förutsäga stressproblem. Därför är ett system, som kan hjälpa kliniker i diagnostisering av stress, viktig. Men de stora individvariationerna och bristen av precisa diagnosregler gör det svårt att använda ett datorbaserat system. Detta forskningsarbete har tittat på flera tekniker och metoder inom artificiell intelligens för att hitta en väg fram till ett intelligent sensorbaserat system för diagnos och utformning av behandlingsplaner inom stressområdet. För att diagnostisera individuell stress har fallbaserat resonerande visat sig framgångsrikt, en teknik som gör det möjligt att återanvända erfarenhet, förklara beslut, genom att hämta tidigare liknande fingertemperaturprofilerar. Vidare används "fuzzy logic", luddig logik så att systemet kan hantera de inneboende vagheter i domänen. Metoder och algoritmer har utvecklats för detta. Valideringen av ansatsen baseras på nära samarbete med experter och mätningar från tjugofyra användare. Trettionio tidserier från dessa 24 personer har varit basen för utvärderingen av ansatsen, och en erfaren kliniker har klassificerat alla fall och systemet har visat sig producera resultat nära en expert. Det föreslagna systemet kan användas som ett referens för en mindre erfaren kliniker eller som ett "second opinion" för en erfaren kliniker i deras beslutsprocess. Dessutom har finger temperatur visat sig passa bra för användning i hemmet vid träning eller kontroll vilket blir möjligt med ett datorbaserat stressklassificeringssystem på exempelvis en PC med en USB fingertemperaturmätare. vii Acknowledgemen

Hybrid Recommender Systems: A Systematic Literature Review

Recommender systems are software tools used to generate and provide suggestions for items and other entities to the users by exploiting various strategies. Hybrid recommender systems combine two or more recommendation strategies in different ways to benefit from their complementary advantages. This systematic literature review presents the state of the art in hybrid recommender systems of the last decade. It is the first quantitative review work completely focused in hybrid recommenders. We address the most relevant problems considered and present the associated data mining and recommendation techniques used to overcome them. We also explore the hybridization classes each hybrid recommender belongs to, the application domains, the evaluation process and proposed future research directions. Based on our findings, most of the studies combine collaborative filtering with another technique often in a weighted way. Also cold-start and data sparsity are the two traditional and top problems being addressed in 23 and 22 studies each, while movies and movie datasets are still widely used by most of the authors. As most of the studies are evaluated by comparisons with similar methods using accuracy metrics, providing more credible and user oriented evaluations remains a typical challenge. Besides this, newer challenges were also identified such as responding to the variation of user context, evolving user tastes or providing cross-domain recommendations. Being a hot topic, hybrid recommenders represent a good basis with which to respond accordingly by exploring newer opportunities such as contextualizing recommendations, involving parallel hybrid algorithms, processing larger datasets, etc

Intelligent Feature Extraction, Data Fusion and Detection of Concrete Bridge Cracks: Current Development and Challenges

As a common appearance defect of concrete bridges, cracks are important indices for bridge structure health assessment. Although there has been much research on crack identification, research on the evolution mechanism of bridge cracks is still far from practical applications. In this paper, the state-of-the-art research on intelligent theories and methodologies for intelligent feature extraction, data fusion and crack detection based on data-driven approaches is comprehensively reviewed. The research is discussed from three aspects: the feature extraction level of the multimodal parameters of bridge cracks, the description level and the diagnosis level of the bridge crack damage states. We focus on previous research concerning the quantitative characterization problems of multimodal parameters of bridge cracks and their implementation in crack identification, while highlighting some of their major drawbacks. In addition, the current challenges and potential future research directions are discussed.Comment: Published at Intelligence & Robotics; Its copyright belongs to author

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

A First Step Towards Behavioral Coaching for Managing Stress: A Case Study on Optimal Policy Estimation with Multi-stage Threshold Q-learning

Psychological stress is a major contributor to the adoption of unhealthy behaviors, which in turn accounts for 41% of global cardiovascular disease burden. While the proliferation of mobile health apps has offered promise to stress management, these apps do not provide micro-level feedback with regard to how to adjust one's behaviors to achieve a desired health outcome. In this paper, we formulate the task of multi-stage stress management as a sequential decision-making problem and explore the application of reinforcement learning to provide micro-level feedback for stress reduction. Specifically, we incorporate a multi-stage threshold selection into Q-learning to derive an interpretable form of a recommendation policy for behavioral coaching. We apply this method on an observational dataset that contains Fitbit ActiGraph measurements and self-reported stress levels. The estimated policy is then used to understand how exercise patterns may affect users' psychological stress levels and to perform coaching more effectively