Our medical world is replete with clinical data but this data is rarely automatically exploited for bringing more health to our society. Many researches have been conducted in Medical Data Mining, but almost all of them have focused on diagnosing the diseases not treating the patients. In this paper we propose the Causality-based Medical Diagnosis and Treatment System, which can be used to diagnose a patient disease and suggest treatments to her/him. Our proposed system has three main subsystems: Causal Network Extractor, Diagnosis Subsystem and Treatment Suggesting Subsystem. Two main features of our system are: it takes solely observational data as input data and uses the causality-based action mining methodology. Action Mining is relatively a new trend in Data Mining which aims in proposing more actionable patterns to domain experts. We have implemented and tested our proposed method on some real and synthesized data. The results show superiority of our method over current state of the art method. Taking into account the causality results in more reliable treatments and makes it possible to use this system in real world situations. پرونده مقاله

Quantum-dot Cellular Automata (QCA) technology is attractive due to its low power consumption, fast speed and small dimension, therefore, it is a promising alternative to CMOS technology. In QCA, configuration of charges plays the role which is played by current in CMOS. This replacement provides the significant advantages. Additionally, exclusive-or (XOR) gate is a useful building block in many important circuits such as full adder (FA). In this paper we propose a novel design for two-input XOR gate in QCA. Two-input XORs could be utilized in constructing three-input ones which are widely used. The proposed XOR gate is the smallest design among the two-input XORs and it achieves significant improvements in terms of complexity and latency in comparison to the only existing similarly designed gate. Contrary to the common designing method which uses the logic function of XOR, the gate is constructed based on explicit interactions between QCA cells. The simulation results have been verified using the QCADesigner. پرونده مقاله

Recommender Systems are part of our life nowadays. In almost every big business, recommender systems are suggesting items to users automatically. The only factor that traditional recommender systems take into account is accuracy. They try to estimate user-item ratings as accurate as possible to recommend the more preferable items to users. But from the supplier point of view, profit is the most desirable achievement. In this paper we have proposed a profit-aware recommender system based on the multi-objective genetic algorithm. Our proposed method consider two objectives at the same time: Profit and Accuracy. Profit is amount of profit that company will gain of selling items and accuracy measures how much recommendations are close to user preferences. the NSGA-II as the most successful MO-GA has been selected here and its Crossover and Mutation operations have been designed. Our method and traditional collaborative-filtering method have been implemented and tested on three different datasets: Movielens, Netflix and Yahoo. Results confirm that in both accuracy and net-profit our method prevail over CF method. پرونده مقاله