Decentralized recommender system for ambient intelligence of tourism destinations serious game using known and unknown rating approach.

Tourism destinations serious game (TDSG) requires the ability to respond to players through recommendations for selecting appropriate tourist destinations for them as potential tourists. This research utilizes ambient intelligence technology to regulate the response visualized through a choice of serious game scenarios. This research uses the Multi-Criteria Recommender System (MCRS) to produce recommendations for selecting tourist destinations as a reference for selecting scenario visualizations. Recommender systems require a decentralized, distributed, and secure data-sharing concept to distribute data and assignments between nodes. We propose using the Ethereum blockchain platform to handle data circulation between parts of the system and implement decentralized technology. We also use the known and unknown rating (KUR) approach to improve the system's ability to generate recommendations for players who can provide rating values or those who cannot. This study uses the tourism theme of Batu City, Indonesia, so we use personal characteristics (PC) and rating of destinations attribute (RDA) data for tourists in that city. The test results show that the blockchain can handle decentralized data-sharing well to ensure PC and RDA data circulation between nodes. MCRS has produced recommendations for players based on the KUR approach, indicating that the known rating has better accuracy than the unknown rating. Furthermore, the player can choose and run the tour visualization through game scenarios that appear based on the recommendation ranking results.

Optimizing player engagement in an immersive serious game for soil tillage base on Pareto optimal strategies.

In most cases, problems that increase player involvement in immersive serious games do so by combining fun elements with a specific purpose. Previous studies have produced models of soil porosity and plow force that use the speed of plowing, the angle of the plow's eye, and the depth of the plow as the basis for a design strategy in immersion serious games. However, these studies have not been able to show the optimal strategy of engagement of the player in the game. In the domain of serious game concept learning, strategies can be formed based on real conditions or data from experimental results. In a serious game, the aim is to increase the player's knowledge so that the player gains knowledge by coming up with strategies to play the game. This research aims to increase the engagement of players by means of multi-objective optimization based on Pareto optima, with the objectivity of soil porosity and plow force that is affected by the speed of plowing, the angle of the plow's eye, and the depth of the plow. The results of this optimization are used as a basis for the design of strategies in a serious game in the form of Hierarchy Finite State Machine (HFSM). From the results of the study, it was found that there is an optimal area for the game strategy that is also an indicator of how to successfully process the soil tillage using a moldboard plow.

Osteoarthritis classification using self organizing map based on gabor kernel and contrast-limited adaptive histogram equalization.

Localization is the first step in osteoarthritis (OA) classification. Manual classification, however, is time-consuming, tedious, and expensive. The proposed system is designed as decision support system for medical doctors to classify the severity of knee OA. A method has been proposed here to localize a joint space area for OA and then classify it in 4 steps to classify OA into KL-Grade 0, KL-Grade 1, KL-Grade 2, KL-Grade 3 and KL-Grade 4, which are preprocessing, segmentation, feature extraction, and classification. In this proposed system, right and left knee detection was performed by employing the Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the template matching. The Gabor kernel, row sum graph and moment methods were used to localize the junction space area of knee. CLAHE is used for preprocessing step, i.e.to normalize the varied intensities. The segmentation process was conducted using the Gabor kernel, template matching, row sum graph and gray level center of mass method. Here GLCM (contrast, correlation, energy, and homogeinity) features were employed as training data. Overall, 50 data were evaluated for training and 258 data for testing. Experimental results showed the best performance by using gabor kernel with parameters α=8, θ=0, Ψ=[0 π/2], γ=0,8, N=4 and with number of iterations being 5000, momentum value 0.5 and α0=0.6 for the classification process. The run gave classification accuracy rate of 93.8% for KL-Grade 0, 70% for KL-Grade 1, 4% for KL-Grade 2, 10% for KL-Grade 3 and 88.9% for KL-Grade 4.