We present a corpus-based hybrid approach to music analysis and composition, which incorporates statistical, connectionist, and evolutionary components. Our framework employs artificial music critics, which may be tra...
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Body weight and length are often used as a reference index in classifying the nutritional status of infants and toddlers. In Posyandu Anggrek in Limo, Depok, Indonesia, the anthropometric index calculation used is sti...
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Body weight and length are often used as a reference index in classifying the nutritional status of infants and toddlers. In Posyandu Anggrek in Limo, Depok, Indonesia, the anthropometric index calculation used is still manual, using the z-score list or WHO NCHS standard deviation found in KMS (Healthy Cards for Toddlers). Nutritional status is only seen based on the colors in the KMS without calculation or looking at the index in the anthropometric table. Naïve Bayes, one of the methods used in classifying by calculating probability data, will be used in this study to measure the nutritional status of infants and children. The variables used in evaluating the nutritional status of infants and toddlers are data about body weight and body length. Based on the results of research and discussions conducted, 46 data about infants and toddlers are divided into 90% testing data and 10% training data by type of sampling using stratified sampling at RapidMiner. Based on the data obtained, the accuracy of Naïve Bayes in classifying the nutritional status of infants and toddlers up to 75%.
A statistical delay model for CMOS digital circuits called the "vector synthesis model" is proposed. The model provides a relationship between process random variables and a digital circuit path delay. A fir...
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A statistical delay model for CMOS digital circuits called the "vector synthesis model" is proposed. The model provides a relationship between process random variables and a digital circuit path delay. A first order coefficient vector (FOCV), which characterizes the drain current of a transistor, is introduced as a characteristic parameter of the cell delay. The circuit path delay is modeled by synthesizing a FOCV of the path using the FOCVs of the cells constituting the path. The simple structure of the vector synthesis model enables the reduction of simulation cost for a statistical analysis. The accuracy of the vector synthesis model has been verified experimentally. The deviation of the worst case delay from the result by SPICE Monte Carlo analysis is around 5%, whereas that of an usual corner (slow-slow and fast-fast) analysis is as high as 25%.
At present, the smartphone is equipped with several sensors such as Accelerometer, Gravity sensor, and Gyroscope which can be used to recognize human physical activities such as walking upstair and walking downstairs ...
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In surgery, the application of appropriate force levels is critical for the success and safety of a given procedure. While many studies are focused on measuring in situ forces, little attention has been devoted to rel...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
In surgery, the application of appropriate force levels is critical for the success and safety of a given procedure. While many studies are focused on measuring in situ forces, little attention has been devoted to relating these observed forces to surgical techniques. Answering questions like "Can certain changes to a surgical technique result in lower forces and increased safety margins?" could lead to improved surgical practice, and importantly, patient outcomes. However, such studies would require a large number of trials and professional surgeons, which is generally impractical to arrange. Instead, we show how robots can learn several variations of a surgical technique from a smaller number of surgical demonstrations and interpolate learnt behaviour via a parameterised skill model. This enables a large number of trials to be performed by a robotic system and the analysis of surgical techniques and their downstream effects on tissue. Here, we introduce a parameterised model of the elliptical excision skill and apply a Bayesian optimisation scheme to optimise the excision behaviour with respect to expert ratings, as well as individual characteristics of excision forces. Results show that the proposed framework can successfully align the generated robot behaviour with subjects across varying levels of proficiency in terms of excision forces.
E-Commerce is a prominent application of information technology in business, offering convenience in transactions. However, as more products and users join the platform, the complexity of E-Commerce systems increases....
E-Commerce is a prominent application of information technology in business, offering convenience in transactions. However, as more products and users join the platform, the complexity of E-Commerce systems increases. This necessitates the implementation of a recommendation system to enhance the user experience and address individual preferences. E-Commerce platforms often face the challenge of the cold start problem, where new products are introduced to the platform or when there is limited information available about new users. In such cases, traditional recommendation systems struggle to provide accurate recommendations due to the lack of historical data or user preferences. In this research, a comprehensive framework for an efficient multi-mode Hybrid Recommendation System is proposed, focusing on addressing the cold start problem. The system leverages user behavior tracking, specifically search history and product visit history, to capture user preferences effectively. Through experimental evaluation, the system demonstrates its adaptability to different dataset conditions, achieving a high precision rate of 90%. This research addresses the challenges of data sparsity and the cold start problem in recommendation systems, particularly in the E-Commerce context. The flexibility of the recommendation system ensures optimal recommendations in various scenarios, with potential applications beyond E-Commerce.
In this paper we propose a linear-time certifying algorithm for the single-source shortest-path problem capable of verifying graphs with positive, negative, and zero arc weights. Previously proposed linear-time approa...
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The authors present the architecture of a Web-based intelligent tutoring system (ITS) for teaching high school teachers how to use new technologies. It offers course units covering the needs of users with different kn...
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The authors present the architecture of a Web-based intelligent tutoring system (ITS) for teaching high school teachers how to use new technologies. It offers course units covering the needs of users with different knowledge levels and characteristics. It tailors the presentation of the educational material to the users' diverse needs by using AI techniques to specify each user's model as well as to make pedagogical decisions. This is achieved via an expert system that uses a hybrid knowledge representation formalism integrating symbolic rules with neurocomputing.
Training Artificial Neural Networks (ANNs) poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in trai...
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ISBN:
(纸本)9781665430654
Training Artificial Neural Networks (ANNs) poses a challenging and critical problem in machine learning. Despite the effectiveness of gradient-based learning methods, such as Stochastic Gradient Descent (SGD), in training neural networks, they do have several limitations. For instance, they require differentiable activation functions, and cannot optimize a model based on several independent non-differentiable loss functions simultaneously;for example, the F1-score, which is used during testing, can be used during training when a gradient-free optimization algorithm is utilized. Furthermore, the training (i.e., optimization of weights) in any DNN can be possible with a small size of the training dataset. To address these concerns, we propose an efficient version of the gradient-free Coordinate Search (CS) algorithm, an instance of General Pattern Search (GPS) methods, for training (i.e., optimizing) neural networks. The proposed algorithm can be used with non-differentiable activation functions and tailored to multi-objective/multi-loss problems. Finding the optimal values for weights of ANNs is a large-scale optimization problem. Therefore instead of finding the optimal value for each variable, which is the common technique in classical CS, we accelerate optimization and convergence by bundling the variables (i.e., weights). In fact, this strategy is a form of dimension reduction for optimization problems. Based on the experimental results, the proposed method is comparable with the SGD algorithm, and in some cases, it outperforms the gradient-based approach. Particularly, in situations with insufficient labeled training data, the proposed CS method performs better. The performance plots demonstrate a high convergence rate, highlighting the capability of our suggested method to find a reasonable solution with fewer function calls. As of now, the only practical and efficient way of training ANNs with hundreds of thousands of weights is gradient-based algorithms such
Ultra wide-band (UWB) radar imaging systems are a promising field of research as they cover a variety of applications. Among all UWB radar imaging methods, the time-reversal (TR) method enables high-resolution imaging...
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Ultra wide-band (UWB) radar imaging systems are a promising field of research as they cover a variety of applications. Among all UWB radar imaging methods, the time-reversal (TR) method enables high-resolution imaging in a multi path environment. Conventional TR methods have been applied to antenna array systems while our previous work proposed a type of TR method, namely the frequency-domain Decomposition de Operateur de Retournement Temporel (DORT) method, designed for a low-cost single antenna based system. Because the frequency-domain DORT method was developed assuming a point-like target, the performance of the method for a finite-sized target is unknown. In this study, we investigate numerically the performance of the frequency-domain DORT method by applying it to finite sized targets and evaluating the quality of the resultant images.
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