<abstract xmlns="http://www.w3.org/1999/xhtml">The estimation of the generalization error of a trained classifier by means of a test set is one of the oldest problems in pattern recognition and machine learning. Despite this problem has been addressed for several decades, it seems that the last word has not been written yet, because new proposals continue to appear in the literature. Our objective is to survey and compare old and new techniques, in terms of quality of the estimation, easiness of use, and rigorousness of the approach, so to understand if the new proposals represent an effective improvement on old ones.</abstract>

A Survey of old and New Results for the Test Error Estimation of a Classifier

<abstract xmlns="http://www.w3.org/1999/xhtml"> Prompt and proper management of healthcare waste is critical to minimize the negative impact on the environment. Improving the prediction accuracy of the healthcare waste generated in hospitals is essential and advantageous in effective waste management. This study aims at developing a model to predict the amount of healthcare waste. For this purpose, three models based on artificial neural network (ANN), multiple linear regression (MLR), and combination of ANN and genetic algorithm (ANN-GA) are applied to predict the waste of 50 hospitals in Iran. In order to improve the performance of ANN for prediction, GA is applied to find the optimal initial weights in the ANN. The performance of the three models is evaluated by mean squared errors. The obtained results have shown that GA has significant impact on optimizing initial weights and improving the performance of ANN.</abstract>

Application of Artificial Neural Network and Genetic Algorithm to Healthcarewaste Prediction

<abstract xmlns="http://www.w3.org/1999/xhtml"> This paper presents a comparative analysis of complexity between the B-TREE and the Binary Search Algorithms, both theoretically and experimentally, to evaluate their efficiency in finding overlap of classes for students and teachers in the University Course Timetabling Problem (UCTP). According to the theory, B-TREE Search complexity is lower than Binary Search. The performed experimental tests showed the B-TREE Search Algorithm is more efficient than Binary Search, but only using a dataset larger than 75 students per classroom.</abstract>

B-Tree Algorithm Complexity Analysis to Evaluate the Feasibility of its Application in the University Course Timetabling Problem

<abstract xmlns="http://www.w3.org/1999/xhtml">In this research, a new method for automatic detection and classification of suspected breast cancer lesions using ultrasound images is proposed. In this fully automated method, de-noising using fuzzy logic and correlation among ultrasound images taken from different angles is used. Feature selection using combination of sequential backward search, sequential forward search and distance-based methods is obtained. A new segmentation method based on automatic selection of seed points and region growing is proposed and classification of lesions into two malignant and benign classes using combination of AdaBoost, Artificial Neural Network and Fuzzy Support Vector Machine classifiers and majority voting is implemented.</abstract>

A Novel Approach for Automatic Detection and Classification of Suspicious Lesions in Breast Ultrasound Images

<abstract xmlns="http://www.w3.org/1999/xhtml"> This work presents an analysis of Higher Order Singular Value Decomposition (HOSVD) applied to reduction of dimensionality of 3D mesh animations. Compression error is measured using three metrics (MSE, Hausdorff, MSDM). Results are compared with a method based on Principal Component Analysis (PCA) and presented on a set of animations with typical mesh deformations.</abstract>

Dimensionality Reduction of Dynamic Mesh Animations Using HO-SVD

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Journal of Artificial Intelligence and Soft Computing Research

 Print version published by: The Journal of the Polish Neural Network Society and the University of Social Sciences in Lodz  Editor-in-Chief: Prof. Leszek Rutkowski, IEEE FellowFrequency of publishing: quarterlyMonths of publication: January, April, July, October  ISSN (print): 2083-2567ISSN (on-line): 2449-6499  Journal of Artificial Intelligence and Soft Computing Research is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.  Why JAISCR?   Premier source of high quality research,  We have practice in publishing innovative articles dealing with various aspects of artificial intelligence methods and soft computing,  Extensive scope,  We are focused on permanent development of our journal.   JAISCR research areas  JAISCR publishes high quality, innovative research results in various areas of artificial intelligence and soft computing. These areas include, but are not limited to:   AI in Modelling and Simulation,  AI in Scheduling and Optimization,  Automated Reasoning and Inference,  Bioinformatics,  Cognitive Aspects of AI,  Computer Vision and Speech Understanding,  Data Mining,  Distributed Intelligent Processing,  Evolutionary Design,  Expert Systems,  Fuzzy Modelling and Control,  Hardware Implementations,  Heuristic Search,  Hybrid Models,  Information Retrieval,  Intelligent Database Systems,  Knowledge Engineering,  Mechatronics,  Multi-agent Systems,  Natural Language Processing,  Neural Network Theory and Architectures,  Neuro-informatics and Bio-inspired Models,  Pattern Recognition,  Robotics and Related Fields,  Rough Sets Theory: Foundations and Applications,  Supervised and Unsupervised Learning,  Swarm Intelligence and Systems,  Web Intelligence Applications &amp; Search,  Various Applications.   Submitting to JAISCR - a great advantage for the authorsWhy?   Fair and thorough peer review (at least three referees are required for the acceptance of each article),  Fast and efficient publication process,  Polishing the structure and technical format of each submission,  Open access (all articles published in JAISCR are open access - freely available on Sciendo platform),  Permanence (because of the open access form, permanent accessibility is assured),  High visibility within the field (the author's work is freely accessible to a global audience. In addition, articles are available through Sciendo),  Promotion of the published papers in the scientific community.   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 