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Objective Manual evaluation of machine learning algorithms and selection of a suitable classifier from the list of available candidate classifiers, is highly time consuming and challenging task. If the selection is not carefully and accurately done, the resulting classification model will not be able to produce the expected performance results. In this study, we present an accurate multi-criteria decision making methodology (AMD) which empirically evaluates and ranks classifiers’ and allow end users or experts to choose the top ranked classifier for their applications to learn and build classification models for them. Methods and material Existing classifiers performance analysis and recommendation methodologies lack (a) appropriate method for suitable evaluation criteria selection, (b) relative consistent weighting mechanism, (c) fitness assessment of the classifiers’ performances, and (d) satisfaction of various constraints during the analysis process. To assist machine learning practitioners in the selection of suitable classifier(s), AMD methodology is proposed that presents an expert group-based criteria selection method, relative consistent weighting scheme, a new ranking method, called optimum performance ranking criteria, based on multiple evaluation metrics, statistical significance and fitness assessment functions, and implicit and explicit constraints satisfaction at the time of analysis. For ranking the classifiers performance, the proposed ranking method integrates Wgt.Avg.F-score, CPUTimeTesting, CPUTimeTraining, and Consistency measures using the technique for order performance by similarity to ideal solution (TOPSIS). The final relative closeness score produced by TOPSIS, is ranked and the practitioners select the best performance (top-ranked) classifier for their problems in-hand. Findings Based on the extensive experiments performed on 15 publically available UCI and OpenML datasets using 35 classification algorithms from heterogeneous families of classifiers, an average Spearman's rank correlation coefficient of 0.98 is observed. Similarly, the AMD method has showed improved performance of 0.98 average Spearman's rank correlation coefficient as compared to 0.83 and 0.045 correlation coefficient of the state-of-the-art ranking methods, performance of algorithms (PAlg) and adjusted ratio of ratio (ARR). Conclusion and implication The evaluation, empirical analysis of results and comparison with state-of-the-art methods demonstrate the feasibility of AMD methodology, especially the selection and weighting of right evaluation criteria, accurate ranking and selection of optimum performance classifier(s) for the user's application's data in hand. AMD reduces expert's time and efforts and improves system performance by designing suitable classifier recommended by AMD methodology. [ABSTRACT FROM AUTHOR]

Supporting safe and resilient authentication and integrity of digital images is of critical importance in a time of enormous creation and sharing of these contents. This paper presents an improved digital image watermarking model based on a coefficient quantization technique that intelligently encodes the owner’s information for each color channel to improve imperceptibility and robustness of the hidden information. Concretely, a novel color channel selection mechanism automatically selects the optimal HL4 and LH4 wavelet coefficient blocks for embedding binary bits by adjusting block differences, calculated between LH and HL coefficients of the host image. The channel selection aims to minimize the visual difference between the original image and the embedded image. On the other hand, the strength of the watermark is controlled by a factor to achieve an acceptable tradeoff between robustness and imperceptibility. The arrangement of the watermark pixels before shuffling and the channel into which each pixel is embedded is ciphered in an associated key. This key is utterly required to recover the original watermark, which is extracted through an adaptive clustering thresholding mechanism based on the Otsu’s algorithm. Benchmark results prove the model to support imperceptible watermarking as well as high robustness against common attacks in image processing, including geometric, non-geometric transformations, and lossy JPEG compression. The proposed method enhances more than 4 dB in the watermarked image quality and significantly reduces Bit Error Rate in the comparison of state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

A huge array of personalized healthcare and wellness systems are introduced into the portfolio of digital health and quantified-self movement in recent years. These systems share common capabilities including self-tracking/monitoring and self-quantifications, based on the raw sensory data. These capabilities provide solid ground for the users to be more aware of their health; however, such measures are inefficient for changing the unhealthy habits of the users. In order to induce healthy habits in the users, a system must be capable of generating context-aware personalized recommendations. The main obstacle in this regard is the contextual interpretation of recommendations based on user's current context and contextual preferences. To resolve these issues, we propose a methodology of cross-context interpretation of recommendations (CCIR) for personalized health and wellness services. The CCIR method adds additional capabilities to the traditional reasoning methods and builds advanced form of the reasoning with the incorporation of contextual factors in the process of interpretations of the recommendations. With CCIR, the self-quantification systems can be enhanced to generate personalized recommendations in addition to tracking, quantifying, and monitoring user activities. In order to validate the proposed CCIR methodology, a set of 40 contextual scenarios and corresponding recommendations are presented for the evaluation collected from 40 different end users and 10 domain experts. Using chi-square test evaluation, the results demonstrated acceptable “goodness of fit” indices for the system developed on proposed CCIR methodology with respect to the end users’ opinion. Also from the statistical observation, it is found that there exists a higher level agreement towards the system between the participants of both end users and experts. [ABSTRACT FROM AUTHOR]

Short Message Service (SMS) has become one of the most important media of communications due to the rapid increase of mobile users and it’s easy to use operating mechanism. This flood of SMS goes with the problem of spam SMS that are generated by spurious users. The detection of spam SMS has gotten more attention of researchers in recent times and is treated with a number of different machine learning approaches. Supervised machine learning approaches, used so far, demands a large amount of labeled data which is not always available in real applications. The traditional semi-supervised methods can alleviate this problem but may not produce good results if they are provided with only positive and unlabeled data. In this paper, we have proposed a novel semi-supervised learning method which makes use of frequent itemset and ensemble learning ( FIEL ) to overcome this limitation. In this approach, Apriori algorithm has been used for finding the frequent itemset while Multinomial Naive Bayes, Random Forest and LibSVM are used as base learners for ensemble learning which uses majority voting scheme. Our proposed approach works well with small number of positive data and different amounts of unlabeled dataset with higher accuracy. Extensive experiments have been conducted over UCI SMS spam collection data set, SMS spam collection Corpus v.0.1 Small and Big which show significant improvements in accuracy with very small amount of positive data. We have compared our proposed FIEL approach with the existing SPY-EM and PEBL approaches and the results show that our approach is more stable than the compared approaches with minimum support. [ABSTRACT FROM AUTHOR]