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The purpose of this paper is to enhance the performance of spammer identification problem in online social networks. Hyperparameter tuning has been performed by researchers in the past to enhance the performance of classifiers. The AdaBoost algorithm belongs to a class of ensemble classifiers and is widely applied in binary classification problems. A single algorithm may not yield accurate results. However, an ensemble of classifiers built from multiple models has been successfully applied to solve many classification tasks. The search space to find an optimal set of parametric values is vast and so enumerating all possible combinations is not feasible. Hence, a hybrid modified whale optimization algorithm for spam profile detection (MWOA-SPD) model is proposed to find optimal values for these parameters.

In this work, the hyperparameters of AdaBoost are fine-tuned to find its application to identify spammers in social networks. AdaBoost algorithm linearly combines several weak classifiers to produce a stronger one. The proposed MWOA-SPD model hybridizes the whale optimization algorithm and salp swarm algorithm.

The technique is applied to a manually constructed Twitter data set. It is compared with the existing optimization and hyperparameter tuning methods. The results indicate that the proposed method outperforms the existing techniques in terms of accuracy and computational efficiency.

The proposed method reduces the server load by excluding complex features retaining only the lightweight features. It aids in identifying the spammers at an earlier stage thereby offering users a propitious environment.

The primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an understanding about how well the proposed framework is evaluated and what type and ratio of missingness are addressed in the proposals. The review questions in this study are (1) what are the ML-based imputation methods studied and proposed during 2010–2020? (2) How the experimentation setup, characteristics of data sets and missingness are employed in these studies? (3) What metrics were used for the evaluation of imputation method?

The review process went through the standard identification, screening and selection process. The initial search on electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2,883. Most of the papers at this stage were not exactly an MVI technique relevant to this study. The literature reviews are first scanned in the title for relevancy, and 306 literature reviews were identified as appropriate. Upon reviewing the abstract text, 151 literature reviews that are not eligible for this study are dropped. This resulted in 155 research papers suitable for full-text review. From this, 117 papers are used in assessment of the review questions.

This study shows that clustering- and instance-based algorithms are the most proposed MVI methods. Percentage of correct prediction (PCP) and root mean square error (RMSE) are most used evaluation metrics in these studies. For experimentation, majority of the studies sourced the data sets from publicly available data set repositories. A common approach is that the complete data set is set as baseline to evaluate the effectiveness of imputation on the test data sets with artificially induced missingness. The data set size and missingness ratio varied across the experimentations, while missing datatype and mechanism are pertaining to the capability of imputation. Computational expense is a concern, and experimentation using large data sets appears to be a challenge.

It is understood from the review that there is no single universal solution to missing data problem. Variants of ML approaches work well with the missingness based on the characteristics of the data set. Most of the methods reviewed lack generalization with regard to applicability. Another concern related to applicability is the complexity of the formulation and implementation of the algorithm. Imputations based on k-nearest neighbors (kNN) and clustering algorithms which are simple and easy to implement make it popular across various domains.

The purpose of this paper is to show a bibliographical review of the applications of the MTS throughout the time and the different fields.

The Mahalanobis-Taguchi system (MTS) is an analytical method used for the diagnosis and/or pattern recognition of multivariate data for quantitative decision making.

Its scope is very broad, ranging from engineering, medicine, education, and manufacturing, among others. This work presents a classification of the literature in the following areas of the MTS: introduction of the method, cases of study/application, comparison with other methods, integration and development of the MTS with other methods, construction of Mahalanobis space, dimensional reduction and threshold establishment. It realized a wide search of the publications in magazines and congresses.

This paper is a summary of the main applications, contributions and changes to MTS.

The purpose of this paper is to present and analyze the current literature related to developing and improving the Mahalanobis-Taguchi system (MTS) and to present the shortcomings related to this method for future research.

In this paper, articles in the literature are classified to give an overview on the MT strategy. For this purpose, 46 articles are considered for classification from 2000 to 2013 on the basis of: MTS contribution area, description of the issue, and results.

In this paper a review on the concepts and operations of the MTS was provided as a new method in the field of pattern recognition, multivariable diagnosis, and forecasting. A large number of studies were performed in recent years consisting of developing MTS and MTS case studies. The analysis of the articles showed the fields of MTS which had more potential for future studies and developing. The comparison of the MTS to other methods and the selection of the normal group for constructing the Mahalanobis space have received the most attention by researchers. In addition, several studies concentrated on the use of other methods instead of design of experiments, finding applications for multiclass MTS and finding an alternative for the SN ratio.

This paper contains the publications in the field of MTS chronologically and shows different areas for developing and case studies. It will be useful to researchers and professionals who are interested in pattern recognition, multivariate analysis, and forecasting.

The purpose of this paper is to improve the classification of families having children with affective-behavioral maladies, and thus giving the families a suitable orientation.

The proposed methodology includes three steps. Step 1 addresses initial data preprocessing, by noise filtering or data condensation. Step 2 performs a multiple feature sets selection, by using genetic algorithms and rough sets. Finally, Step 3 merges the candidate solutions and obtains the selected features and instances.

The new proposal show very good results on the family data (with 100 percent of correct classifications). It also obtained accurate results over a variety of repository data sets. The proposed approach is suitable for dealing with non-symmetric similarity functions, as well as with high-dimensionality mixed and incomplete data.

Previous work in the state of the art only considers instance selection to preprocess the schools for children with affective-behavioral maladies data. This paper explores using a new combined instance and feature selection technique to select relevant instances and features, leading to better classification, and to a simplification of the data.

Multitasking machining (MTM) systems have become increasingly sophisticated and expensive capital equipment. The lack of practical guidelines for selection of these machines can lead to significant undesirable machine attributes, application mismatch, and longer return on investment. The purpose of this paper is to provide an insight to numerous features and configurations of MTM systems and to present several application‐based selection guidelines.

A taxonomy of MTM systems is developed based on the number of axes of motions, tooling and spindle systems. Practical guidelines for general and advance features are presented with special regard to multi‐axis and multi‐spindle features.

MTM systems are capable of meeting several production goals such as cycle time reduction, minimizing non‐value added times and concurrent processing of multiple parts. However, they possess inherent programming challenges due to their complex configuration and simultaneous machining functions.

The diversity of system configurations demand a decision support system, such as a rule‐based expert system to capture the many variations of MTM systems.

This paper should be useful to decision makers in industry or academia who are involved in selection of MTM systems.

Social scientific research is having a substantial impact on eyewitness identification procedural reforms. Police agencies in the USA have changed their eyewitness practices based on the results of social scientific research. The purpose of this paper is to contribute new knowledge by using a unique set of data to describe detailed aspects of eyewitness identification procedures conducted as part of robbery investigations in Houston, TX.

Robbery investigators completed surveys following identification procedures conducted during a six-month period of time. The sample includes 975 identification procedures. The analysis describes important features of identification procedures and places results in the context of existing research.

Results show that photo spreads were the most frequently used lineup procedure and selection outcomes were similar to recent field studies conducted in the USA. Results also show that the type of procedure, presence of a weapon, cross-race identifications, and viewing opportunity were significantly correlated with selection outcomes.

Police are reforming their eyewitness identification procedures based on findings from social science research. The study measures and describe the characteristics of a large sample eyewitness procedures conducted by investigators in the field.

This paper deals with the organizing of interactive product development. Developing products in interaction between firms may provide benefits in terms of specialization, increased innovation, and possibilities to perform development activities in parallel. However, the differentiation of product development among a number of firms also implies that various dependencies need to be dealt with across firm boundaries. How dependencies may be dealt with across firms is related to how product development is organized. The purpose of the paper is to explore dependencies and how interactive product development may be organized with regard to these dependencies.

The analytical framework is based on the industrial network approach, and deals with the development of products in terms of adaptation and combination of heterogeneous resources. There are dependencies between resources, that is, they are embedded, implying that no resource can be developed in isolation. The characteristics of and dependencies related to four main categories of resources (products, production facilities, business units and business relationships) provide a basis for analyzing the organizing of interactive product development.

Three in-depth case studies are used to explore the organizing of interactive product development with regard to dependencies. The first two cases are based on the development of the electrical system and the seats for Volvo’s large car platform (P2), performed in interaction with Delphi and Lear respectively. The third case is based on the interaction between Scania and Dayco/DFC Tech for the development of various pipes and hoses for a new truck model.

The analysis is focused on what different dependencies the firms considered and dealt with, and how product development was organized with regard to these dependencies. It is concluded that there is a complex and dynamic pattern of dependencies that reaches far beyond the developed product as well as beyond individual business units. To deal with these dependencies, development may be organized in teams where several business units are represented. This enables interaction between different business units’ resource collections, which is important for resource adaptation as well as for innovation. The delimiting and relating functions of the team boundary are elaborated upon and it is argued that also teams may be regarded as actors. It is also concluded that a modular product structure may entail a modular organization with regard to the teams, though, interaction between business units and teams is needed. A strong connection between the technical structure and the organizational structure is identified and it is concluded that policies regarding the technical structure (e.g. concerning “carry-over”) cannot be separated from the management of the organizational structure (e.g. the supplier structure). The organizing of product development is in itself a complex and dynamic task that needs to be subject to interaction between business units.

In outside constructions (e.g. aircraft frames, bridges, tanks and ships) real‐life noises reduce significantly the capability of location and characterization of crack events. Among the most important types of noise is the rain, producing a signal similar to crack. This paper seeks to present a robust crack detection system with simultaneous raining conditions and additive white‐Gaussian noise at −20 to 20 dB signal‐to‐noise ratio (SNR).

The proposed crack detection system consists of two sequentially, connected modules: the feature extraction module where 15 robust features are derived from the signal and a radial basis function neural network is built up in the pattern classification module to extract the crack events.

The evaluation process is carried out in a database consisting of over 4,000 simulated cracks and drops signals. The analysis showed that the detection accuracy using the most robust 15 features ranges from 77.7 to 93 percent in noise‐free environment. This is a promising method for non‐destructive testing (NDT) by acoustic emission method of aircraft frame structures in extremely noisy conditions.

Continuous monitoring of crack events in the field requires the development of advance noise reduction and signal identification techniques. Robust detection of crack signals in noisy environment, including raining drops, improves significantly the reliability of real‐time monitoring systems in large and complex constructions and in adverse weather conditions.

As far as is known this is the first time that an efficient system is presented and evaluated which deals with the problem of crack detection in adverse environment including both stationary and non‐stationary noise components. Moreover, it provides further information on the engineering and efficiency problems associated with NDT techniques in the aircraft industry.