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Many investigations have performed sentiment analysis to gauge public opinions in various languages, including English, French, Chinese, and others. The most spoken language in South Asia is Urdu. However, less work has been carried out on Urdu, as Roman Urdu is also used in social media (Urdu written in English alphabets); therefore, it is easy to use it in English language processing software. Lots of data in Urdu, as well as in Roman Urdu, are posted on social media sites such as Instagram, Twitter, Facebook, etc. This research focused on the collection of pure Urdu Language data and the preprocessing of the data, applying feature extraction, and innovative methods to perform sentiment analysis. After reviewing previous efforts, machine learning and deep learning algorithms were applied to the data. The obtained results were compared, and hybrid methods were also recommended in this research, enabling new avenues to conduct Urdu language data sentiment analysis. [ABSTRACT FROM AUTHOR]

Considered as one of the emerging evolutionary algorithms, the Dendritic Cell Algorithm (DCA) is based on the behavior of specific immune agents; known as Dendritic Cells (DCs). Studies related with DCA are increasingly becoming popular and this is due to the worthy characteristics expressed by the algorithm as it exhibits several potentially beneficial features for binary classification problems. Yet, according to our best knowledge, there is no study summarizing the basic features of the DCA developed versions all in one paper. Hence, in this paper we aim at summarizing the powerful characteristics of the DCA while making a general review of the algorithm. We aim at studying the various versions of DCA while highlighting their characteristics, advantages and limitations. Based on this study and from the conducted conclusions, we intend to generate a well-studied novel DC classifier based on the positive aspects reflected by the previously proposed DCA versions. Results show that our proposed algorithm succeeds in obtaining significantly improved classification accuracy. [ABSTRACT FROM AUTHOR]

This study presents a data-driven model for identifying failure modes (FMs) and predicting the corresponding punching shear resistance of slab-column connections with shear reinforcement. An experimental database that contains 328 test results is used to determine nine input variables based on the punching shear mechanism. A comparison is conducted between three typical machine learning (ML) approaches: random forest (RF), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost) and two hybrid optimized algorithms: grey wolf optimization (GWO) and whale optimization algorithm (WOA). It was found that the XGBoost classifier had the highest accuracy rate, precision, and recall values for FM identification. In testing, WOA-XGBoost has the best accuracy in predicting punching shear resistance, with R2, MAE, and RMSE values of 0.9642, 0.087 MN, and 0.126 MN, respectively. However, a comparison between experimental values and calculated values derived from classical analytical methods clearly demonstrates that existing design codes need to be improved. Additionally, Shapley additive explanations (SHAP) were applied to explain the model's predictions, with factors categorized according to their impact on failure modes and punching shear resistance. By modifying these parameters, punching resistance can be improved while reducing unpredictable failure. With the proposed hybrid algorithms, it is possible to determine the failure modes and the punching shear resistance of slabs during the preliminary stages of the construction. [ABSTRACT FROM AUTHOR]

In the process of selecting a machine learning algorithm to solve a problem, questions like the following commonly arise: (1) Are some algorithms basically the same, or are they fundamentally different? (2) How different? (3) How do we measure that difference? (4) If we want to combine algorithms, what algorithms and combinators should be tried? This research proposes COD (Classifier Output Difference) distance as a diversity metric. COD separates difference from accuracy, COD goes beyond accuracy to consider differences in output behavior as the basis for comparison. The paper extends earlier on COD by giving a basic comparison to other diversity metrics, and by giving an example of using COD data as a predictive model from which to select algorithms to include in an ensemble. COD may fill a niche in metalearning as a predictive aid to selecting algorithms for ensembles and hybrid systems by providing a simple, straightforward, computationally reasonable alternative to other approaches. [ABSTRACT FROM AUTHOR]

The use of simulation and reinforcement learning can be viewed as a flexible approach to aid managerial decision-making, particularly in the face of growing complexity in manufacturing and logistic systems. Efficient supply chains heavily rely on steamlined warehouse operations, and therefore, having a well-informed storage location assignment policy is crucial for their improvement. The traditional methods found in the literature for tackling the storage location assignment problem have certain drawbacks, including the omission of stochastic process variability or the neglect of interaction between various warehouse workers. In this context, we explore the possibilities of combining simulation with reinforcement learning to develop effective mechanisms that allow for the quick acquisition of information about a complex environment, the processing of that information, and then the decision-making about the best storage location assignment. In order to test these concepts, we will make use of the FlexSim commercial simulator. [ABSTRACT FROM AUTHOR]

Network intrusion detection is a relevant cybersecurity research field. The growing number of intrusions requires more sophisticated methods to protect computer networks. Various machine learning algorithms are used to detect network intrusions and anomalies, but their accuracy is limited. In this research, we address the problem of improving network-level intrusion detection by applying hybrid machine-learning algorithms. The paper proposes three new hybrid machine learning methods and investigates their accuracy using two publicly available datasets CSE-CIC-IDS2018 and NSW-NB-15. In order to increase the accuracy of the classification models, hyperparameter optimization was performed. The iteration method and the Chi-square χ2 test were used to identify significant features of the data set. Analyzing the research results, it was found that the highest network anomaly recognition accuracy of 99.34% was achieved by applying a hybrid algorithm consisting of a decision tree, naive Bayesian, and multilayer perceptron algorithms. Achieved result is 3.13% higher than the best accuracy achieved by individual machine learning algorithms. In order to comprehensively evaluate the studied machine learning algorithms and their suitability for detecting intrusions in a computer network, the algorithms were ranked using the SCR, DR, FR ranking methods. Article in Lithuanian. Įsilaužimų aptikimas kompiuterių tinkluose taikant hibridinius mašininio mokymosi metodus Santrauka Viena iš aktualių kibernetinės saugos tyrimų krypčių – tai įsilaužimų arba anomalijų aptikimas kompiuterių tinkle. Įsilaužimų skaičius nuolat didėja, o taikomos įsilaužimo technikos ir metodai sudėtingėja, todėl siekiant apsaugoti kompiuterių tinklą, reikia taikyti vis sudėtingesnius apsaugos metodus. Tinklo įsilaužimams ir anomalijoms nustatyti taikomi įvairūs mašininio mokymosi algoritmai, tačiau jų tikslumas yra ribotas. Siekiama pagerinti tinklo anomalijų aptikimą, taikomi hibridiniai mašininio mokymosi algoritmai. Straipsnyje pasiūlyti trys nauji hibridiniai mašininio mokymosi algoritmai, ištirtas jų tikslumas naudojant du viešai prieinamus duomenų rinkinius, t. y. CSE-CIC-IDS2018 ir NSW-NB-15. Siekiant padidinti klasifikavimo modelių tikslumą, buvo atliktas hiperparametrų optimizavimas. Reikšmingiems duomenų rinkinio požymiams nustatyti taikytas iteracijų metodas ir Chi kvadrato χ2 testas. Analizuojant tyrimo rezultatus, nustatyta, kad aukščiausias tinklo anomalijų atpažinimo tikslumas 99,34 % buvo pasiektas taikant hibridinį algoritmą, sudarytą iš sprendimų medžio, naivaus Bajeso ir daugiasluoksnio perceptrono algoritmų rinkinio. Šis rezultatas yra 3,13 % geresnis, lyginant su geriausiu tikslumu, gautu taikant atskirus mašininio mokymosi algoritmus. Siekiant kompleksiškai įvertinti tirtus mašininio mokymosi algoritmus ir jų tinkamumą įsilaužimams kompiuterių tinkle aptikti, algoritmai buvo sureitinguoti taikant SCR, DR, FR reitingavimo metodus. Reikšminiai žodžiai: tinklo anomalijos, mašininis mokymasis, χ2 Chi kvadratu testas, hiperparametrai, hibridiniai algoritmai.

This paper reviews the existing literature on the combination of metaheuristics with machine learning methods and then introduces the concept of learnheuristics, a novel type of hybrid algorithms. Learnheuristics can be used to solve combinatorial optimization problems with dynamic inputs (COPDIs). In these COPDIs, the problem inputs (elements either located in the objective function or in the constraints set) are not fixed in advance as usual. On the contrary, they might vary in a predictable (non-random) way as the solution is partially built according to some heuristic-based iterative process. For instance, a consumer’s willingness to spend on a specific product might change as the availability of this product decreases and its price rises. Thus, these inputs might take different values depending on the current solution configuration. These variations in the inputs might require from a coordination between the learning mechanism and the metaheuristic algorithm: at each iteration, the learning method updates the inputs model used by the metaheuristic.