Gamed-based is a new stochastic metaheuristics optimization category that is inspired by traditional or digital game genres. Unlike SI-based algorithms, individuals do not work together with the goal of defeating other individuals and winning the game. Battle royale optimizer (BRO) is a Gamed-based metaheuristic optimization algorithm that has been recently proposed for the task of continuous problems. This paper proposes a modified BRO (M-BRO) in order to improve balance between exploration and exploitation. For this matter, an additional movement operator has been used in the movement strategy. Moreover, no extra parameters are re . . .quired for the proposed approach. Furthermore, the complexity of this modified algorithm is the same as the original one. Experiments are performed on a set of 19 (unimodal and multimodal) benchmark functions (CEC 2010). The proposed method has been compared with the original BRO alongside six well-known/recently proposed optimization algorithms. The results show that BRO with additional movement operator performs well to solve complex numerical optimization problems compared to the original BRO and other competitors
Personality detection is an old topic in psychology and automatic personality prediction (or perception) (APP) is the automated (computationally) forecasting of the personality on diferent types of human generated/exchanged contents (such as text, speech, image, and video). The principal objective of this study is to ofer a shallow (overall) review of natural language processing approaches on APP since 2010. With the advent of deep learning and following it transfer-learning and pre-trained model in NLP, APP research area has been a hot topic, so in this review, methods are categorized into three: pre-trained independent, pre-traine . . .d model based, and multimodal approaches. In addition, to achieve a comprehensive comparison, reported results are informed by datasets
Here we perform the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative or fractional deriva- tives. Our operators are defined by using a density function generated by the Richards curve, which is generalized logistic function. The approximations are pointwise and of the uniform norm. Th . . .e related Banach space valued feed-forward neural networks are with one hidden layer
İstanbulTopkapı Üniversitesi, akademisyen ve lisansüstü öğrencilerinin iç ve dış paydaşlarla birlikte ürettikleri bilimsel çalışmalarını, Akademik Açık Arşivi'nde dijital olarak yayınlayarak, ülke ve dünya genelinde bilim topluluğuna açık erişim sağlamaktadır.
Akademik Açık Arşivi'nde bulunan tüm kaynaklar, telif haklarına saygı gösterilerek ve açık erişim ilkeleri doğrultusunda yayınlanmaktadır.
İstanbul Topkapı Üniversitesi, bilimsel bilgiye erişimi kolaylaştırarak, araştırma sonuçlarını ve bilimsel yayınları geniş bir kitleye sunarak bilimsel gelişmelere katkıda bulunmayı amaçlamaktadır.
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