Sine cosine optimization algorithm combined with balloon effect for adaptive position control of a cart forced by an armature-controlled DC motor.

For a car that is propelled by an armature-controlled DC motor This study proposes an adjustable linear positioning control. In this paper, to optimize the parameters of the car's position controller the sine cosine optimization algorithm (SCA) is utilized, with support from the Balloon effect (BE), The BE is incorporated to enhance the responsiveness of the traditional sine cosine optimization algorithm when faced with external disturbances and variations in system parameters. In the proposed approach, the determined value of the open loop transfer function of the motor and the updated values of the controller gains serve as the basis for the modified sine cosine algorithm's objective function (OF). Under the influence of changes in motor parameters and step load disturbances, the system using the suggested controller is evaluated. Results from simulations and experiments show that the proposed adaptive controller, which implements the modified sine cosine algorithm, enhances the system's overall performance in the presence of load disturbances and parameter uncertainties.

Adaptive Aquila Optimizer with Explainable Artificial Intelligence-Enabled Cancer Diagnosis on Medical Imaging.

Explainable Artificial Intelligence (XAI) is a branch of AI that mainly focuses on developing systems that provide understandable and clear explanations for their decisions. In the context of cancer diagnoses on medical imaging, an XAI technology uses advanced image analysis methods like deep learning (DL) to make a diagnosis and analyze medical images, as well as provide a clear explanation for how it arrived at its diagnoses. This includes highlighting specific areas of the image that the system recognized as indicative of cancer while also providing data on the fundamental AI algorithm and decision-making process used. The objective of XAI is to provide patients and doctors with a better understanding of the system's decision-making process and to increase transparency and trust in the diagnosis method. Therefore, this study develops an Adaptive Aquila Optimizer with Explainable Artificial Intelligence Enabled Cancer Diagnosis (AAOXAI-CD) technique on Medical Imaging. The proposed AAOXAI-CD technique intends to accomplish the effectual colorectal and osteosarcoma cancer classification process. To achieve this, the AAOXAI-CD technique initially employs the Faster SqueezeNet model for feature vector generation. As well, the hyperparameter tuning of the Faster SqueezeNet model takes place with the use of the AAO algorithm. For cancer classification, the majority weighted voting ensemble model with three DL classifiers, namely recurrent neural network (RNN), gated recurrent unit (GRU), and bidirectional long short-term memory (BiLSTM). Furthermore, the AAOXAI-CD technique combines the XAI approach LIME for better understanding and explainability of the black-box method for accurate cancer detection. The simulation evaluation of the AAOXAI-CD methodology can be tested on medical cancer imaging databases, and the outcomes ensured the auspicious outcome of the AAOXAI-CD methodology than other current approaches.

Developing gamification e-quizzes based on an android app: the impact of asynchronous form.

Because of the heath measures taken during the outbreak of Covid-19, the lack of educational methods has become the primary concern among educational professionals who have been using technology as a motivational tool. Gamification is very important because it helps students to represent their study contents and enrich their experiences of higher education when learning in-person is unavailable during the Covid-19 period. This study seeks to present an Android-based gamification app to evaluate the effect of using gamification and e-quizzes on college students' learning. We used the visual blocks language from the MIT App Inventor platform to develop an application, available at (https://play.google.com/store/apps/details?id=appinventor.ai_mekomerofofo.projectGamification). The participants were students from level 2 who used digital lessons for learning MATLAB. The study included gamified learning and non-gamified learning, both integrated into lesson plans, to investigate the differences in learners' performance. Two types of quizzes were used for instruction: gamified e-quizzes and paper-based quizzes. The outcomes plainly showed that using the new gamified e-quiz was more effective than using paper-based quizzes. They are better for assessing the learning performance of the students in question, specifically in terms of formative assessment. It is very important for instructors to apply games as a modern and innovation-oriented tool through which students can be engaged in an attractive, competitive experience.

Multi-variant differential evolution algorithm for feature selection.

This work introduces a new population-based stochastic search technique, named multi-variant differential evolution (MVDE) algorithm for solving fifteen well-known real world problems from UCI repository and compared to four popular optimization methods. The MVDE proposes a new self-adaptive scaling factor based on cosine and logistic distributions as an almost factor-free optimization technique. For more updated chances, this factor is binary-mapped by incorporating an adaptive crossover operator. During the evolution, both greedy and less-greedy variants are managed by adjusting and incorporating the binary scaling factor and elite identification mechanism into a new multi-mutation crossover process through a number of sequentially evolutionary phases. Feature selection decreases the number of features by eliminating irrelevant or misleading, noisy and redundant data which can accelerate the process of classification. In this paper, a new feature selection algorithm based on the MVDE method and artificial neural network is presented which enabled MVDE to get a combination features' set, accelerate the accuracy of the classification, and optimize both the structure and weights of Artificial Neural Network (ANN) simultaneously. The experimental results show the encouraging behavior of the proposed algorithm in terms of the classification accuracies and optimal number of feature selection.