Brain-computer interface (BCI) has become extremely popular in recent decades. It gained its significance from the intention of helping paralyzed people communicate with the external environment. One of the major challenges facing BCI systems is obtaining reliable classification accuracy of motor imagery (MI) mental tasks. In this paper, a novel CSP\AM-BA-SVM approach is proposed using bio-inspired algorithms for feature selection and classifier optimization to improve classification accuracy of the MI-BCI systems. The proposed approach applies optimum selection of time interval for each subject. The features are extracted from EEG signal using the common spatial pattern (CSP). Binary CSP is extended to multi-class problems by utilizing one-vs-one strategy. This paper introduces applying a hybrid attractor metagene (AM) algorithm along with the Bat optimization algorithm (BA) to select the most discriminant CSP features and optimize SVM parameters. The efficacy of the proposed approach was examined using three data sets. The proposed approach has achieved 78.55% accuracy and 0.71 mean kappa for BCI Competition IV data set 2a, 86.6% accuracy and 0.82 mean kappa for BCI Competition III data set IIIa, and 85% for the binary class BCI Competition III data set IVa. For multi-class data sets, the proposed approach outperforms winners of BCIC IV, 2a and BCIC III, IIIa with kappa 0.14 and 0.17, respectively. For binary class BCIC III, IVa, it performed slightly better than existing studies in the literature by ≈ 0.5%. The proposed CSP\AM-BA-SVM transcends the traditional CSP\SVM approach and other existing studies.

Alzheimer’s disease (AD) is considered the 6 ^th leading cause of death worldwide. Early diagnosis of AD is not an easy task, and no preventive cures have been discovered yet. Having an accurate computer-aided system for the early detection of AD is important to help patients with AD. This study proposes a new approach for classifying disease stages. First, we worked on the MRI images and split them into an appropriate format to avoid data leakage. Subsequently, a simple and fast registration-free preprocessing pipeline was applied to the dataset. Numerous experiments were conducted to analyze the performances of different 3D classification architectures. Finally, an ensemble learning approach is applied to the top-performing models. The outstanding performance of the proposed method was demonstrated using augmentation of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Our proposed ensemble approach outperforms studies in literature for distinguishing between people with AD and mild cognitive impairment (MCI), and MCI and cognitive normal (CN) with an AUC score of 91.28% and 88.42%, respectively. We also targeted the multiclass task, which was marginalized in previous work, by differentiating between the three stages of the disease.

Code conversion, encompassing translation, optimization, and generation, is becoming increasingly critical in information systems and the software industry. Traditional validation methods, such as test cases and code coverage metrics, often fail to ensure the correctness, completeness, and equivalence of converted code to its original form. Formal verification emerges as a crucial methodology to address these limitations. Although numerous surveys have explored formal verification in various contexts, a significant research gap exists in pinpointing appropriate formal verification approaches to code conversion tasks. This paper provides a detailed survey of formal verification techniques applicable to code conversion. This survey identifies the strengths and limitations of contemporary adopted approaches while outlining a trajectory for future research, emphasizing the need for automated and scalable verification tools. The novel categorization of formal verification methods provided in this paper serves as a foundational guide for researchers seeking to enhance the reliability of code conversion processes.

Recent advancements in Brain-Computer Interface (BCI) technology are shifting towards inner speech over motor imagery due to its intuitive nature and broader command spectrum, enhancing interaction with electronic devices. However, the reliance on a large number of electrodes in available datasets complicates the development of cost-effective BCIs. Additionally, the lack of publicly available datasets hinder the development of this technology. To address this, we introduce a new Arabic Inner Speech dataset, featuring five distinct classes, exceeding the typical four-class datasets, and recorded using only eight electrodes, making it an economical solution. Our primary objective is to provide an open-access, multi-class Electroencephalographic (EEG) dataset in Arabic for inner speech, encompassing five commands. This dataset is designed to enhance our understanding of brain activity, facilitate the integration of BCI technologies in Arabic-speaking regions, and serve as a valuable resource for developing and testing real-world BCI applications. Through this contribution, we aim to bridge the gap between language-specific neural data and the field of neurotechnology, fostering innovation and inclusivity in BCI research.