This paper introduces an unsupervised deep learning-driven scheme for mental tasks’ recognition using EEG signals. To this end, the Multichannel Wiener filter was first applied to EEG signals as an artifact removal algorithm to achieve robust recognition. Then, a quadratic time-frequency distribution (QTFD) was applied to extract effective time-frequency signal representation of the EEG signals and catch the EEG signals’ spectral variations over time to improve the recognition of mental tasks. The QTFD time-frequency features are employed as input for the proposed deep belief network (DBN)-driven Isolation Forest (iF) scheme to classify the EEG signals. Indeed, a single DBN-based iF detector is constructed based on each class’s training data, with the class’s samples as inliers and all other samples as anomalies (i.e., one-vs.-rest). The DBN is considered to learn pertinent information without assumptions on the data distribution, and the iF scheme is used for data discrimination. This approach is assessed using experimental data comprising five mental tasks from a publicly available database from the Graz University of Technology. Compared to the DBN-based Elliptical Envelope, Local Outlier Factor, and state-of-the-art EEG-based classification methods, the proposed DBN-based iF detector offers superior discrimination performance of mental tasks.

The World Health Organization estimates that there were around 10 million deaths due to cancer in 2020, and lung cancer was the most common type of cancer, with over 2.2 million new cases and 1.8 million deaths. While there have been advances in the diagnosis and prediction of lung cancer, there is still a need for new, intelligent methods or diagnostic tools to help medical professionals detect the disease. Since it is currently unable to detect at an early stage, speedy detection and identification are crucial because they can increase a patient’s chances of survival. This article focuses on developing a new tool for diagnosing lung tumors and providing thermal touch feedback using virtual reality visualization and thermal technology. This tool is intended to help identify and locate tumors and measure the size and temperature of the tumor surface. The tool uses data from CT scans to create a virtual reality visualization of the lung tissue and includes a thermal display incorporated into a haptic device. The tool is also tested by touching virtual tumors in a virtual reality application. On the other hand, thermal feedback could be used as a sensory substitute or adjunct for visual or tactile feedback. The experimental results are evaluated with the performance comparison of different algorithms and demonstrate that the proposed thermal model is effective. The results also show that the tool can estimate the characteristics of tumors accurately and that it has the potential to be used in a virtual reality application to “touch” virtual tumors. In other words, the results support the use of the tool for diagnosing lung tumors and providing thermal touch feedback using virtual reality visualization, force, and thermal technology.

Document image binarization is a fundamental yet challenging task in document analysis, particularly for historical documents affected by complex degradations such as uneven illumination, bleed-through, stains, and ink fading. While substantial progress has been made, algorithms performance strongly depends on document characteristics and acquisition conditions. To support objective comparison, benchmarking initiatives such as the Document Image Binarization Competition (DIBCO) provide standardized datasets and pixel-level evaluation protocols. While existing studies largely focus on binarization accuracy, practical deployment additionally requires careful consideration of computational efficiency, as high-quality methods may incur prohibitive processing costs. This paper presents a critical review of document image binarization evaluation methodologies, with a particular focus on historical documents. We analyze the evolution of algorithms evaluated within the DIBCO frameworks, examine the strengths and limitations of commonly used performance metrics, and discuss the impact of blind evaluation protocols on algorithm ranking. Furthermore, we highlight the emerging importance of time-quality trade-offs and efficiency-aware evaluation criteria. The survey highlights the absence of a universally optimal binarization method and points to the need for adaptive, degradation-aware strategies and more comprehensive evaluation practices in future research.