Journal of Cognitive Human-Computer Interaction

Cognitive human-computer interaction (HCI) requires systems that do not only estimate whether a user is under high cognitive load, but also detect moments when interaction demands become unstable, disruptive, or cognitively misaligned with the user’s current state. Existing cognitive-load models commonly treat workload estimation as a static classification task, which limits their usefulness for adaptive interfaces. This paper introduces an interaction-stability gated multimodal learning framework for detecting cognitive friction during HCI. The proposed model combines subject-normalized physiological and gaze features with a temporal stability gate that adjusts the contribution of electrocardiography (ECG), electrodermal activity (EDA), electroencephalography (EEG), and gaze streams according to local signal reliability and cognitive-state fluctuation. A Cognitive Friction Index is further proposed to identify transition periods where cognitive load rises sharply or remains unstable across modalities. The study is designed for reproducibility using the public CLARE dataset, which contains multimodal physiological and gaze recordings from participants performing Multi-Attribute Task Battery II (MATB-II) computer-based workload tasks. Baseline evidence from the CLARE benchmark shows that multimodal learning improves cognitive-load estimation, but leave-one-subject-out performance remains lower than random 10-fold validation, indicating a strong personalization challenge. The proposed framework addresses this gap by modeling temporal instability and subject-level calibration rather than only point-level workload labels. The paper contributes a reproducible Cognitive HCI model, a friction oriented interpretation layer, and an adaptive-interface decision mechanism.

Road accidents and emergency services delay are the main significant issues. To overcome these issues need to develop a system. Efficient handling of accidents through the immediate detection and provide timely aid are more crucial. Accident detection and emergency system depends on IoT (Internet of things) with minimum delay are gaining significant attention towards industry and academic literature. Several researches are investigated using IOT technology to detect accidents. In this work, we proposed an effective accident detection method by employing five sensors not only to detect accident but also to report type of accident such as collision, no accident, roll over or fall off. In addition to that, the status of the accident is communicated to the IBM Watson Cloud platform. The incoming data received in the node red platform is integrated with the Google Maps to show location and other information about the accident that can be accessed by the hospital through website and sending alert messages to victim acquaintances. In addition, two Machine Learning (ML) models based on K-Nearest Neighbor (KNN) model and the Naïve Bayes (NB) model are compared to find out the best accident detection model. It is noticed that the KNN model is the very effective ML model, which employed to know the accident status and to enhance the system by providing patient’s details, a kill switch and sending messages often until acknowledgement is received.

Intracranial hemorrhage (ICH) poses a large chance to affected person fitness, regularly modern requiring set off diagnosis and intervention. In latest years, the medical imaging techniques, specifically computed tomography (CT) scanning, have end up critical tools for detecting and characterizing ICH. This paper offers a complete evaluate comprehensive review of the state-of-the-art techniques for the segmentation, category, and visualization cutting-edge intracranial hemorrhage in CT mind pics. The evaluate encompasses numerous methodologies, consisting of conventional picture processing strategies, system cutting-edge algorithms, and deep brand new strategies, highlighting their strengths, limitations, and capability applications in scientific exercise. Additionally, it discusses the challenges associated with correct ICH detection and quantification, inclusive of the presence modern day artifacts, anatomical variations, and sophistication imbalance. Furthermore, the paper explores emerging tendencies in ICH research, which includes the combination trendy multimodal imaging information and the improvement trendy interactive visualization gear for enhanced medical choice-making. The segmented portion from each CT image is constructed into a single 3D volumetric structure and essential information such as region Area, volume and location are provided. Further the classification accuracy between normal brain and ICH brain is 95.8%. Such a 3D visualization, Classification and volumetric analysis of ICH can provide the exact and necessary information to the neurologist which is essential for the treatment of ICH.

Remote Sensing Scene Classification (RSSC) is the distinctive classification of remote sensing images into numerous classes of scene classifications based on the image content. RSSC plays a significant role in several domains, like land mapping, agriculture, and the classification of disaster-prone regions. The Internet of Things (IoT) is a dynamic global network of devices, for example, vehicles, sensors, actuators, surveillance cameras, etc. These interconnected objects were distinctively recognizable and they could separately transfer and obtain valuable data through the network. However, satellite images were frequently degraded and blurred owing to aerosol dispersion under haze, fog, and other weather circumstances, decreasing the color fidelity and contrast of the image. To use effectual RSSC in real-time, widespread researchers concentrate on creating aerospace image processing systems, like airborne or spaceborne systems. Recently, with the quick improvement of deep learning (DL) and Machine learning (ML) techniques, the performance of RSSC has significantly developed owing to the hierarchical feature representation learning. Both technique has greater achievement in the domain of image scene classification. This study presents a Leveraging Tiny Convolutional Neural Networks with a Water Cycle Algorithm for Remote Sensing Scene Classification (LTCNN-WCRSSC) model. The LTCNN-WCRSSC technique is designed for efficient RSS classification in resource-constrained devices with on-board training capabilities. At first, the LTCNN-WCRSSC model applies image processing using a median filter (MF) to eliminate the noise. Next, the feature extraction process can be exploited by the ConvNeXt-Tiny method. For the RSSC model, the spatiotemporal attention bidirectional long short-term memory (STA-BiLSTM) technique is performed. Eventually, the water cycle algorithm (WCA)-based hyperparameter choice process can be performed to optimize the classification results of the STA-BiLSTM algorithm. The experimental evaluation of the LTCNN-WCRSSC technique takes place using a benchmark image dataset. The stimulated results indicated the superior performances of the LTCNN-WCRSSC model over other approaches.