Multi-modal remote perception learning for object sensory data.

Introduction: When it comes to interpreting visual input, intelligent systems make use of contextual scene learning, which significantly improves both resilience and context awareness. The management of enormous amounts of data is a driving force behind the growing interest in computational frameworks, particularly in the context of autonomous cars.

Method: The purpose of this study is to introduce a novel approach known as Deep Fused Networks (DFN), which improves contextual scene comprehension by merging multi-object detection and semantic analysis.

Gamify4LexAmb: a gamification-based approach to address lexical ambiguity in natural language requirements.

Ambiguity is a common challenge in specifying natural language (NL) requirements. One of the reasons for the occurrence of ambiguity in software requirements is the lack of user involvement in requirements elicitation and inspection phases. Even if they get involved, it is hard for them to understand the context of the system, and ultimately unable to provide requirements correctly due to a lack of interest.

Vehicle recognition pipeline via DeepSort on aerial image datasets.

Introduction: Unmanned aerial vehicles (UAVs) are widely used in various computer vision applications, especially in intelligent traffic monitoring, as they are agile and simplify operations while boosting efficiency. However, automating these procedures is still a significant challenge due to the difficulty of extracting foreground (vehicle) information from complex traffic scenes.

Methods: This paper presents a unique method for autonomous vehicle surveillance that uses FCM to segment aerial images.

A robust multimodal detection system: physical exercise monitoring in long-term care environments.

Introduction: Falls are a major cause of accidents that can lead to serious injuries, especially among geriatric populations worldwide. Ensuring constant supervision in hospitals or smart environments while maintaining comfort and privacy is practically impossible. Therefore, fall detection has become a significant area of research, particularly with the use of multimodal sensors.

Biosensor-Driven IoT Wearables for Accurate Body Motion Tracking and Localization.

The domain of human locomotion identification through smartphone sensors is witnessing rapid expansion within the realm of research. This domain boasts significant potential across various sectors, including healthcare, sports, security systems, home automation, and real-time location tracking. Despite the considerable volume of existing research, the greater portion of it has primarily concentrated on locomotion activities.

BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification.

A significant issue in computer-aided diagnosis (CAD) for medical applications is brain tumor classification. Radiologists could reliably detect tumors using machine learning algorithms without extensive surgery. However, a few important challenges arise, such as (i) the selection of the most important deep learning architecture for classification (ii) an expert in the field who can assess the output of deep learning models.

IoMT based smart healthcare system to control outbreaks of the COVID-19 pandemic.

The COVID-19 pandemic caused millions of infections and deaths globally requiring effective solutions to fight the pandemic. The Internet of Things (IoT) provides data transmission without human intervention and thus mitigates infection chances. A road map is discussed in this study regarding the role of IoT applications to combat COVID-19.

Visualizing software refactoring using radar charts.

Refactoring tools have advanced greatly and are being used in many large projects. As a result, a great deal of information is now available about past refactoring and its effects on the source code. However, when multiple refactoring is performed at once, it becomes more difficult to analyze their impact.

Intelligent Localization and Deep Human Activity Recognition through IoT Devices.

Ubiquitous computing has been a green research area that has managed to attract and sustain the attention of researchers for some time now. As ubiquitous computing applications, human activity recognition and localization have also been popularly worked on. These applications are used in healthcare monitoring, behavior analysis, personal safety, and entertainment.

Brain-Computer Interface: The HOL-SSA Decomposition and Two-Phase Classification on the HGD EEG Data.

An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain-Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information.

Precision in Dermatology: Developing an Optimal Feature Selection Framework for Skin Lesion Classification.

Melanoma is widely recognized as one of the most lethal forms of skin cancer, with its incidence showing an upward trend in recent years. Nonetheless, the timely detection of this malignancy substantially enhances the likelihood of patients' long-term survival. Several computer-based methods have recently been proposed, in the pursuit of diagnosing skin lesions at their early stages.

Gaussian-Filtered High-Frequency-Feature Trained Optimized BiLSTM Network for Spoofed-Speech Classification.

Voice-controlled devices are in demand due to their hands-free controls. However, using voice-controlled devices in sensitive scenarios like smartphone applications and financial transactions requires protection against fraudulent attacks referred to as "speech spoofing". The algorithms used in spoof attacks are practically unknown; hence, further analysis and development of spoof-detection models for improving spoof classification are required.

BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images.

The early detection of breast cancer using mammogram images is critical for lowering women's mortality rates and allowing for proper treatment. Deep learning techniques are commonly used for feature extraction and have demonstrated significant performance in the literature. However, these features do not perform well in several cases due to redundant and irrelevant information.

BCNetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection.

One of the most frequent cancers in women is breast cancer, and in the year 2022, approximately 287,850 new cases have been diagnosed. From them, 43,250 women died from this cancer. An early diagnosis of this cancer can help to overcome the mortality rate.

Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks.

Diabetic retinopathy (DR) and diabetic macular edema (DME) are forms of eye illness caused by diabetes that affects the blood vessels in the eyes, with the ground occupied by lesions of varied extent determining the disease burden. This is among the most common cause of visual impairment in the working population. Various factors have been discovered to play an important role in a person's growth of this condition.