This study introduces a comprehensive framework to enhance distance estimation models in indoor environments by incorporating Bluetooth Low Energy (BLE) Anchor information along with Received Signal Strength Indicator...
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Biomedical image processing acts as an essential part of severalmedical applications in supporting computer aided disease diagnosis. MagneticResonance Image (MRI) is a commonly utilized imaging tool used tosave glioma...
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Biomedical image processing acts as an essential part of severalmedical applications in supporting computer aided disease diagnosis. MagneticResonance Image (MRI) is a commonly utilized imaging tool used tosave glioma for clinical examination. Biomedical image segmentation plays avital role in healthcare decision making process which also helps to identifythe affected regions in the MRI. Though numerous segmentation models areavailable in the literature, it is still needed to develop effective segmentationmodels for BT. This study develops a salp swarm algorithm with multi-levelthresholding based brain tumor segmentation (SSAMLT-BTS) model. Thepresented SSAMLT-BTS model initially employs bilateral filtering based onnoise removal and skull stripping as a pre-processing phase. In addition,Otsu thresholding approach is applied to segment the biomedical imagesand the optimum threshold values are chosen by the use of SSA. Finally,active contour (AC) technique is used to identify the suspicious regions in themedical image. A comprehensive experimental analysis of the SSAMLT-BTSmodel is performed using benchmark dataset and the outcomes are inspectedin many aspects. The simulation outcomes reported the improved outcomesof the SSAMLT-BTS model over recent approaches with maximum accuracyof 95.95%.
Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-m...
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Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-making, has made automatic emotion recognition and examination of a significant feature in the field of psychiatric disease treatment and cure. The problem arises from the limited spatial resolution of EEG recorders. Predetermined quantities of electroencephalography (EEG) channels are used by existing algorithms, which combine several methods to extract significant data. The major intention of this study was to focus on enhancing the efficiency of recognizing emotions using signals from the brain through an experimental, adaptive selective channel selection approach that recognizes that brain function shows distinctive behaviors that vary from one individual to another individual and from one state of emotions to another. We apply a Bernoulli–Laplace-based Bayesian model to map each emotion from the scalp senses to brain sources to resolve this issue of emotion mapping. The standard low-resolution electromagnetic tomography (sLORETA) technique is employed to instantiate the source signals. We employed a progressive graph convolutional neural network (PG-CNN) to identify the sources of the suggested localization model and the emotional EEG as the main graph nodes. In this study, the proposed framework uses a PG-CNN adjacency matrix to express the connectivity between the EEG source signals and the matrix. Research on an EEG dataset of parents of an ASD (autism spectrum disorder) child has been utilized to investigate the ways of parenting of the child's mother and father. We engage with identifying the personality of parental behaviors when regulating the child and supervising his or her daily activities. These recorded datasets incorporated by the proposed method identify five emotions from brain source modeling, which significantly improves the accurac
In the past few years, new ways have been found to use IoT technology to make smart cities safer for everyone. These include real-time monitoring, data analytics, AI integration, and advanced IoT devices for emergency...
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Object detection (OD) in Advanced Driver Assistant systems (ADAS) has been a fundamental problem especially when complex unseen cross-domain adaptations occur in real driving scenarios of autonomous Vehicles (AVs). Du...
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Object detection (OD) in Advanced Driver Assistant systems (ADAS) has been a fundamental problem especially when complex unseen cross-domain adaptations occur in real driving scenarios of autonomous Vehicles (AVs). During the sensory perception of autonomous Vehicles (AV) in the driving environment, the Deep Neural Networks (DNNs) trained on the existing large datasets fail to detect the vehicular instances in the real-world driving scenes having sophisticated dynamics. Recent advances in Generative Adversarial Networks (GAN) have been effective in generating different domain adaptations under various operational conditions of AVs, however, it lacks key-object preservation during the image-to-image translation process. Moreover, high translation discrepancy has been observed with many existing GAN frameworks when encountered with large and complex domain shifts such as night, rain, fog, etc. resulting in an increased number of false positives during vehicle detection. Motivated by the above challenges, we propose COPGAN, a cycle-object preserving cross-domain GAN framework that generates diverse variations of cross-domain mappings by translating the driving conditions of AV to a desired target domain while preserving the key objects. We fine-tune the COPGAN training with an initial step of key-feature selection so that we realize the instance-aware image translation model. It introduces a cycle-consistency loss to produce instance specific translated images in various domains. As compared to the baseline models that needed a pixel-level identification for preserving the object features, COPGAN requires instance-level annotations that are easier to acquire. We test the robustness of the object detectors SSD, Detectron, and YOLOv5 (SDY) against the synthetically-generated COPGAN images, along with AdaIN images, stylized renderings, and augmented images. The robustness of COPGAN is measured in mean performance degradation for the distorted test set (at IoU threshold =
Android applications are evolving quickly, and both consumers and developers are growing in number. This popularity increases Android system vulnerabilities and attacks. Researchers have proposed various approaches to...
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The recent Internet of Things (IoT) adoption has revolutionized various applications while introducing significant security and privacy challenges. Traditional security solutions are unsuitable for IoT systems due to ...
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Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a crucial role in enhancing emergency respo...
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Malware propagation by adversaries has witnessed many issues across the globe. Often it is found that malware is released in different countries for monetary gains. With the proliferation of malware spreading activiti...
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The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the ...
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The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of *** resolve this problem,efficient and flexible malware detection tools are *** work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data ***,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:***,***,***,***,*** network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained *** addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional *** success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.
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