In recent years, mental health issues have profoundly impacted individuals’ well-being, necessitating prompt identification and intervention. Existing approaches grapple with the complex nature of mental health, faci...
详细信息
In recent years, mental health issues have profoundly impacted individuals’ well-being, necessitating prompt identification and intervention. Existing approaches grapple with the complex nature of mental health, facing challenges like task interference, limited adaptability, and difficulty in capturing nuanced linguistic expressions indicative of various conditions. In response to these challenges, our research presents three novel models employing multi-task learning (MTL) to understand mental health behaviors comprehensively. These models encompass soft-parameter sharing-based long short-term memory with attention mechanism (SPS-LSTM-AM), SPS-based bidirectional gated neural networks with self-head attention mechanism (SPS-BiGRU-SAM), and SPS-based bidirectional neural network with multi-head attention mechanism (SPS-BNN-MHAM). Our models address diverse tasks, including detecting disorders such as bipolar disorder, insomnia, obsessive-compulsive disorder, and panic in psychiatric texts, alongside classifying suicide or non-suicide-related texts on social media as auxiliary tasks. Emotion detection in suicide notes, covering emotions of abuse, blame, and sorrow, serves as the main task. We observe significant performance enhancement in the primary task by incorporating auxiliary tasks. Advanced encoder-building techniques, including auto-regressive-based permutation and enhanced permutation language modeling, are recommended for effectively capturing mental health contexts’ subtleties, semantic nuances, and syntactic structures. We present the shared feature extractor called shared auto-regressive for language modeling (S-ARLM) to capture high-level representations that are useful across tasks. Additionally, we recommend soft-parameter sharing (SPS) subtypes-fully sharing, partial sharing, and independent layer-to minimize tight coupling and enhance adaptability. Our models exhibit outstanding performance across various datasets, achieving accuracies of 96.9%, 97.
Emerging technologies of Agriculture 4.0 such as the Internet of Things (IoT), Cloud Computing, Artificial Intelligence (AI), and 5G network services are being rapidly deployed to address smart farming implementation-...
详细信息
Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acq...
详细信息
Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acquisition and transmission phases,noise is introduced into the acquired image,which can have a negative impact on downstream analyses such as classification,target tracking,and spectral *** in hyperspectral images(HSI)is modelled as a combination from several sources,including Gaussian/impulse noise,stripes,and *** HSI restoration method for such a mixed noise model is ***,a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data as well as the sparse/impulse noise ***,a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image ***,to model the sparse nature of impulse noise,anℓ_(1)−norm over the impulse noise gradient is *** the proposed methodology employs two distinct priors,the authors refer to it as the hyperspectral dual prior(HySpDualP)*** the best of authors'knowledge,this joint optimisation framework is the first attempt in this *** handle the non-smooth and nonconvex nature of the generalℓ_(p)−norm-based regularisation term,a generalised shrinkage/thresholding(GST)solver is ***,an efficient split-Bregman approach is used to solve the resulting optimisation *** results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’proposed model outperforms state-of-the-art methods,both visually and in terms of various image quality assessment metrics.
The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprec...
详细信息
The Quantum Internet of Things (QIoT) in the healthcare industry holds the promise of transforming patient care, diagnostics, and medical research. Quantum-enhanced sensors, communication, and computation offer unprecedented capabilities that can revolutionize how healthcare services are delivered and experienced. This paper explores the potential of QIoT in the context of smart healthcare, where interconnected quantum-enabled devices and systems create an ecosystem that enhances data security, enables real-time monitoring, and advances medical knowledge. We delve into the applications of quantum sensors in precise health monitoring, the role of quantum communication in secure telemedicine, and the computational power of quantum computing in drug discovery and personalized medicine. We discuss challenges such as technical feasibility, scalability, and regulatory considerations, along with the emerging trends and opportunities in this transformative field. By examining the intersection of quantum technologies and smart healthcare, this paper aims to shed light on the novel approaches and breakthroughs that could redefine the future of healthcare delivery and patient outcomes. IEEE
This study proposes a malicious code detection model DTL-MD based on deep transfer learning, which aims to improve the detection accuracy of existing methods in complex malicious code and data scarcity. In the feature...
详细信息
In today’s era, smartphones are used in daily lives because they are ubiquitous and can be customized by installing third-party apps. As a result, the menaces because of these apps, which are potentially risky for u...
详细信息
The effects of changing learning rates, data augmentation percentage and numbers of epochs on the performance of Wasserstein Generative Adversarial Networks with Gradient Penalties (WGAN-GP) are evaluated in this stud...
详细信息
Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs) is not only constitute an encouraging research domain but also represent a promising industrial trend that permits the development of various IoT-based ...
详细信息
Data collection using mobile sink(s) has proven to reduce energy consumption and enhance the network lifetime of wireless sensor networks. Generally speaking, a mobile sink (MS) traverses the network region, sojournin...
详细信息
Internet of Things (IoT) devices are often directly authenticated by the gateways within the network. In complex and large systems, IoT devices may be connected to the gateway through another device in the network. In...
详细信息
暂无评论