The need to have equitable access quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines developmental agenda of UN for next 15 years. In particular, third SDG focuses on “ensure healthy lives and promote well-being all at ages”. this paper, we build case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence machine learning), will transform global systems near future....

The mHealth trend, which uses mobile devices and associated technology for health interventions, offers unprecedented opportunity to transform the services available people across globe. In particular, transformation can be most disruptive in developing countries, is often characterized by a dysfunctional public system. Despite this opportunity, growth of countries rather slow no existing studies have conducted an in-depth search identify reasons. We present comprehensive report about...

Intrusion detection systems (IDSs) are an essential cog of the network security suite that can defend from malicious intrusions and anomalous traffic. Many machine learning (ML)-based IDSs have been proposed in literature for However, recent works shown ML models vulnerable to adversarial perturbations through which adversary cause malfunction by introducing a small impracticable perturbation In this paper, we propose attack using generative networks (GANs) successfully evade ML-based IDS....

Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities widespread ground-glass opacities. This makes automatic recognition imaging challenging task. To overcome this issue, we propose densely attention mechanism-based network (DAM-Net) CXR. DAM-Net adaptively extracts spatial from infected regions...

Deep learning-based cardiac auscultation is of significant interest to the healthcare community as it can help reducing burden manual with automated detection abnormal heartbeats. However, problem automatic complicated due requirement reliable and highly accurate systems, which are robust background noise in heartbeat sound. In this paper, we propose a Recurrent Neural Networks (RNNs)-based solution. Our choice RNNs motivated by their great success modeling sequential or temporal data even...

The majority of existing speech emotion recognition research focuses on automatic detection using training and testing data from same corpus collected under the conditions.The performance such systems has been shown to drop significantly in cross-corpus cross-language scenarios.To address problem, this paper exploits a transfer learning technique improve that is novel scenarios.Evaluations five different corpora three languages show Deep Belief Networks (DBNs) offer better accuracy than...

Inspite the emerging importance of Speech Emotion Recognition (SER), state-of-the-art accuracy is quite low and needs improvement to make commercial applications SER viable. A key underlying reason for scarcity emotion datasets, which a challenge developing any robust machine learning model in general. In this article, we propose solution problem: multi-task framework that uses auxiliary tasks data abundantly available. We show utilisation additional can improve primary task only limited...

Speech emotion recognition is a challenging task and heavily depends on hand-engineered acoustic features, which are typically cra ed to echo human perception of speech signals.However, lter bank that designed from perceptual evidence not always guaranteed be the best in statistical modelling framework where end goal for example classi cation.is has fuelled emerging trend learning representations raw especially using deep neural networks.In particular, combination Convolution Neural Networks...

Cross-lingual speech emotion recognition is an important task for practical applications. The performance of automatic systems degrades in cross-corpus scenarios, particularly scenarios involving multiple languages or a previously unseen language such as Urdu which limited no data available. In this study, we investigate the problem cross-lingual and contribute URDU-the first ever spontaneous Urdu-language database. Evaluations are performed using three different Western against experimental...

Learning the latent representation of data in unsupervised fashion is a very interesting process that provides relevant features for enhancing performance classifier.For speech emotion recognition tasks, generating effective crucial.Currently, handcrafted are mostly used recognition, however, learned automatically using deep learning have shown strong success many problems, especially image processing.In particular, generative models such as Variational Autoencoders (VAEs) gained enormous...

Abstract Deep reinforcement learning (DRL) is poised to revolutionise the field of artificial intelligence (AI) by endowing autonomous systems with high levels understanding real world. Currently, deep (DL) enabling DRL effectively solve various intractable problems in fields including computer vision, natural language processing, healthcare, robotics, name a few. Most importantly, algorithms are also being employed audio signal processing learn directly from speech, music and other sound...

Traditionally, speech emotion recognition (SER) research has relied on manually handcrafted acoustic features using feature engineering. However, the design of for complex SER tasks requires significant manual effort, which impedes generalisability and slows pace innovation. This motivated adoption representation learning techniques that can automatically learn an intermediate input signal without any Representation led to improved performance enabled rapid Its effectiveness further...

Diabetic retinopathy (DR) is a diabetes complication that can cause vision loss among patients due to damage blood vessels in the retina. Early retinal screening avoid severe consequences of DR and enable timely treatment. Nowadays, researchers are trying develop automated deep learning-based segmentation tools using fundus images help Ophthalmologists with early diagnosis. However, recent studies unable design accurate models unavailability larger training data consistent fine-grained...

Abstract Multishot Magnetic Resonance Imaging (MRI) is a promising data acquisition technique that can produce high-resolution image with relatively less time than the standard spin echo. The downside of multishot MRI it very sensitive to subject motion and even small levels during scan artifacts in final magnetic resonance (MR) image, which may result misdiagnosis. Numerous efforts have focused on addressing this issue; however, all these proposals are limited terms how much they correct...

Research on speech processing has traditionally considered the task of designing hand-engineered acoustic features (feature engineering) as a separate distinct problem from efficient machine learning (ML) models to make prediction and classification decisions. There are two main drawbacks this approach: firstly, feature engineering being manual is cumbersome requires human knowledge; secondly, designed might not be best for objective at hand. This motivated adoption recent trend in community...

Despite the recent advancement in speech emotion recognition (SER) within a single corpus setting, performance of these SER systems degrades significantly for cross-corpus and cross-language scenarios. The key reason is lack generalisation towards unseen conditions, which causes them to perform poorly settings. Recent studies focus on utilising adversarial methods learn domain generalised representation improving address this issue. However, many only without addressing degradation due...

The metaverse is currently undergoing a profound transformation, fundamentally reshaping our perception of reality. It has transcended its origins to become an expansion human consciousness, seamlessly blending the physical and virtual worlds. Amidst this transformative evolution, numerous applications are striving mould into digital counterpart capable delivering immersive human-like experiences. These envisage future where users effortlessly traverse between dimensions. Taking step...

Cross-lingual speech emotion recognition (SER)is a crucial task for many real-world applications. The performance of SER systems is often degraded by the differences in distributions training and test data. These become more apparent when data belong to different languages, which cause significant gap between validation scores. It imperative build robust models that can fit practical applications systems. Therefore, this paper, we propose Generative Adversarial Network (GAN)-based model...

Distress is a complex condition, which affects significant percentage of cancer patients and may lead to depression, anxiety, sadness, suicide other forms psychological morbidity. Compelling evidence supports screening for distress as means facilitating early intervention subsequent improvements in well-being overall quality life. Nevertheless, despite the existence evidence-based easily administered tools, example, Thermometer, routine yet achieve widespread implementation. Efforts are...