Prayag Tiwari

Halmstad University · ITE- School of Information Technology

Knowledge graph reasoning aims to find reasoning paths for relations over incomplete knowledge graphs (KG). Prior works may not take into account that the rewards for each position (vertex in the graph) may be different. We propose the distance-aware reward in the reinforcement learning framework to assign different rewards for different positions....

With the acceleration of the pace of work and life, people are facing more and more pressure, which increases the probability of suffering from depression. However, many patients may fail to get a timely diagnosis due to the serious imbalance in the doctor-patient ratio in the world. A promising development is that physiological and and psychologic...

In this paper, by mapping datasets to a set of non-linear coherent states, the process of encoding inputs in quantum states as a non-linear feature map is re-interpreted. As a result of the fact that the Radial Basis Function is recovered when data is mapped to a complex Hilbert state represented by coherent states, non-linear coherent states can b...

Traffic flow forecasting is a challenging task due to its spatio-temporal nature and the stochastic features underlying complex traffic situations. Currently, Graph Convolutional Network (GCN) methods are among the most successful and promising approaches. However, most GCNs methods rely on a static graph structure, which is generally unable to ext...

The widespread use of mobile devices for all kind of transactions makes necessary reliable and real-time identity authentication, leading to the adoption of face recognition (FR) via the cameras embedded in such devices. Progress of deep Convolutional Neural Networks (CNNs) has provided substantial advances in FR. Nonetheless, the size of state-of-...

Exploring the missing values is an essential but challenging issue due to the complex latent spatio-temporal correlation and dynamic nature of time series. Owing to the outstanding performance in dealing with structure learning potentials, Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs) are often used to capture such complex spati...

This survey paper proposes a clearer view of natural language reasoning in the field of Natural Language Processing (NLP), both conceptually and practically. Conceptually, we provide a distinct definition for natural language reasoning in NLP, based on both philosophy and NLP scenarios, discuss what types of tasks require reasoning, and introduce a...

Pseudouridine is an RNA modification that is widely distributed in both prokaryotes and eukaryotes, and plays a critical role in numerous biological activities. Despite its importance, the precise identification of pseudouridine sites through experimental approaches poses significant challenges, requiring substantial time and resources.Therefore, t...

Deep Non-Negative Matrix Factorization (DNMF) methods provide an efficient low-dimensional representation of given data through their layered architecture. A limitation of such methods is that they cannot effectively preserve the local and global geometric structures of the data in each layer. Consequently, a significant amount of the geometrical i...

Drug-drug interaction (DDI) is an important part of drug development and pharmacovigilance. At the same time, DDI is an important factor in treatment plan, effect of medicine and patient safety, and has a significant impact on public health. Therefore, using deep learning technology to extract DDI from scientific literature has become a valuable re...

The technology of speech emotion recognition (SER) has been widely applied in the field of human-computer interaction within the Internet of Vehicles (IoV). The incorporation of emerging technologies such as artificial intelligence and big data has accelerated the advancement of SER technology. However, this reveals challenges such as limited compu...

The Internet of Medical Things (IoMT) is the new digital healthcare application paradigm that offers many healthcare services to users. IoMT-based emerging healthcare applications such as cyborgs, the combination of advanced artificial intelligence (AI) robots, and doctors performing surgical operations remotely from hospitals to patients in their...

An orthogonal representation of features can offer valuable insights into feature selection as it aims to find a representative subset of features in which all features can be accurately reconstructed by a set of features that are linearly independent, uncorrelated, and perpendicular to each other. In this paper, a novel feature selection method, c...

The association between drugs and side effects encompasses information about approved medications and their documented adverse drug reactions. Traditional experimental approaches for studying this association tend to be time-consuming and expensive. To represent all drug-side effect associations, a bipartite network framework is employed. Consequen...

Given the overwhelming and rapidly increasing volumes of the published biomedical literature, automatic biomedical text summarization has long been a highly important task. Recently, great advances in the performance of biomedical text summarization have been facilitated by pre-trained language models (PLMs) based on fine-tuning. However, existing...

Smart healthcare aims to revolutionize med-ical services by integrating artificial intelligence (AI). The limitations of classical machine learning include privacy concerns that prevent direct data sharing among medical institutions, untimely updates, and long training times. To address these issues, this study proposes a digital twin-assisted quan...

Pre-trained language models (PLMs) have been the de facto paradigm for most natural language processing (NLP) tasks. This also benefits the biomedical domain: researchers from informatics, medicine, and computer science (CS) communities propose various PLMs trained on biomedical datasets, e.g., biomedical text, electronic health records, protein, a...

This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting. LIME was initially proposed for networks with the same output classes used for training, and it employs the softmax probability to determine which regions of the image contribute the most to c...

A growing number of individuals are expressing their opinions and engaging in interactive communication with others through various modalities, including natural language (text), facial gestures (vision), acoustic behaviors (audio), and more. Within the realms of natural language processing (NLP) and artificial intelligence (AI), multi-modal sentim...

Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things (IoMT). The advent of the quantum era makes it difficult for classical blockchain technology to provide security for ECG data storage. Therefore, f...

Subspace distance is an invaluable tool exploited in a wide range of feature selection methods. The power of subspace distance is that it can identify a representative subspace, including a group of features that can efficiently approximate the space of original features. On the other hand, employing intrinsic statistical information of data can pl...

In this study, a sliding mode observer (SMO) is implemented on a T–S fuzzy system with multiple time–varying delays over continuous time. Because state data may not be fully available in practice, state observers are used to estimate state information. A system based on observers is implemented with non–parallel distribution compensation (N-PDC). M...

Session-based recommendation is misleading by popularity bias and always favors short-head items with more popularity. This paper studies a new causal-based framework CaTailReS to increase the diversity of session recommendations. We first propose a new causal graph and then use the do-calculus in order to understand how popularity influences the p...

Graph neural networks (GNNs), especially dynamic GNNs, have become a research hotspot in spatio-temporal forecasting problems. While many dynamic graph construction methods have been developed, relatively few of them explore the causal relationship between neighbour nodes. Thus, the resulting models lack strong explainability for the causal relatio...

Recently, the rapid development of Unmanned Aerial Vehicles (UAVs) enables ecological conservation, such as low-carbon and “green” transport, which helps environmental sustainability. In order to address control issues in a given region, UAV charging infrastructure is urgently needed. To better achieve this task, an investigation into the T–S fuzzy...

N4-methylcytosine (4mC) is a common DNA methylation that has been implicated in epigenetic regulation and host defense. Accurate prediction of 4mC sites in DNA sequences will help to better explore the biological processes and mechanisms. For this problem, computational methods based on machine learning (ML) and deep learning (DL) are faster, less...

An adverse drug event (ADE) is defined as an adverse reaction resulting from improper drug use, reported in various documents such as biomedical literature, drug reviews, and user posts on social media. The recent advances in natural language processing techniques have facilitated automated ADE detection from documents. However, the contextualized...

Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote service...

Breast cancer is one of the most common cancer types among women, and it is a deadly disease caused by the uncontrolled proliferation of cells. Pathologists face a challenging issue of mitotic cell identification and counting during manual detection and identification of cancer. Artificial intelligence can help the medical professional with early,...

Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices. On the other hand, IoT-based heartbeat digital...

Continuously analyzing medical time series as new classes emerge is meaningful for health monitoring and medical decision-making. Few-shot class-incremental learning (FSCIL) explores the classification of few-shot new classes without forgetting old classes. However, little of the existing research on FSCIL focuses on medical time series classificat...

Detection of therapeutic peptide is a major research direction in the current biopharmaceutical field. However, traditional biochemical experimental detection methods take a lot of time. As supplementary methods for biochemical experiments, the computational methods can improve the efficiency of therapeutic peptide detection. Currently, most machin...

Security and privacy are issues that cannot be ignored when collecting and processing medical data in the Internet of Medical Things (IoMT). Blockchain technology is a decentralized ledger system that has diverse application scenarios in the medical field. Blockchain technology relies on traditional cryptography to ensure data integrity and verifia...

Sarcasm is a form of figurative language device to express human inner feelings, where the author writes the positive sentence on surface form, while he/she actually expresses negative sentiment, vice versa. Sentiment thus comes into sight, and is closely related with sarcasm, leading to the recent popularity of multi-modal sarcasm and sentiment jo...

The increasing popularity of 6G communication within the Internet of Vehicles (IoV) ecosystem is expected to induce a surge in both user numbers and data volumes. This expansion will cause substantial challenges in ensuring network security and privacy protection, as well as in addressing the associated issue of inadequate cloud computing resources...

Intelligent traffic incident detection provides benefits such as minimizing traffic accidents and fuel consumption, reducing congestion, and enhancing transportation safety. Hence, traffic incident detection has been an active research area in customer-centric intelligent transportation systems (ITS). Given that a driver’s negative emotions (e.g. a...

Data heterogeneity, insufficient scalability, and data privacy protection are the technological challenges of personalized recommendations. This study proposes a new federated learning algorithm (FedSarah) to address low scalability caused by data heterogeneity and uneven computing power in consumer-centric personalized recommendation systems while...

As an emerging technology, Blockchain (BC) has been playing a promising role in today's Software Defined-Networking (SDN)-enabled Internet of Things (IoT) applications. Because of the salient feature of the Network Function Virtualization (NFV) techniques, SDN can ensure an IoT system runs efficiently and smoothly in a cloud-driven ecosystem. When...

Non-coding RNAs (ncRNAs) play an important role in revealing the mechanism of human disease for anti-tumor and anti-virus substances. Detecting subcellular locations of ncRNAs is a necessary way to study ncRNA. Traditional biochemical methods are time-consuming and labor-intensive, and computational-based methods can help detect the location of ncR...

This paper presents a portable toolkit, SwitchNet, for extracting relations from textual input. We summarize four data protocols for relation extraction tasks, including relation classification, relation extraction, triple extraction, and distant supervision relation extraction. This neural architecture is modular, so it can take as input data at d...

DNA-binding proteins (DBPs) protect DNA from nuclease hydrolysis, inhibit the action of RNA polymerase, prevents replication and transcription from occurring simultaneously on a piece of DNA. Most of the conventional methods for detecting DBPs are biochemical methods, but the time cost is high. In recent years, a variety of machine learning-based m...

In this paper, a Covid-19 dynamical transmission model of a coupled non-linear fractional differential equation in the Atangana-Baleanu Caputo sense is proposed. The basic dynamical transmission features of the proposed system are briefly discussed. The qualitative as well as quantitative results on the existence and uniqueness of the solutions are...

Some of the significant new technologies researched in recent studies include Blockchain (BC), Software Defined Networking (SDN), and Smart Industrial Internet of Things (IIoT). All three technology provide data integrity, confidentiality, and integrity in their respective use cases (especially in industrial fields). Additionally, cloud computing h...

This study discusses the new stochastic maximum power point tracking control approach toward the photovoltaic cells (PCs). A PC generator is isolated from the grid, resulting in a direct current microgrid that can provide changing loads. In the course of the nonlinear systems through the time-varying delays, we proposed networked control sys...

Facial reenactment is aimed at animating a source face image into a new place using a driving facial picture. In a few shot scenarios, the present strategies are designed with one or more identities or identity-sustained suffering protection challenges. These current solutions are either developed with one or more identities in mind, or face identi...

The combination of Non-Orthogonal Multiplex Access and Unmanned Aerial Vehicles (UAV) can improve the energy efficiency (EE) for Internet-of-Things (IoT). On the condition of interference constraint and minimum achievable rate of the secondary users, we propose an iterative optimization algorithm on EE. Firstly, with given UAV trajectory, the Dinke...

The Big Video Data generated in today's smart cities has raised concerns from its purposeful usage perspective, where surveillance cameras, among many others are the most prominent resources to contribute to the huge volumes of data, making its automated analysis a difficult task in terms of computation and preciseness. Violence Detection (VD), bro...

Current human biomedical research shows that human diseases are closely related to non-coding RNAs, so it is of great significance for human medicine to study the relationship between diseases and non-coding RNAs. Current research has found associations between non-coding RNAs and human diseases through a variety of effective methods, but most of t...

The Big Video Data generated in today's smart cities has raised concerns from its purposeful usage perspective, where surveillance cameras, among many others are the most prominent resources to contribute to the huge volumes of data, making its automated analysis a difficult task in terms of computation and preciseness. Violence Detection (VD), bro...

Gene expression data have become increasingly important in machine learning and computational biology over the past few years. In the field of gene expression analysis, several matrix factorization-based dimensionality reduction methods have been developed. However, such methods can still be improved in terms of efficiency and reliability. In this...

In recent years, neural networks have achieved impressive performance on dialogue response generation. However, most of these models still suffer from some shortcomings, such as yielding uninformative responses and lacking explainable ability. The paper proposes a Two-Stage Dialogue Response Generation model (TSRG), which specifies a method to gene...

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in th...

In recent times, COVID-19 infection gets increased exponentially with the existence of a restricted number of rapid testing kits. Several studies have reported the COVID-19 diagnosis model from chest X-ray images. But the diagnosis of COVID-19 patients from chest X-ray images is a tedious process as the bilateral modifications are considered an ill...

With the emergence of deep learning method, which has been driven a great success for the field of person re-identification (re-ID). However, the existing works mainly focus on first-order attention (i.e., spatial and channels attention) statistics to model the valuable information for person re-ID. On the other hand, most existing methods operate...

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare field. Traditionally, the healthcare system works based on centralized agents sharing their raw data. Therefore, huge vulnerabilities and challenges are still existing in th...

Big data is driving the growth of businesses, data is the money, big data is the fuel of the twenty-first century, and there are many other claims over Big Data. Can we, however, rely on big data blindly? What happens if the training data set of a machine learning module is incorrect and contains a statistical paradox? Data, like fossil fuels, is v...

With the emerging development in transport technologies, many vehicle applications access roadside unit (RSU) services. These applications use different types of paid services (e.g., communication, WiFi, and computing nodes) for their execution. The vehicular fog cloud computing (VFCN) is the cooperative computing environment that handles vehicle a...

Biomedical text summarization is a critical task for comprehension of an ever-growing amount of biomedical literature. Pre-trained language models (PLMs) with transformer-based architectures have been shown to greatly improve performance in biomedical text mining tasks. However, existing methods for text summarization generally fine-tune PLMs on th...

The size of data collected around the world is growing exponentially, and it has become popular as big data. The volume and velocity of big data are facilitating the transition of machine learning (ML), deep learning (DL) and artificial intelligence (AI) from research laboratories to real life. There are numerous other claims made about Big Data. C...

Identifying DNA N4-methylcytosine (4mC) sites is of great significance in biological research, such as chromatin structure, DNA stability, DNA-protein interaction and controlling gene expression. However, the traditional sequencing technology to identify 4mC sites is very time-consuming. In order to detect 4mC sites, we develop a multi-view learnin...

Depression has been considered the most dominant mental disorder over the past few years. To help clinicians effectively and efficiently estimate the severity scale of depression, various automated systems based on deep learning have been proposed. To estimate the severity of depression, i.e., the depression severity score (Beck Depression Inventor...

DNA-binding proteins(DBPs) are of great significance in many basic cellular processes. Experiment-based methods for identifying DBPs are costly and time-consuming. To deal with large-scale DBP identification tasks, a variety of computation-based methods have been developed. Inspired by previous work, we propose a multiple Laplacian regularized supp...

Computational Linguistics (CL) associated with the Internet of Multimedia Things (IoMT) enabled multimedia computing applications brings several research challenges, such as real-time speech understanding, deep fake video detection, emotion recognition, and home automation, etc. Due to the emergence of machine translation, CL solutions have increas...

The Internet of things (IoT) has made it possible for health institutions to have remote diagnosis, reliable, preventive and real-time decision making. However, the anonymity and privacy of patients are not considered in IoT. Therefore, this paper proposes a blockchain-based anonymous system, known as GarliMediChain, for providing anonymity and pri...

Multimedia security is a vital sector due to its significant impact on the development of industry 5.0. The current multimedia security systems depend on complex mathematical calculations, proven theoretically and practically in their inability to provide complete protection of information against internal and external attacks and penetration attem...

These days, the usage of machine-learning-enabled dynamic Internet of Medical Things (IoMT) systems with multiple technologies for digital healthcare applications has been growing progressively in practice. Machine learning plays a vital role in the IoMT system to balance the load between delay and energy. However, the traditional learning models f...

The recent booming of artificial intelligence (AI) applications, e.g., affective robots, human-machine interfaces, autonomous vehicles, etc., has produced a great number of multi-modal records of human communication. Such data often carry latent subjective users’ attitudes and opinions, which provides a practical and feasible path to realize the co...

Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of t...

Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of t...

DNA methylation is an epigenetic marker that plays an important role in the biological processes of regulating gene expression, maintaining chromatin structure, imprinting genes, inactivating X chromosomes, and developing embryos. The traditional detection method is time-consuming. Currently, researchers have used effective computational methods to...

—Internet of medical things (IoMT) has made it possible to collect applications and medical devices to improve healthcare information technology. Since the advent of the pandemic of coronavirus (COVID-19) in 2019, public health information has become more sensitive than ever. Moreover, different news items incorporated have resulted in differing pu...

Analysis of malignant and non-malignant brain tumors is done using a computer-aided diagnosis system by practitioners worldwide. Radiologists refer computer-assisted techniques to draw conclusions using image modalities and inferences. Pedagogically, various machine learning approaches have been used, which usually focus on the classification of im...

Intrusion detection systems (IDS) are amongst the most important automated defense mechanisms in modern industry. It is guarding against many attack vectors, especially in healthcare, where sensitive information (patient’s medical history, prescriptions, electronic health records, medical bills/debts, and many other sensitive data points) is open t...

Cybersecurity issues such as malware, denial of service attacks, and unauthorized access to data for different applications are growing daily. The Industrial Internet of Healthcare Things (IIoHT) has recently been a new healthcare mechanism where many healthcare applications can run on hospital servers for remote medical services. For instance, clo...