GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection.

The digital era has expanded social exposure with easy internet access for mobile users, allowing for global communication. Now, people can get to know what is going on around the globe with just a click; however, this has also resulted in the issue of fake news. Fake news is content that pretends to be true but is actually false and is disseminated to defraud.

Quality and antioxidant potential of goat's milk paneer prepared from different citrus juices and its whey.

The experiments reported in this research paper aimed to evaluate the physico-chemical and sensory characteristics, microbial quality and antioxidant potential of goat's milk paneer during storage (0-12 d, 4 ± 1°C). The juices from five different citrus fruits were used as coagulant (treatments) to make goat's milk paneer. The pH of all paneer samples decreased during storage whereas titratable acidity increased.

Internet of medical things and blockchain-enabled patient-centric agent through SDN for remote patient monitoring in 5G network.

During the COVID-19 pandemic, there has been a significant increase in the use of internet resources for accessing medical care, resulting in the development and advancement of the Internet of Medical Things (IoMT). This technology utilizes a range of medical equipment and testing software to broadcast patient results over the internet, hence enabling the provision of remote healthcare services. Nevertheless, the preservation of privacy and security in the realm of online communication continues to provide a significant and pressing obstacle.

4SQR-Code: A 4-state QR code generation model for increasing data storing capacity in the Digital Twin framework.

Introduction: The usage of Quick Response (QR) Codes has become widely popular in recent years, primarily for immense electronic transactions and industry uses. The structural flexibility of QR Code architecture opens many more possibilities for researchers in the domain of the Industrial Internet of Things (IIoT). However, the limited storage capacity of the traditional QR Codes still fails to stretch the data capacity limits.

A Novel Deep Learning-Based Classification Framework for COVID-19 Assisted with Weighted Average Ensemble Modeling.

COVID-19 is an infectious disease caused by the deadly virus SARS-CoV-2 that affects the lung of the patient. Different symptoms, including fever, muscle pain and respiratory syndrome, can be identified in COVID-19-affected patients. The disease needs to be diagnosed in a timely manner, otherwise the lung infection can turn into a severe form and the patient's life may be in danger.

Metal organic framework derived NiO nanoparticles for application as a hole transport layer in perovskite solar cells.

NiO as a hole transport layer (HTL) has gained a lot of research interest in perovskite solar cells (PSCs), owing to its high optical transmittance, high power conversion efficiency, wide band-gap and ease of fabrication. In this work, four different nickel based-metal organic frameworks (MOFs) using 1,3,5-benzenetricarboxylic acid (BTC), terephthalic acid (TPA), 2-aminoterephthalic acid (ATPA), and 2,5-dihydroxyterephthalic acid (DHTPA) ligands respectively, have been employed as precursors to synthesize NiO NPs. The employment of different ligands was found to result in NiO NPs with different structural, optical and morphological properties.

IoT-Based Medical Image Monitoring System Using HL7 in a Hospital Database.

In recent years, the healthcare system, along with the technology that surrounds it, has become a sector in much need of development. It has already improved in a wide range of areas thanks to significant and continuous research into the practical implications of biomedical and telemedicine studies. To ensure the continuing technological improvement of hospitals, physicians now also must properly maintain and manage large volumes of patient data.

Human Fall Detection Using 3D Multi-Stream Convolutional Neural Networks with Fusion.

Human falls, especially for elderly people, can cause serious injuries that might lead to permanent disability. Approximately 20-30% of the aged people in the United States who experienced fall accidents suffer from head trauma, injuries, or bruises. Fall detection is becoming an important public healthcare problem.

Analysis of IoT-Related Ergonomics-Based Healthcare Issues Using Analytic Hierarchy Process Methodology.

The objective of the present work is for assessing ergonomics-based IoT (Internet of Things) related healthcare issues with the use of a popular multi-criteria decision-making technique named the analytic hierarchy process (AHP). Multiple criteria decision making (MCDM) is a technique that combines alternative performance across numerous contradicting, qualitative, and/or quantitative criteria, resulting in a solution requiring a consensus. The AHP is a flexible strategy for organizing and simplifying complex MCDM concerns by disassembling a compound decision problem into an ordered array of relational decision components (evaluation criteria, sub-criteria, and substitutions).

A Novel QKD Approach to Enhance IIOT Privacy and Computational Knacks.

The industry-based internet of things (IIoT) describes how IIoT devices enhance and extend their capabilities for production amenities, security, and efficacy. IIoT establishes an enterprise-to-enterprise setup that means industries have several factories and manufacturing units that are dependent on other sectors for their services and products. In this context, individual industries need to share their information with other external sectors in a shared environment which may not be secure.

Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects.

Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has been conducted on HAR and numerous approaches based on deep learning have been exploited by the research community to classify human activities. The main goal of this review is to summarize recent works based on a wide range of deep neural networks architecture, namely convolutional neural networks (CNNs) for human activity recognition.

Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues.

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 this system.

Proactive Caching in D2D Assisted Multitier Cellular Network.

Cache-enabled networks suffer hugely from the challenge of content caching and content delivery. In this regard, cache-enabled device-to-device (D2D) assisted multitier cellular networks are expected to relieve the network data pressure and effectively solve the problem of content placement and content delivery. Consequently, the user can have a better opportunity to get their favored contents from nearby cache-enabled transmitters (CETs) through reliable and good-quality links; however, as expected, designing an effective caching policy is a challenging task due to the limited cache memory of CETs and uncertainty in user preferences.

Electroencephalogram-Based Motor Imagery Signals Classification Using a Multi-Branch Convolutional Neural Network Model with Attention Blocks.

Brain signals can be captured via electroencephalogram (EEG) and be used in various brain-computer interface (BCI) applications. Classifying motor imagery (MI) using EEG signals is one of the important applications that can help a stroke patient to rehabilitate or perform certain tasks. Dealing with EEG-MI signals is challenging because the signals are weak, may contain artefacts, are dependent on the patient's mood and posture, and have low signal-to-noise ratio.

Deep learning application detecting SARS-CoV-2 key enzymes inhibitors.

The fast spread of the COVID-19 over the world pressured scientists to find its cures. Especially, with the disastrous results, it engendered from human life losses to long-term impacts on infected people's health and the huge financial losses. In addition to the massive efforts made by researchers and medicals on finding safe, smart, fast, and efficient methods to accurately make an early diagnosis of the COVID-19.

Seroprevalence and Molecular Evidence of in Dromedary Camels of Pakistan.

Coxiellosis is a zoonosis in animals caused by . A cross-sectional study was conducted on 920 (591 female and 329 male) randomly selected camels () of different age groups from 13 districts representative of the three different ecological zones in the Province Punjab, Pakistan to determine the prevalence and associated risk factors of coxiellosis. The blood samples were collected and tested for anti- antibodies using indirect multispecies ELISA.

Deep learning in multimedia healthcare applications: a review.

The increase in chronic diseases has affected the countries' health system and economy. With the recent COVID-19 virus, humanity has experienced a great challenge, which has led to make efforts to detect it and prevent its spread. Hence, it is necessary to develop new solutions that are based on technology and low cost, to satisfy the citizens' needs.

Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence.

In most regions of the world, tuberculosis (TB) is classified as a malignant infectious disease that can be fatal. Using advanced tools and technology, automatic analysis and classification of chest X-rays (CXRs) into TB and non-TB can be a reliable alternative to the subjective assessment performed by healthcare professionals. Thus, in the study, we propose an automatic TB detection system using advanced deep learning (DL) models.

A Multi-Branch Convolutional Neural Network with Squeeze-and-Excitation Attention Blocks for EEG-Based Motor Imagery Signals Classification.

Electroencephalography-based motor imagery (EEG-MI) classification is a critical component of the brain-computer interface (BCI), which enables people with physical limitations to communicate with the outside world via assistive technology. Regrettably, EEG decoding is challenging because of the complexity, dynamic nature, and low signal-to-noise ratio of the EEG signal. Developing an end-to-end architecture capable of correctly extracting EEG data's high-level features remains a difficulty.

Intelligent Tracking of Mechanically Thrown Objects by Industrial Catching Robot for Automated In-Plant Logistics 4.0.

Industry 4.0 smart manufacturing systems are equipped with sensors, smart machines, and intelligent robots. The automated in-plant transportation of manufacturing parts through throwing and catching robots is an attempt to accelerate the transportation process and increase productivity by the optimized utilization of in-plant facilities.

Efficacy Evaluation of a Combined Hemorrhagic Septicemia-Mastitis Vaccine in Dairy Cows and Buffaloes.

Hemorrhagic septicemia (HS) and mastitis caused by Pasteurella (P.) multocida, Staphylococcus (S.) aureus and Streptococcus (Str.