In this paper, linear regression (LR), multi-linear regression (MLR) and polynomial regression (PR) techniques are applied to propose a model Li-MuLi-Poly. The model predicts COVID-19 deaths happening in the United States of America. The experiment was carried out on machine learning model, minimum mean square error model, and maximum likelihood ratio model. The best-fitting model was selected according

To enhance the security of single-chaotic systems, we propose a novel image encryption cryptosystem based on true random numbers and chaotic systems. First, we select any one of several chaotic systems. Next, the hash function is used to calculate the initial value of the chaotic system using a plaintext image. Then, we obtain a solution of this chaotic system and use the k-medoids clustering machine-learning

Image forgery detection focuses more on tampering regions than image content of semantic segmentation, it is revealed that wealthier features need to be learned. Moreover, insufficient semantic information causes low efficiency of forgery detection. To address these issues, we propose a hybrid features and semantic reinforcement network (HFSRNet) for image forgery detection, which is an encoding and

Machine learning and deep learning play a crucial role in identification of various diseases like neurological, skin, eyes, blood and cancers. The deep learning algorithms can be performed promising for prediction of Alzheimer’s disease using MRI scans. Alzheimer disease becoming more common in the people (age 65 years or above). The disease becomes severe before the symptoms appear and causes brain

Key frame extraction is an important manner of video summarization. It can be used to interpret video content quickly. Existing approaches first partition the entire video into video clips by shot boundary detection, and then, extract key frames by frame clustering. However, in most team-sport videos, a video clip usually includes many events, and it is difficult to extract the key frames related to

Implementing pedestrian detection real-time embedded systems remains a major challenge. Detecting pedestrians in an advanced driver assistance system requires a lot of time and resources. The method based on Multi-Scale Region Covariance Descriptor (MSRCD) and Support Vector Machine (SVM) is one of the most effective approaches to perform pedestrian detection. However, such implementation is difficult

The performance of multi-person pose estimation has significantly improved with the development of deep convolutional neural networks. However, two challenging issues are still ignored but are key factors causing deterioration in the keypoint localization. These two issues are scale variation of human body parts and huge information loss caused by consecutive striding in multiple upsampling. In this

Visible light polarization image desmogging aims to recover the clear image solely from an input visible light polarization image with smog. It is a challenging research topic due to desmogging is an ill-posed problem. Most of existing methods effort to build desmogging physical model based on classical atmospheric scattering principle. However, compared with natural image dehazing task, the smog in

Malignant melanoma is one of the most dreadful skin cancer types caused by the abnormal growth of melanocyte cells. Deep convolutional neural network (CNN) models are becoming prominent for the automated diagnosis of melanoma from dermoscopic images. Although being incredibly accurate, the “black-box” nature of deep CNN models due to the lack of proper interpretability still prevents their wide-spread

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep

Weight normalization (WN) can help to stabilize the distribution of activations over layers, which boost the performance of DNNs in generalization. In this paper, we further propose deep structural weight normalization (DSWN) methods to inject the network structure measurements into the WN to fully acknowledge the data propagation through the neural network. In DSWN, two novel structural measurements

Scientists and researchers have tried to establish a bond between the emotions conveyed and the subsequent mood perceived in a person. Emotions play a major role in terms of our choices, preferences, and decision-making. Emotions appear whenever a person perceives a change in their surroundings or within their body. Since early times, a considerable amount of effort has been made in the field of emotion

Classification of human emotions based on electroencephalography (EEG) is a very popular topic nowadays in the provision of human health care and well-being. Fast and effective emotion recognition can play an important role in understanding a patient’s emotions and in monitoring stress levels in real-time. Due to the noisy and non-linear nature of the EEG signal, it is still difficult to understand

In this paper, an enhanced version of Embedded zerotree wavelet (EZW) image coding algorithm is proposed, referred to as EZW-SC. By exploiting a new principle that relies on a subband classification concept, the enhanced algorithm allows the prediction of insignificant subbands at early passes, along with the use of an improved significance map. This reduces the redundancy of zerotree symbols, speeds

Due to the superior performance and fast running speed, deep learning methods have been widely employed in image processing fields. However, most deep learning-based denoising methods require a specific noise level to train their models, and denoising models are built to remove specific levels of noise, which lacks the flexibility to deal with spatially variant noise. In this paper, we present a discrete

Various performance measures based on the ground truth and without ground truth exist to evaluate the quality of a developed tracking algorithm. The existing popular measures—average center location error (acle) and average tracking accuracy (ata) based on ground truth—may sometimes create confusion to quantify the quality of a developed algorithm for tracking an object under some complex environments

Steganalysis is a technique for detecting existence of secret data hidden in digital media. Researchers are often concentrated in minimizing the misclassification rate. Many steganalysis methods try to choose high-dimension features to reveal subtle changes due to data embedding. By exploring properties of the filter bases, we steer the filters to capture tiny embedding traces in different scales and

The advent of internet-based video streaming service has changed the paradigm of streaming content delivery. However, there has been a call for traditional satellite and the online platform to coexist to improve the access to content delivered to subscribers. In addition, revenue earning is also challenging for service providers especially those unknown to subscribers. Therefore, it is important to

Aggressive comments containing offensive images and inappropriate gesture signs together with textual comments have grown exponentially in the recent past on social media. These aggressive contents on social media are affecting the victims negatively causing fear, stress, sleeping problems and even suicide in some cases. Since social media contents are unmoderated, a technical solution with the characteristic

The live streaming of large-scale events over the Internet attracts a highly diverse audience. Despite the progress in streaming technologies, which employ adaptive bitrate to dynamically adjust the streaming of individual clients to reflect their resource availability, content providers still struggle to efficiently match provisioned capacity and demand in such events. In this paper, we present an

In this paper, a novel two-stage R-CNN network called ParallelNet is proposed for thigh fracture detection task. In the proposed method, multiple parallel backbone networks and a feature fusion connection structure are designed, which can extract features with different reception fields. Specifically, the first backbone network is denoted as main network, which adopted normal convolution to detect

Both JPEG restoration and anti-forensics remove the artifacts left by JPEG compression, and recover the JPEG compressed image. However, how to restore the high-frequency details of a JPEG compressed image for JPEG restoration and how to deceive the existing JPEG compression detectors without sacrificing visual quality in JPEG anti-forensics remain challenging. To address these issues, a framework of

Pre-processing of medical images plays a vibrant part in the field of medicine to detect patient’s disease at an earlier stage. Hospitals and medical centers generate an enormous volume of digital medical images day by day, which is used for several purposes of diagnostic procedures. Because of many images,for secured transmission, image compression is required to reduce the redundancies in the image

A correction to this paper has been published: https://doi.org/10.1007/s00530-021-00791-9

With the increase in the culture of social media and netizen, every day, millions of comments are posted on the uploaded posts. The use of abusive language in user comments has been increased rapidly. Abusive language in online comments initiates cyber-bullying that targets individuals (celebrity, politician, and product) and a group of people (specific country, age, and religion). It is important

Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity’s body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January

Artificial intelligence has the potential to revolutionize disease diagnosis, classification, and identification. However, the implementation of clinical-decision support algorithms for medical imaging faces challenges with reliability and interpretability. This study presents a diagnostic tool based on a deep-learning framework for four-class classification of ocular diseases by automatically detecting

In the technological era, the primary source of information is in the form of digital data, which has to be secured while storing or transmitting during communication over an unsecured network. Different approaches are used to provide security to digital data, viz. text, audio, image, and video. This paper initially explains the security system such as cryptography, watermarking, and steganography

Knowledge of the underlying anatomy of the left atrium can promote improved diagnostic protocols and clinical interventions. Hence, an automatic segmentation of the left atrium on magnetic resonance imaging (MRI) can support diagnosis, treatment and surgery planning of heart. However, due to the small size of left atrium with respect to the whole MRI volume, accurate segmentation of left atrium is

Recent studies show that the performance of deep convolutional neural network (CNN) applied to steganalysis is better than that of traditional methods. However, the existing network structure is still caused by artificial design, which may not be the optimal training network. This paper describes a deep residual network based on a neural architecture search (NAS) algorithm, to minimize the artificial

Point cloud analysis becomes a fundamental but challenging problem in the field of 3D scene understanding. To deal with unstructured and unordered point clouds in the embedded 3D space, we propose a novel direction-induced convolution (DIConv) to obtain the hierarchical representations of point clouds and then boost the performance of point cloud analysis. Specifically, we first construct a direction

Human action recognition is widely explored as it finds varied applications including visual navigation, surveillance, video indexing, biometrics, human–computer interaction, ambient assisted living, etc. This paper aims to design and analyze the performance of Spatial and Temporal CNN streams for action recognition from videos. An action video is fragmented into a predefined number of segments called

Now, all satellite channels handle high-quality data, including HD video files (1 K, 2 K, and 4 K). High-quality compression software is required to archive and transmit these files accurately and efficiently. HEVC is a standard development technique for high-quality video compression which can produce double compression while preserving video quality. On the other hand, the use of a robust digital

Cyber-bullying has been on the rise especially after the explosive widespread of various cyber-attacks. Various types of techniques have been used to tackle cyber-bullying. These techniques focused primarily on data traffic for monitoring malicious activities. This research proposes a methodology where we can detect early Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks. First

A high-level of understanding about the surrounding context of an image is indispensable for VQA when faced with difficult questions. Previous studies address this issue by modeling object-level visual contents and transforming the internal relationships into a graph or tree. On one hand, however, this still leaves a gap between the modalities of language and vision. On the other hand, the abstract-level

The popular task of 3D human action recognition is almost exclusively solved by training deep-learning classifiers. To achieve high recognition accuracy, input 3D actions are often pre-processed by various normalization or augmentation techniques. However, it is not computationally feasible to train a classifier for each possible variant of training data to select the best-performing combination of

Recently, most existing human pose estimation methods fuse multi-stage convolutional modules to learn a shared feature representation. In this paper, we propose a expectation–maximization (EM) mapping-based network to learn specific related body parts for human pose estimation, named EMposeNet. It maps specific feature of related parts from the original fully shared feature space. From the perspective

This paper introduces a new multi-image cryptosystem based on modified Henon map and non-linear combinations of chaotic seed maps. Based on the degree of correlation between the adjacent pixels of the plain images, a unique weight is assigned to the plain images. First, the coordinates of plain images are disrupted by modified Henon map as confusion phase. In the first step of diffusion phase, the

Frequent itemset mining (FIM) is a significant data mining technique which is widely adopted in numerous applications for exploring frequent items. With the rapid growth and expansion of datasets, FIM has become an interesting topic for many researchers, which has triggered many innovations of numerous FIM algorithms in the big data environment. This study aims to design an optimization parallel frequent

The scalable extension of the high efficiency video coding standard (SHVC) combines the large compression efficiency and high visual quality of HEVC, with the possibility of encoding different versions of the same video in a single bitstream. However, this comes at the cost of high computational complexity. In this context, many research works aim to reduce this complexity for texture images. We aim

Cryptography plays a significant role in ensuring data security and confidentiality. The security provided by a crypto system mainly depends on the secrecy of the cryptographic key. If the secret key gets compromised, then it may lead to compromise of the protected data. Biometric cryptosystem provides a solution for securing the cryptographic key by binding the secret key with user biometric data

In this paper, we propose an energy-saving video compression scheme targeting to work over an IoT network where the perception layer is implemented through smartphone-based delay-tolerant network (DTN). Here, the inter-frame compression is done by a proposed progression-based frame prediction technique and a newly introduced frame-similarity measurement parameter. The inter-frame co-relation is removed

In recent years, face synthesis and manipulation technology have been developed rapidly, and now it is feasible to synthesize extremely realistic fake face videos, which can easily deceive existing face recognition systems. Due to the high quality of fake videos, allowing fake face videos to propagate on Internet may cause serious ethical, moral and legal problems. Therefore, the effective and reliable

In today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions

Communication and storage technologies have greatly advanced in recent years, providing ever increased bandwidths and storage capacities respectively. But, efficient utilization of resources like network bandwidth and data storage is relevant even today. More and more data are generated every unit of time and this enormous data is sent over networks and stored in local and/or cloud storage devices

With the rapid development of intelligent transportation, the video surveillance system as its important component has been achieved much attention. Traffic condition closely related to people’s lives needs to be tracked in time. Some methods estimate traffic flow by analyzing the pictures taken by fixed cameras. However, they can only estimate the traffic condition of particular roads. Different from

Among other grain-based foods, rice is an important and favorite food of Pakistan. Its demand has increased significantly in the recent era. The type of rice grains is very important for export and import. Therefore, it becomes necessary to distinguish rice types to avoid their fraudulent labeling. With similar aims, a dataset consisting of seven rice varieties, i.e., Basmati 2000, Chenab basmati,

Learning distinguishing features from training datasets while filtering features of occlusions is critical to person retrieval scenarios. Most of the current person re-identification (Re-ID) methods based on classification or deep metric representation learning tend to overlook occlusion issues on the training set. Such representations from obstacles are easily over-fitted and misleading due to being

In recent years, machine learning has become a hot research method in various fields and has been applied to every aspect of our life, providing an intelligent solution to problems that could not be solved or difficult to be solved before. Machine learning is driven by data. It learns from a part of the input data and builds a model. The model is used to predict and analyze another part of the data

With the development of motion capture technologies, 3D action recognition has become a popular task that finds great applicability in many areas, such as augmented reality, human–computer interaction, sports, or healthcare. On the other hand, the acquisition of 3D human skeleton data is an expensive and time-consuming process, mainly due to the high costs of capturing technologies and the absence

Low contrast image is one of the major challenges in photography. The low contrast image not only poses difficulty to the interpretation of the scene but also causes trouble in the onward processing of the image for computer vision tasks. Histogram equalization (HE) is a traditional and widely used approach for contrast enhancement and applies to almost all types of images. However, HE causes over-enhancement

Self-embedding fragile watermarking algorithms can perform detection of the manipulated areas as well as recovery of these detected areas. Most of the self-embedding fragile watermarking algorithms are performed block-wise authentication approach. However, entire block is marked as tampered in case one of the pixels in the block is detected as manipulated. This situation decreases the accuracy rate

Cyberbullying is the use of information technology networks by individuals’ to humiliate, tease, embarrass, taunt, defame and disparage a target without any face-to-face contact. Social media is the 'virtual playground' used by bullies with the upsurge of social networking sites such as Facebook, Instagram, YouTube and Twitter. It is critical to implement models and systems for automatic detection

Anomaly in Online Social Network can be designated as an unusual or illegal activity of an individual. It can also be considered as an outlier or a surprising truth. Due to the emergence of social networking sites such as Facebook, Instagram, etc., the number of negative impacts of aggressive and bullying phenomena has increased exponentially. Anomaly detection is a problem of crucial importance which

Keypoint detection is an important research topic in target recognition and classification. This paper studies the detection of keypoints in images of Amur tigers and proposes a target keypoint detection method based on heterogeneous convolution neural networks. Because of the limited storage capacity of the monitoring device and higher accuracy requirement, we propose a heterogeneous convolution called

Agility gives the ability to be highly responsive to changes and make improvements in the way the agile project is documented. The just-in-time and just barely good enough documentation may miss out on important executable specifications. Interestingly, the frequently asked ‘how-to-do’ questions on the popular question answering (Q&A) websites like stack overflow are strong indicators of gaps in documentation

This paper proposes a deep learning priors-based data reconstruction method of 1D isometric feature mapping (ISOMAP) representations. ISOMAP is a classical algorithm of nonlinear dimensionality reduction (NLDR) or manifold leaning (ML), which is devoted to questing for the low dimensional structure of high dimensional data. The reconstruction of ISOMAP representations, or the inverse problem of ISOMAP

Driver action recognition aims to distinguish normal driver action and some abnormal driver actions such as leaving the wheel, talking on the phone, diving with smoking, etc. For the purpose of traffic safety, studies on the computer vision technologies for driver action recognition have become especially meaningful. However, this issue is far from being solved, mainly due to the subtle variations

This paper is concerned withthe problem of paraphrase detection. For a number of applications, the ability to detect similar sentences, such as text mining, summary text, plagiarism detection, authorship authentication, and question answering, is important. Given two phrases, the goal is to detect whether they are identical semantically. This work involves a novel model namely, ParaCap, which uses

Clinical decisions are more promising and evidence-based, hence, big data analytics to assist clinical decision-making has been expressed for a variety of clinical fields. Due to the sheer size and availability of healthcare data, big data analytics has revolutionized this industry and promises us a world of opportunities. It promises us the power of early detection, prediction, prevention, and helps