To ensure the image security, a large number of ciphertext image retrieval methods have been studied and applied, such as homomorphic encryption and multi-key encryption. However, most of these algorithms do not consider the protection of image copyright, user information and traitor tracking. For this reason, this paper proposes a privacy-preserving and traitor tracking content-based image retrieval

Speech signal is commonly debased by room reverberation and included noises in genuine climates. This paper focuses on disengaging objective speech signals in reverberant conditions from multichannel input signals. To overcome all the existing drawbacks, this work proposes an efficient technique like, multichannel speech signal separation using a new hybrid method that combines grasshopper optimization-based

Animated emoji is a kind of GIF image, which is widely used in online social networks (OSN) for its efficiency in transmitting vivid and personalized information. Aiming at realizing covert communication in animated emoji, this paper proposes an improved steganography framework in animated emoji. We propose a self-reference algorithm to improve the steganography security. Meanwhile, the relations between

Myocardial infarction (MI) is an acute interruption of blood flow to the heart, which causes the heart to suffer from a deficiency of blood and ischemia, so the heart muscle is damaged, and cells can die and lose their function. Despite the low incidence of MI in the world, it is still a common disease-causing death. Therefore, detecting the MI signals early can reduce mortality. This paper presented

Classical cryptosystems are not suitable for all situations for they fail to meet some current security requirements due to their small keyspace, which makes them prone to brute-force attacks, as well as their inefficiency in real-time applications due to their considerable time complexity. Recently, secure applications such as hyperchaos-based speech signals became a focal topic for research. Their

Ulcerative colitis (UC) can be classified as proctitis, left-sided colitis or pancolitis, usually with rectal involvement at the beginning. Mucosal carcinogenesis is one of the most severe complications of UC. Persistent inflammation of the rectal mucosa may be an essential cause of mucosal cancer, thus the detection of rectal inflammation is of great significance. In this paper, we propose a transfer

This paper proposes a novel reversible data hiding (RDH) scheme in encrypted domain for medical DICOM images. Although a lot of RDH schemes for encrypted images have been presented, however, most of them are unsuitable for medical DICOM images, as they do not utilize the features of the DICOM image format, and the recovery accuracy is low because medical images have large areas with the same pixel

There has been less attention towards the research on face recognition with partial occlusion. Facial accessories such as masks, sunglasses, and caps, etc., cause partial occlusion which results in a significant performance drop of the face recognition system. In this paper, a novel compressed string matching algorithm based on run-length encoding (CSM-RL) is proposed to solve the partial occlusion

Image contrast enhancement is a prerequisite and plays a very important role in many image processing field like medical imaging, face recognition, computer-vision, and satellite imaging. In this paper we proposed reversible data hiding based Limited Dynamic Weighted Histogram Equalization techniques for Abnormal Tumor regions which improve the contrast, transmit the hidden secret information, preserve

Data hiding techniques are used to embed information into digital objects for the purpose of copyright protection, covert communication, media notation, among other applications. Compared with traditional data hiding techniques, reversible data hiding (RDH) can restore the original digital object after extracting the hidden data. This makes RDH very useful for applications requiring high fidelity of

In this paper, PAMTEL-RT, a web-based platform to provide remote health professional support and guidance during the initial assistance of emergency situations is presented. In particular, this platform focuses on the pediatric population and has been specifically designed to be used by non-healthcare professionals in training centers, such as educational or sport centers. It allows pediatricians in

Recently, the introduction of Convolutional Neural Network (CNNs) has advanced the way of solving image segmentation tasks. Semantic image segmentation has considerably benefited from employing various CNN models. The most widely used network in this field is U-Net and its different variations. However, these models require significant number of trainable parameters, floating-point operations per second

Face spoofing detection, also known as liveness detection, is a challenging and one of the most active research areas in computer vision. In this paper, a novel texture descriptor, namely equilibrium difference local binary pattern (ED-LBP), is proposed for the representation and recognition of face texture. First, the adjacent pixels discrepancy in a facial image is fully considered and the discrepancy

With the development of deep learning (DL), convolutional neural networks (CNNs) have shown great reconstruction performance in single image super-resolution (SISR). However, some methods blindly deepen the networks to purchase the performance, which neglect to make full use of the multi-scale information of different receptive fields and ignore the efficiency in practice. In this paper, a lightweight

With the rapid growth of big multimedia data, multimedia processing techniques are facing some challenges, such as knowledge understanding, semantic modeling, feature representation, etc. Hence, based on TextCNN and WGAN-gp (improved training of Wasserstein GANs), a deep learning framework is suggested to improve the efficiency of discriminating the specific style features and the style-independent

To simplify effective music filtering, recommender systems (RS) have received great attention from both industry and academia area. To select which music to recommend, traditional RS uses an approximation of users’ real interests. However, while discarding users’ contexts, profiles information is not able to reflect their exact needs and to provide overpowering recommendations. One of the main issues

Managing the integrity of products and processes in a multi-stakeholder supply chain environment is a significant challenge. Many current solutions suffer from data fragmentation, lack of reliable provenance, and diverse protocol regulations across multiple distributions and processes. Amongst other solutions, Blockchain has emerged as a leading technology, since it provides secure traceability and

There are many techniques for conducting image analysis and pattern recognition. This papers explores a way to optimize one of these techniques—image segmentation—with the help of a novel hybrid optimization algorithm. Image segmentation is mostly used for a semantic segmentation of images, and thresholding is one the most common techniques for performing this segmentation. Otsu’s and Kapur’s thresholding

With the improvement of SAR resolution, super-resolution SAR imaging is more and more widely used in the all-time and all-weather video surveillance and remote sensing imaging. One implementation of super-resolution SAR is that radar works in the spotlight mode. In the case with highly squinted angle and acceleration, the azimuth space variance and the coupling between range and azimuth will become

One dimensional encoding in optical communications has limitations in terms of the number of supported users and large bandwidth consumption. This study presents a new algorithm to generate two-dimensional (2D) encoding utilizing permutation vectors (PV) theory for incoherent multiple access network to suppress multiple access interference (MAI) and system complexity. The proposed code design approach

A skip-connection learning framework-based convolution neural network (CNN) has recently achieved great success in image super-resolution (SR). However, most CNN models based on the skip-connection learning framework do not fully make use of potential multi-scale features of images. In this paper, we propose a multi-scale skip-connection network (MSN) to improve the visual quality of the image SR.

Cyberbullying can be delineated as a purposive and recurrent act, which is aggressive in nature, done via different social media platforms such as Facebook, Twitter, Instagram, and others. A state-of-the-art pre-training language model, BERT (Bidirectional Encoder Representations from Transformers), has achieved remarkable results in many language understanding tasks. In this paper, we present a novel

Medical image fusion is a synthesis of visual information present in any number of medical imaging inputs into a single fused image without any distortion or loss of detail. It enhances image quality by retaining specific features to improve the clinical applicability of medical imaging for treatment and evaluation of medical conditions. A big challenge in the processing of medical images is to incorporate

This paper develops an end-to-end ECG signal classification algorithm based on a novel segmentation strategy and 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals and alleviate the workload of physicians. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) is used for segmenting the

Segmentation of the liver from abdominal CT images is an essential step for computer-aided diagnosis and surgery planning. The U-Net architecture is one of the most well-known CNN architectures which achieved remarkable successes in both medical and biological image segmentation domain. However, it does not perform well when the target area is small or partitioned. In this paper, we propose a novel

Background subtraction, being the most cited algorithm for foreground detection, encounters the major problem of proper threshold value at run time. For effective value of the threshold at run time in background subtraction algorithm, the primary component of the foreground detection process, motion is used, in the proposed algorithm. For the said purpose, the smooth histogram peaks and valley of the

In digital communication, it is very necessary to prevent the unlawful intrusion of unwanted parties. Apart from that, the authenticity of a signal being sent is a key factor otherwise false data can break the commitment between the sender and the receiver of the signal. By digital watermarking, both the insurance of the authenticity and the prevention of unlawful intrusion can be maintained by inserting

Demosaicking is the way toward reproducing a full hued picture from a deficient shaded picture. The single sensor doesn't catch all hues for a single pixel. To address this, a color filter array (CFA) is utilized to get a hued picture from a single sensor. The created picture from CFA is called a mosaic picture. In this research, we utilize specialized networks to remove the noise from Bayer images

Social media platforms have enabled users to share their thoughts, ideas, and opinions on different subject matters and meanwhile generate lots of information which can be adopted to understand people’s emotion towards certain products. This information can be effectively applied for Aspect Category Detection (ACD). Similarly, people’s emotions and recommendation-based Artificial Intelligence (AI)-powered

Image hashing is a useful technology of many multimedia systems, such as image retrieval, image copy detection, multimedia forensics and image authentication. Most of the existing hashing algorithms do not reach a good classification between robustness and discrimination and some hashing algorithms based on dimensionality reduction have high computational cost. To solve these problems, we propose a

Resampling detection is an important problem in image forensics. Several exiting approaches have been proposed to solve it, but few of them focus on resampling parameter estimation. Especially, the estimation of downsampling scenarios is very challenging. In this paper, we propose a dual-filtering based convolutional neural network (CNN) to extract features directly from the images. First, we analyze

Motion estimation from a video sequence is an interesting issue in video processing. Nowadays, research has been focused on global optimization techniques, that estimate the optical flow for pixel neighborhoods. In this paper, a hybrid statistically effective motion estimation procedure has been proposed for better effectiveness video compression. This method explores by utilizing a hexagonal search

This work presents a high-performance approach for 3D lung segmentation tasks in computer tomography images using a new intelligent machine learning algorithm called Floor of Log(FoL). The Support Vector Machine was used to learn the better parameter of the FoL algorithm using the parenchyma and its border as labels. Sensitivity, Matthews Correlation Coefficient (MCC), Hausdorff Distance (HD), Dice

Recently, reversible data hiding in encrypted image (RDHEI) has attracted extensive attention, which can be used in secure cloud computing and privacy protection effectively. In this paper, a low-complexity RDHEI scheme based on MSB hierarchical coding and LSB compression is proposed. Content owner first utilizes a specific encryption method, including pixel encrypting, block, and pixel scrambling

Cyberbullying is disturbing and troubling online misconduct. It appears in various forms and is usually in a textual format in most social networks. Intelligent systems are necessary for automated detection of these incidents. Some of the recent experiments have tackled this issue with traditional machine learning models. Most of the models have been applied to one social network at a time. The latest

Image upsampling and noise removal are important tasks in digital image processing. Single-image upsampling and denoising influence the quality of the resulting images. Image upsampling is known as super-resolution (SR) and referred to as the restoration of a higher-resolution image from a given low-resolution image. In facial expression analysis, the resolution of the original image directly affects

Conventional steganography methods fabricate the secret information into the cover pixels without analyzing the pixel intensities of an image. As a result, some minor pixel level manipulations may lead to huge visual distortion in the stego-image. To this end, in this paper, a novel steganographic scheme based on Kirsch edge detector is proposed. The aim of the scheme is to maximize the payload by

Machine Translation is a mechanism of transforming text from one language to another with the help of computer technology. Earlier in 2018, a machine translation system had been developed by the authors that translate Sanskrit text to Universal Networking Language expressions and was named as SANSUNL. The work presented in this paper is an extension of SANSUNL system by enhancing POS tagging, Sanskrit

User segmentation is an essential element of marketing and product development that considers customers’ needs and recognizes the heterogeneity of those needs. In a key study of smartphone user segmentation, Lee et al. analyzed app usage sequencing using seq2seq architecture. However, despite achieving meaningful results, their approach could not provide a robust interpretation of user segmentation

Habitat mapping is an important and challenging task that helps in monitoring, managing, and preserving ecosystems. It becomes more challenging when marine habitats are mapped, as it is difficult to get quality images in an underwater environment. Moreover, achieving good location accuracy in underwater environments is an additional issue. Sonar imagery has good quality but is hard to be analyzed.

In this paper, an effective method is proposed for restoring underwater image based on color correction and image sharpening. The main purpose of this method is to improve the visibility of underwater images. Traditional methods generally adopt color balancing method to restore the images. However, we found that color balancing has a poor effectiveness on underwater image when the values of blue channel

With time, AI technologies have matured well and resonated in various domains of applied sciences and engineering. The sub-domains of AI, machine learning (ML), deep learning (DL), and associated statistical tools are getting more attention. Therefore, various machine learning models are being created to take advantage of the data available and accomplish tasks, such as automatic prediction, classification

Multi-exposure image fusion is an effective method for depicting high dynamic range of the target scene in a single image. However, there are still some problems remaining: the preserving of global contrast, the preserving of the local details in saturated regions, and the existence of halo artifacts. To solve these problems, this paper proposes a new multi-exposure image fusion algorithm with detail

With the world’s interaction moving more and more toward using online social media platforms, the advent of cyberbullying has also raised its head. Multiple forms of cyberbullying exist from the more common text based to images or even videos, and this paper will explore the context of textual comments. Even in the niche area of considering only text-based data, several approaches have already been

View variation is one of the greatest challenges in the field of gait recognition. Subspace learning approaches are designed to solve this issue by projecting cross-view features into a common subspace before recognition. However, similarity measures are data-dependent, which results in low accuracy when cross-view gait samples are randomly arranged. Inspired by the recent developments of data-driven

The role of compression is inevitable in the storage and transmission of medical images. The polynomial based image compression is proposed in this work for the compression of abdomen CT medical images. The input images are preprocessed by min–max normalization; the pixels are scanned and subjected to polynomial approximation. The polynomial approximated coefficients are subjected to llyods quantization

Virtual reality 360° video has an ultra-high resolution (usually 4–8 K), which takes more coding time than traditional video. Considering the degree of horizontal stretching in a different area of the ERP projected video, a fast coding unit (CU) partition algorithm based on the sum of region-directional dispersion is proposed. The relationship between the current block and its adjacent ones is measured

In today's information age, information is gathered from text and more complex media, such as images, audio, and video. Among these data sources, the rapid growth of video information has led to it to gradually become the main source of information in people's lives. Video information is characterized by many kinds of information, complex forms, and a low degree of structure. Therefore, effectively

One of the biggest changes in H.266/Versatile Video Coding (VVC) is introduced quad-tree with nested multi-type tree (QTMT) coding tree architecture, where the multi-type tree (MTT) structure in H.266/VVC includes binary tree (BT) and ternary tree (TT). Compared with H.265/High Efficiency Video Coding (HEVC) which only is divided by quad-tree (QT), the QTMT architecture makes the coding unit (CU) partition

Action recognition in video is one of the important applications in computer vision. In recent years, the two-stream architecture has made significant progress in action recognition, but it has not systematically explored spatial–temporal features. Therefore, this paper proposes an integrated approach using Gaussian mixture model (GMM) and dilated convolution residual network (GD-RN) for action recognition

Due to the low compression performance of traditional compression models, we have developed a new HOA based Fractional KCA with 2D-DWT for improving the multispectral image quality. In this paper, we present a novel multispectral image compression method for improving the complexity by maintaining quality reconstruction and also reducing the size of the storage of multispectral images. Initially, Karhunen–Loeve

Cricket highlight generation is the process of summarizing a full-length video to a shortened form which should preserve the important moments present in the original video. In this paper, a new approach has been proposed for recognizing the key events and summarization. Audio features are initially used for extracting the excitement clips. Then, the important events like replay, players, umpires,

Color image quantization is a significant procedure of reducing the huge range of color values of a digital color image into a limited range. In this paper, an automated clustering of pixels and color quantization algorithm is proposed. The ideal number of representative colors is unknown beforehand in most color quantization algorithms. This is an important handicap in most practical cases. The proposed

Semantic image segmentation in computer networks is designed to determine the category to which each pixel in an image belongs. It is a basic computer vision task and has a very wide range of applications in practice. In recent years, semantic image segmentation algorithms in computer networks based on deep learning have attracted widespread attention due to their fast speed and high accuracy. However

With the emergence of cyber technology, the biggest evolution has been observed in the use of Internet for financial purposes, in particular for the Internet banking sector. However, with the increase in the number of Internet banking users, many security issues have been discovered. In the recent past, there have been many successful cyber-attacks on the Internet banking services (IBS) throughout

Most state-of-the-art multiple-object tracking (MOT) methods adopt the tracking-by-detection (TBD) paradigm, which is a two-step procedure including the detection module and the tracking module. In these methods, the tracking performance heavily depends on initial detections and data association. In this paper, we present an online MOT method by introducing a single-object tracking (SOT) based on correlation

The deep learning techniques have achieved great success in the application of human activity recognition (HAR). In this paper, we propose a technique for HAR that utilizes the RGB and skeleton information with the help of a convolutional neural network (Convnet) and long short-term memory (LSTM) as a recurrent neural network (RNN). The proposed method has two parts: first, motion representation images

In dark or poorly lit environments, it is often difficult for the naked eye to distinguish low-light-level images because of low brightness, low contrast and noise, and it is difficult to perform subsequent image processing (such as video surveillance and target detection). To solve these problems, this paper proposes a low-light-level image enhancement algorithm based on deep learning. First, the

Research on image retrieval and classification in the food field has become one of the more and more concerned research topics in the field of multimedia analysis and applications. In recent years, with the rapid development of the Internet industry and multimedia technology, image classification and retrieval technology has become a research hotspot at home and abroad. Traditional keyword-based image

A great number of Internet of things (IoT) applications rely on real-time communication (RTC) mechanisms for transmission of media. Essentially, applications analyze and process media to make decisions that typically affect actuation and control of embedded devices. IoT networks, however, are subjected to constrains that limit the computational and resource complexity of all entities involved. This