Images have become a strategic digital asset that powers creative industries, e–commerce, and data–driven services. However, modern editing tools and large–scale sharing platforms have made copyright infringement, unauthorized redistribution, and covert manipulation easier to perpetrate and harder to detect. These risks lead to financial losses and weaken trust in digital ecosystems, creating an urgent need for technical protections that complement legal remedies. This paper presents a comprehensive survey of technologies and approaches for image copyright protection, with a particular emphasis on digital watermarking, deep learning-based methods, and blockchain-enabled frameworks. We systematically examine the principles, mechanisms, and applications of these techniques, evaluating their strengths, limitations, and potential synergies. In addition, we explore how these technologies can be effectively integrated into practical systems for secure, reliable, and scalable copyright protection of images. Finally, we identify existing challenges and propose promising future research directions to advance the state of the art in image copyright protection.

The performance of the cognitive non-orthogonal multiple access (NOMA) systems with random locations is examined in the present paper. More precisely, the positions of the secondary receivers (SRs) are randomly distributed inside a disk with the centre at the secondary transmitter (ST). The primary networks (PNs), on the other hand, are not necessarily inside the transmission range of the ST. Two user selection schemes namely, path-loss-based and channel-gain-based approaches, are adopted to serve two SRs according to the power domain NOMA technique. Additionally, the impact of the imperfect hardware impairments (HIs) on both transmitters and receivers is also taken into consideration. Furthermore, a recently proposed transmit power allocation at the ST is employed to concurrently maximize the system performance of the secondary networks (SNs) and to strictly protect the quality-of-service (QoS) of the PNs. The correctness of the developed mathematical frameworks is corroborated by simulation results based on the Monte Carlo method. Finally, we unveil that there is no scheme that always outperforms another. Thus, an adaptive scheme can be proposed to optimize the networks' performance. Our findings also reveal that the outage probability (OP) under the harmful effect of HI can be effectively mitigated by increasing the transmit power of the ST. Additionally, increasing the transmit power of the PT is also beneficial for the SNs with the adopted transmit power allocation at the ST.

The performance of energy harvesting (EH)-enabled long-range (LoRa) networks is analyzed in this work. Specifically, we employ deep learning (DL) to estimate the coverage probability (Pcov) of the considered networks. Our study incorporates a general fading distribution, specifically the Nakagami-m distribution, and utilizes tools from stochastic geometry (SG) to model the spatial distributions of all nodes and end-devices (EDs) with EH capability. The DL approach is employed to overcome the limitations of model-based methods that can only evaluate the Pcov under simplified network conditions. Therefore, we propose a deep neural network (DNN) that estimates the Pcov with high accuracy compared to the ground truth values. Additionally, we demonstrate that DL significantly outperforms the Monte Carlo simulation approach in terms of resource consumption, including time and memory.

The present work addresses the performance of long-range (LoRa) networks with non-linear energy harvesting (NLEH) modeling. Specifically, we adopt a NLEH model that captures the practical behaviors of non-linear elements in energy harvesting circuits. Additionally, we employ tools from stochastic geometry (SG) to model the spatial distribution of end-devices (EDs) and power beacons (PBs). The coverage probability (Pcov) is used as the sole metric to measure the network performance. Finally, extensive simulation results are provided to demonstrate the performance of the considered networks. We reveal that increasing the average number of power beacons is beneficial for Pcov, while increasing the average number of end-devices is detrimental to the network performance.

In recent years, single-channel physiological recordings have gained popularity in portable health devices and research settings due to their convenience. However, the presence of electrooculogram (EOG) artifacts can significantly degrade the quality of the recorded data, impacting the accuracy of essential signal features. Consequently, artifact removal from physiological signals is a crucial step in signal processing pipelines. Current techniques often employ Independent Component Analysis (ICA) to efficiently separate signal and artifact sources in multichannel recordings. However, limitations arise when dealing with single or a few channel measurements in minimal instrumentation or portable devices, restricting the utility of ICA. To address this challenge, this paper introduces an innovative artifact removal algorithm utilizing enhanced empirical mode decomposition to extract the intrinsic mode functions (IMFs). Subsequently, the algorithm targets the removal of segments related to EOG by isolating them within these IMFs. The proposed method is compared with existing single-channel EEG artifact removal algorithms, demonstrating superior performance. The findings demonstrate the effectiveness of our approach in isolating artifact components, resulting in a reconstructed signal characterized by a strong correlation and a power spectrum closely resembling the ground-truth EEG signal. This outperforms the existing methods in terms of artifact removal. Additionally, the proposed algorithm exhibits significantly reduced execution time, enabling real-time online analysis.

The average transmit power and coverage probability (Pcov) of uplink energy harvesting-enabled long-range networks are investigated in the present paper. Particularly, we model the end-device (EDs) according to the homogeneous Poisson point process while the power beacon is randomly distributed on the circle in the middle of the network. All EDs rely on the harvested energy to perform their operations and transmissions. Under this context, the upper bound of the average transmit power of an end-device is derived in the closed-form expression. The signal-to-noise-ratio condition of the coverage probability is given in the closed-form expression as well. Simulation results are provided to corroborate the accuracy of the derived mathematical framework as well as to feature the impact of some key parameters on the considered metrics. Our findings unveil that increasing either the number of power beacons or their transmit power will monotonically ameliorate the Pcov. Nevertheless, rising the average number of EDs will significantly decline the Pcov's performance.

This paper studies secure multicast networks utilizing two-way relaying with Fountain codes. In this proposed framework, a node S and a group of nodes exchange Fountain packets through a common relay, in the presence of a passive eavesdropper. Our scheme employs three-phase digital network coding and randomize-and-forward techniques. Each node endeavors to collect sufficient Fountain packets to reconstruct the desired data. This paper assesses the performance of the proposed scheme using metrics such as outage probability, intercept probability, system outage probability, and system intercept probability, over Rayleigh fading channels. The findings reveal a trade-off between the outage performance and the intercept performance. Additionally, system parameters such as the relay's position, and the number of Fountain packet transmission significantly impact the system performance.

This papers investigates security-reliability trade-off for multi-hop secure communication networks using Fountain codes (FCs) and reconfigurable intelligent surface (RIS). In the proposed scheme, a source sends Fountain packets to a destination through an established route, with presence of a passive eavesdropper. The destination and the eavesdropper attempt to collect Fountain packets to recover the source data. To enhance reliability of the data transmission at each hop, the RIS is deployed in the considered network, and the RIS is only used when the direct transmission at each hop is outage. This paper derives closed-form expressions of the end-to-end outage probability (OP) and intercept probability (IP) of the proposed scheme over Rayleigh fading channel. Simulations using Monte Carlo method are realized to verify the derivations. The results show that our scheme outperforms the conventional multi-hop secure communication scheme without using RIS.

In this paper, we propose a two-way relaying scheme using digital network coding in an underlay cognitive radio network. In the proposed scheme, the transmit antenna selection and selection techniques are combined using a primary transmitter and a primary receiver, respectively. In the secondary network, two source nodes that cannot directly communicate attempt to exchange their data with each other. As a result, the relaying technique using partial relay selection is applied to assist the data exchange. Particularly, at the first time slot, the selected secondary relay applies an interference cancellation technique to decode the data received from the secondary sources. Then, the selected relay uses digital network coding to send XOR-ed data to the sources at the second time slot. We first derive the outage probability of the primary network over block the Rayleigh fading channel. Then, the transmit power of the secondary transmitters including the source and relay nodes are calculated to guarantee the quality of service of the primary network. Finally, the exact closed-form formulas of the outage probability of the secondary sources over the block Rayleigh fading channel are derived, and then verified by computer simulations using the Monte Carlo method.

: Recently, predicting the buying behaviour of customers on e-commerce websites is a very critical issue in business management. This could help merchants understand the tendencies of consumers in choosing and buying products. It has become increasingly common these days that predicting buying behaviour on online systems. Although this is a challenging task, it is an exciting and hot topic for researchers. This article intends to be proposed as a predictive model for buying behaviour on online systems. This model may be represented as a two-stage process. First, a sequence database is built from a shopping cart. Second, the prediction will be performed by using the CPT+, which is an improved model of CPT (Compact Prediction Tree)). The main contribution of this paper is that we proposed a solution for predicting buying behaviour in the e-commerce context (a case study of an e-commerce company). The core prediction is mainly based on sequence prediction, in particularly, CPT+ (Compact Prediction Tree).

Discovering unseen patterns from web clickstream is an upcoming research area. One of the meaningful approaches for making predictions is using sequence prediction that is typically the improved compact prediction tree (CPT+). However, to increase this method's effectiveness, combining it with at least other methods is necessary. This work investigates such PageRank-based methods related to sequence prediction as All-K-Markov, DG, Markov 1st, CPT, CPT+. The experimental results proved that the integration of CPT+ and PageRank is the right solution for next page prediction in terms of accuracy, which is more than a standard method of approximately 0.0621%. Still, the size of the newly created sequence database is reduced up to 35%. Furthermore, our proposed solution has an accuracy that is much higher than other ones. It is intriguing for the next phase (testing one) to make the next page prediction in terms of time performance.

In this paper, we consider harvest-to-jam based secure multi-hop cluster multi-input multi-output networks, where a multi-antenna source sends its data to a multi-antenna destination via multi-antenna intermediate cluster heads. The data transmission at each hop is realized by using transmit antenna selection and selection combining techniques, and is overheard by a multi-antenna eavesdropper using selection combining. In addition, joint antenna and jammer selection methods are performed at each hop to reduce quality of the eavesdropping channels. The cluster members can harvest wireless energy from the previous cluster head, and use the harvested energy for emitting jamming noises on the eavesdropper. We propose three cooperativejamming algorithms, named best antenna and best jammer selection (BA-BJ), random antenna and all jammer selection (RA-AJ) and all antenna and all jammer selection (AA-AJ). Then, end-to-end outage probability and intercept probability of the proposed algorithms are evaluated via both simulation and analysis, under impact of hardware impairments, over Rayleigh fading channel.

Privacy-preserving for data is one of the crucial responsibilities of service providers. For the web hosting service, customers’ information and source codes of websites are considered sensitive data that need to protect. The solution decentralized for web hosting is a new technology trend, provides an effective mechanism for storing and accessing websites. This paper addresses some problems related to privacy concerns of the decentralized web hosting service. Based on the blockchain technology, cryptography, and interplanetary file system (IPFS) platform, we propose a protocol for web hosting that provides three features. The first one ensures anonymity for information of customers. The second feature provides confidentiality and authentication for transmitting source codes of websites between customers and the service provider (SP). And the last one is responsible for securing the source code of websites from other nodes on the public IPFS network. The experiments demonstrate that the proposed solution efficiently protects privacy for the decentralized web hosting service.

In this article, we provide a novel model to address the issue of webpage access prediction. In particular, the main approach we propose aims to reduce execution time by reducing the sequence space. This solution combines calculation of PageRank values of sequences in sequence databases and analysis of sequences from these shortened sequence databases. To evaluate the solution, we chose K-fold validation with K = 10 by randomizing the dataset 10 times; then the system calculated the average PageRank values of sequences. Next, with acceptable accuracy (when the size of datasets was reduced by up to 30% by PageRank calculation), we performed next access page prediction by analysing 1000 sequences. Experimental results for the real FIFA dataset show that our new proposed approach is much better than previous approaches in terms of prediction execution time.

In this paper, we propose and evaluate the performance of fountain codes (FCs) based secure transmission protocols in multiple-input-multiple-output (MIMO) wireless systems, in presence of a passive eavesdropper. In the proposed protocols, a source selects its best antenna to transmit fountain encoded packets to a destination that employs selection combining (SC) or maximal ratio combing (MRC) to enhance reliability of the decoding. The transmission is terminated when the destination has a required number of the encoded packets to reconstruct the original data of the source. Similarly, the eavesdropper also has the ability to recover the source data if it can intercept a sufficient number of the encoded packets. To reduce the number of time slots used, the source can employ non-orthogonal multiple access (NOMA) to send two encoded packets to the destination at each time slot. For performance analysis, exact formulas of average number of time slots (TS) and intercept probability (IP) over Rayleigh fading channel are derived and then verified by Monte-Carlo simulations. The results presented that the protocol using NOMA not only reduces TS but also obtains lower IP at medium and high transmit signal-to-noise ratios (SNRs), as compared with the corresponding protocol without using NOMA.

The emergence of the Internet of Things (IoT) and the advantages of computer network have attracted the attention of technological experts. However, network security issues remain a challenge. Controlling Web traffic and preventing attack to Web server, especially DoS/DDoS, are tremendous in the current computing ecosystem. In this paper, we propose a solution for controlling connections of the inside as well as the outside of network systems by using an integrated hardware, which would be deployed at gateway of the networks. It is not only used to implement rules but also monitor the network traffic, especially for web traffic which is considered as the common type in the Internet. In our solution, we prefer to use the IP SLA to control the web traffic, rather proxy to set up rules as well as a data stream algorithm for fast detecting Hot-IPs to prevent attacks from outside networks to the inside servers. The proposed solution is trouble-free to use and enhance security in small & medium networks as well.

Aim/Purpose: In this article, we provide a better solution to Webpage access prediction. In particularly, our core proposed approach is to increase accuracy and efficiency by reducing the sequence space with integration of PageRank into CPT+. Background: The problem of predicting the next page on a web site has become significant because of the non-stop growth of Internet in terms of the volume of contents and the mass of users. The webpage prediction is complex because we should consider multiple kinds of information such as the webpage name, the contents of the webpage, the user profile, the time between webpage visits, differences among users, and the time spent on a page or on each part of the page. Therefore, webpage access prediction draws substantial effort of the web mining research community in order to obtain valuable information and improve user experience as well. Methodology: CPT+ is a complex prediction algorithm that dramatically offers more accurate predictions than other state-of-the-art models. The integration of the importance of every particular page on a website (i.e., the PageRank) regarding to its associations with other pages into CPT+ model can improve the performance of the existing model. Contribution: In this paper, we propose an approach to reduce prediction space while improving accuracy through combining CPT+ and PageRank algorithms. Experimental results on several real datasets indicate the space reduced by up to between 15% and 30%. As a result, the run-time is quicker. Furthermore, the prediction accuracy is improved. It is convenient that researchers go on using CPT+ to predict Webpage access. Findings: Our experimental results indicate that PageRank algorithm is a good solution to improve CPT+ prediction. An amount of though approximately 15 % to 30% of redundant data is removed from datasets while improving the accuracy. Recommendations for Practitioners: The result of the article could be used in developing relevant applications such as Webpage and product recommendation systems. Recommendation for Researchers: The paper provides a prediction model that integrates CPT+ and PageRank algorithms to tackle the problem of complexity and accuracy. The model has been experimented against several real datasets in order to show its performance. Impact on Society: Given an improving model to predict Webpage access using in several fields such as e-learning, product recommendation, link prediction, and user behavior prediction, the society can enjoy a better experience and more efficient environment while surfing the Web. Future Research: We intend to further improve the accuracy of webpage access prediction by using the combination of CPT+ and other algorithms.

This paper proposes a decentralized solution for web hosting based on interplanetary file system (IPFS) and Ethereum blockchain. Particularly, we use Ethereum smart contracts to manage the IPFS network and the web hosting service. IPFS platform is used to store data and to host websites. All storage miner nodes on the IPFS network offer the pinning service to ensure that source codes of the websites and users’ data are retained long-term. Moreover, these nodes also enable the interplanetary name space (IPNS) service for creating and updating mutable links to IPFS contents. TXT record is also used in the domain name system (DNS) to map domain names to IPNS addresses for hosted websites. For privacy-preserving data storage, websites need to be deployed an encryption algorithm. The proposed model that combines between the IPFS and blockchain networks to form a platform providing the decentralized web hosting service. Experiment illustrates building and hosting a web application on the IPFS network. Experimental results show that, compared to the traditional web hosting model, the hosted web application on the proposed platform ensures the confidentiality, integrity, and availability.

Blockchain technology offers great benefits to the development of information technology. However, in order to use blockchain effectively, it is necessary to consider security and privacy aspects. This paper presents a survey of different security and privacy aspects of blockchain. In particular, we present the common types of security attacks on blockchain and security enhancement solutions. We also focus on the existing solutions to protect privacy for blockchain, and on the directions for future research using group signature and zeroknowledge schemes. Group signature scheme can be used to validate transactions in a blockchain network, and zero-knowledge to guarantee that transactions are valid despite the fact that the detailed information of transactions remains obscure. The combination of group signature and zeroknowledge could be feasible solutions for improving security and privacy for blockchain. This solution could also be deployed for IoT networks.

Sequential data mining is one of important topics in data mining. One of its important application is to predict a next element in a data sequence or to discover sequential rules. Many algorithms were published to tackle these problems such as sequential rule mining and sequence prediction. This paper aims to present an approach to build a sequence database from a Web log data. This step accounts for first phase of sequential data mining, titled data pre-processing phase, which transforms sequential data in Web log into sequential relational database. This sequential relational database is used as the input data of sequential data mining and sequence prediction. Besides, we present a parallel algorithm to efficiently build sequential database from Weblog files in order to predict Webpage access.