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More than 50 million journal papers will have been published by the end of 2019 with 2 million more journal papers published every year. The number of conference papers is even higher, and millions of other types of scientific research are added to the knowledge base every year. Scientific databases such as Web of Science, Scopus, and PubMed index millions of scientific papers and Google Scholar indexes a huge amount of scientific knowledge across diverse domains. However, current systems provide long lists of results when users attempt to find relevant papers, leaving them with little choice other than manually skimming through the lists. This article surveys different techniques used to identify relevant research papers by knowledge-based organizations. We categorized current literature content as content, metadata, collaborative filtering, and citation based techniques and identified the strengths and limitation for each approach. Further, we evaluated the published techniques and research-based products used to identify relevant documents and identified the strengths and limitations of each approach. This research will greatly help to understand current state-of-the-art techniques internal workings for finding relevant papers, understand the relevant strengths and limitations, and explore previously proposed techniques targeting this area. [ABSTRACT FROM AUTHOR]

Preserving customers' expectations and understanding factors affecting their purchasing decisions are crucial in designing effective marketing and advertising strategies. However, constantly and swiftly changing the customers' interests and consumption behaviors make it inevitable to utilize sophisticated tools and approaches based on advanced technologies. Among them, by measuring the customers' physiological and neural signals, studying the customers' cognitive and affective responses to marketing stimuli, neuromarketing provides deep insight into the customers' motivations, preferences, and decisions. Recently, the internet of everything (IoE) has brought many new opportunities to the industry and has attracted the attention of many researchers in recent years. The main objective of this paper is to address how the IoE would empower neuromarketing techniques. Hence, an in-depth understanding of current research issues as well as emerging trends would help meet this goal. In this paper, a comprehensive review has been done by reviewing numerous journal and conference papers from various academic databases (sciencedirect.com, IEEE Xplore, Springer, Elsevier, Wiley, ACM digital library) based on applying various filtrations on specific keywords to identify and categorize the IoE devices in the neuromarketing field. In particular, we discussed the importance and the applications of IoE gadgets and devices, especially wearable medical technologies in eight neuromarketing techniques. Finally, we described critical existing challenges and limitations in neuromarketing and IoE gadgets that researchers have dealt with in this field of research. [ABSTRACT FROM AUTHOR]

Deep learning has achieved remarkable success in a wide range of domains. However, it has not been comprehensively evaluated as a solution for the task of Chinese biomedical named entity recognition (Bio-NER). The traditional deep-learning approach for the Bio-NER task is usually based on the structure of recurrent neural networks (RNN) and only takes word embeddings into consideration, ignoring the value of character-level embeddings to encode the morphological and shape information. We propose an RNN-based approach, WCP-RNN, for the Chinese Bio-NER problem. Our method combines word embeddings and character embeddings to capture orthographic and lexicosemantic features. In addition, POS tags are involved as a priori word information to improve the final performance. The experimental results show our proposed approach outperforms the baseline method; the highest F-scores for subject and lesion detection tasks reach 90.36 and 90.48% with an increase of 3.10 and 2.60% compared with the baseline methods, respectively. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of guiding a flexible needle from its entry point to a target location through an obstacle-laden environment. To achieve this, we propose a path planning approach grounded in an enhanced particle swarm optimization algorithm (PSO). Our method uses the number and angle of needle rotations to govern the flexible needle's movement. By optimizing for parameters such as path length, distance from obstacles, and needle rotation, we transform the flexible needle puncture path planning problem into a multi-objective optimization task. The optimal path is determined by assigning distinct weights to these four optimization objectives and solving them using the improved PSO algorithm. To demonstrate the effectiveness and practicality of our approach, we conduct MATLAB simulations and compare the results with the unimproved PSO algorithm and the rapidly-exploring random trees algorithm. [ABSTRACT FROM AUTHOR]

This paper mainly studies the retrieval and location of violence in long time sequence video. Aiming at the low accuracy of violence detection in long time sequence video, a two-stage violence time sequence location method based on DC3D network model is proposed in this paper. In the video preprocessing stage, this paper adopts the method of generating video at multiple scales. In the first stage, we first use a large number of labeled datasets for pre-training to obtain a C3D network model for generating candidate videos and finally filter out meaningless background videos. In the second stage, we use the deconvolution method to identify the candidate video accurately to the frame level, so as to determine the specific time of violence. In the first stage, this method can improve the overall accuracy by generating candidate videos. DC3D network model can accurately locate the time of violence to the level of frame. The experimental results show that the proposed method can quickly retrieve and locate the violent fighting behavior in the surveillance video under the long time sequence video. The research results of this paper can provide convenience for the surveillance personnel to quickly retrieve and locate the target segment in a large amount of video data. [ABSTRACT FROM AUTHOR]

This paper proposes a smart machine-learning-based unmanned aerial vehicle (UAV) control system that optimizes UAV operations in real time. The purpose of this system is to increase the efficiency of UAVs that need to operate with limited resources. This can be accomplished by allowing the UAVs in flight to identify their current state and respond appropriately. The proposed system, which is developed based on "cloud robotics," benefits from the powerful computational capabilities of cloud computing and can therefore calculate many types of information received from various sensors in real time to maximize the performance of the UAV control system. The system learn about normal situations when creating models. That is, preprocessing data that is correlated with a particular situation and modeling it with a "multivariate Gaussian distribution." Once the model is created, the UAV can be used to analyze the current situation in real time during flight. Of course, it is possible to recognize the situation based on the traditional RULE or the latest LSTM. However, this is not an appropriate solution for UAV situations where irregularities are severe and unpredictable. In this paper, we succeeded in recognizing the UAV flight status in real time by the proposed method and succeeded in optimizing it by adjusting communication cycle based on a recognized situation. Based on the results of this study, we expect to be able to stabilize and optimize systems that are highly irregular and unpredictable. In other words, this system will be extended to learn about various situations and create a model. A reliable and efficient smart system can be designed by judging the situation comprehensively. [ABSTRACT FROM AUTHOR]

Until now, there has been little research on digital forensics in the IoT (Internet of Things)-based infrastructure. Current digital forensic tools, investigation frameworks, and processes cannot meet the heterogeneity and distribution characteristics of the IoT environment. These characteristics are a challenge for digital forensic investigators and law enforcement agencies. To solve these problems, this paper proposes a digital forensics framework for the IoT environment based on the blockchain technology. In the proposed framework, all communications of IoT devices are stored in the blockchain as transactions, thus making the existing chain of custody process easier and more powerful. By using the blockchain technology, the integrity of the data to be analyzed is ensured and security is strengthened, and the preservation of integrity is made more reliable by a decentralized method of integrity preservation. In addition, since the public distributed ledger is provided, participants in the forensic investigation—such as device users, manufacturers, investigators, and service providers—can confirm the investigation process transparently. We simulated the proposed model to support the proof of concept. [ABSTRACT FROM AUTHOR]

In order to explore the application of neural networks in the intelligent protection of digital music copyrights, this paper initially investigates the key aspects of digital music-related supply chain management (SCM). Subsequently, this paper introduces a fuzzy, comprehensive evaluation-based method for authenticating digital music infringement. Furthermore, it employs a convolutional neural network to analyze digital music's source components. Within this context, an attention mechanism and an adaptive gate model are meticulously designed. Moreover, a selective adaptive cascade is implemented to optimize the structure of the multi-resolution coder–decoder. Through fuzzy authentication, it becomes feasible to accurately and reliably track and verify copyright information pertaining to digital music, thus providing an effective protective mechanism for the digital music industry. Experimental results reveal that in comparison with the SHN-4 series models, the network model embedded with the channel space (CS) attention module achieves improvements of up to 0.04 dB in terms of performance metrics for human voice and accompaniment sources. Furthermore, it outperforms these models by up to 0.19 dB in music source separation metrics. The incorporation of the adaptive attention mechanism profoundly enhances the model's performance in audio source separation tasks, benefiting both vocal and accompaniment sources. This improvement is evident in the form of a 0.44 SDR enhancement for vocal sources and a 0.18 SDR enhancement for accompaniment sources. Additionally, the proposed two-stage music separation-gate self-attention model surpasses the two-stage music separation-gate model in metrics associated with human voice and accompaniment sources. This outcome convincingly underscores the enhanced effectiveness achieved by integrating CS, self-attention, and adaptive connection in network performance, as well as the adaptive gate structure for music copyright protection. This approach heightens the model's autonomous selectivity, minimizes outliers, stabilizes waveform characteristics, and significantly contributes to the safeguarding of digital content. [ABSTRACT FROM AUTHOR]

The number of research articles published on COVID-19 has dramatically increased since the outbreak of the pandemic in November 2019. This absurd rate of productivity in research articles leads to information overload. It has increasingly become urgent for researchers and medical associations to stay up to date on the latest COVID-19 studies. To address information overload in COVID-19 scientific literature, the study presents a novel hybrid model named CovSumm, an unsupervised graph-based hybrid approach for single-document summarization, that is evaluated on the CORD-19 dataset. We have tested the proposed methodology on the scientific papers in the database dated from January 1, 2021 to December 31, 2021, consisting of 840 documents in total. The proposed text summarization is a hybrid of two distinctive extractive approaches (1) GenCompareSum (transformer-based approach) and (2) TextRank (graph-based approach). The sum of scores generated by both methods is used to rank the sentences for generating the summary. On the CORD-19, the recall-oriented understudy for gisting evaluation (ROUGE) score metric is used to compare the performance of the CovSumm model with various state-of-the-art techniques. The proposed method achieved the highest scores of ROUGE-1: 40.14%, ROUGE-2: 13.25%, and ROUGE-L: 36.32%. The proposed hybrid approach shows improved performance on the CORD-19 dataset when compared to existing unsupervised text summarization methods. [ABSTRACT FROM AUTHOR]

Arithmetic logic unit (ALU), a core component of a processor, is one of the thrust areas of the current research. Presently, ALU is designed by transistor-based CMOS technique and its individual components are placed in different layers. The current design is affected by the limitations of Moore's law and design complexity. At present, 'Quantum cellular automata electro-spin (QCA-ES)' technology is widely accepted technology as an alternative of 'CMOS' to minimize the above discussed problems. In this research paper, the design of a novel multilayer portable, dynamic, fault-tolerant, power-efficient, thermally stable reversible ALU is proposed which is explored through QCA-ES. All the arithmetic and logical components of ALU are separately placed in different layers. Area density, delay, fault tolerance and thermal stability are investigated. A specific type of gate, known as reversible gate (modified 3:3 'TSG' gate), is used in this proposed design with QCA technology to get the optimized design ALU with low occupied area, complexity, delay and power dissipation. Investigation of a fault-free design and saturated amplitude level (of output) change with respect to temperature increment in the proposed device are also discussed in this paper. Not only the thermal stability (up to 6 k temperature) but also an investigation on cell complexity of the 100% fault-free (against multiple cell-omission, cell-displacement, cell-orientation change and extra cell deposition), multilayer nano-device is represented in this work. 'QCA-Designer' software is used in this research work to design and develop layout of the proposed components in quantum-field and find out the occupied area, delay and complexity of proposed design. 'QCA-Pro' software is used for getting the value of dissipated power. [ABSTRACT FROM AUTHOR]

The aim of this special issue is to introduce the burgeoning topic of Autonomous System. Finally, in order to check the conformity of this enforcement with defined multi-cloud SLA, the authors propose an approach for multi-cloud SLA reporting inspired by conformance checking techniques. I In i I the i I paper i I authored i I by i I Jeremy i I Mechouche i , the authors propose a hierarchical representation of multi-cloud SLAs: sub-SLAs associated with a system's components deployed on distinct cloud service providers and global-SLA associated with the whole system. [Extracted from the article]

In industry, robots are widely used to solve repetitive or dangerous actions in product production, so that product production can be more efficient. However, the problem that robots are often challenged is the convenience and the efficiency of introducing the production line. Therefore, the intuitive robot guidance method is an important issue; this paper will introduce the concept of human–robot interactions (HRI) and use deep learning methods on the machine vision system to complete the robot-guided assembly operation analysis, and the assembly operation analysis requires semantic segmentation as pre-processing. Therefore, we propose a novel semantic template correlation model architecture based on zero-shot learning (ZSL) to achieve the effect of rapid deployment. The semantic template correlation model is to search for the object area offline learning through the semantic template generated by the physics engine, and when inferring online, we can directly enter the semantic template to obtain the relevant object region. Finally, this paper verifies that the MIoU can be increased by more than 5% through the verification of the general database VOC2012. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are mostly used for monitoring the environment; however, they are usually powered by non-rechargeable batteries with limited energy. Solar energy harvesting is an attractive solution to the limit by charging the sensor nodes; however, the harvested solar energy is easily affected by weather conditions. Based on the characteristics of uncertainty and intermittency of solar energy, this paper proposes a plus-profile solar energy prediction algorithm. This algorithm makes the prediction of future available solar energy by finding the data in the dataset that is most similar to the data of the day and combining it with recent weather trend. According to the predicted result, the paper further proposes an adaptive energy management scheme to suit the harvested energy. In the scheme, sensor nodes can adaptively adjust task scheduling to achieve energy neutrality. The simulation results show that compared with other algorithms, the prediction accuracy of the proposed prediction algorithm is improved by 17.7 and 22.4%, respectively, and the proposed energy management scheme reduced energy loss by 6.2 and 46.8%, respectively. [ABSTRACT FROM AUTHOR]

In order to detect the oil leakage problem of transformers in time and avoid the abnormal operation of power system caused by transformer oil leakage, this paper proposed a transformer oil leakage detection method based on improved YOLOv5. Firstly, by adding 4 × sampling layer to the FPN structure, the feature information was fused across layers to improve the detection accuracy of the model. Secondly, the Wise-IoU (WIoU) bounding loss function with dynamic non-monotony focusing mechanism was introduced to accelerate the training and reasoning of the network, and the overall performance of the model was further improved by balancing the learning of low and high-quality samples. Finally, the model was improved from Anchor-based to Anchor-free, which greatly reduced the number of parameters and calculation amount of the model, and had better performance than the original model. Used the self-built outdoor transformer oil leakage data set for training and testing, the results showed that compared with the original model, the improved network precision was increased by 7.3%, the recall was increased by 5.0%, the mAP0.5 was increased by 6.4%, the number of parameters was decreased by 28.2%, and the reasoning speed increased by 51.7%, which was beneficial to engineering deployment. [ABSTRACT FROM AUTHOR]

While traveling, people need to utilize smart tourism service systems based on the Internet of Things (IoT) to enhance their journeys' convenience. However, existing IoT-based smart tourism service systems still possess certain shortcomings. In light of this, this paper delves into the integration of IoT and machine learning (ML) technologies to improve the design of smart tourism service systems and establish a platform for intelligent tourism services. Simulated datasets are employed to evaluate the performance and functionality of the smart tourism service system. The primary contributions, as inferred from the test results, are as follows: In area 1, the model achieves a congestion recognition rate of 92.6% with an error rate of only 1.5% in congestion judgment. In area 2, congestion is accurately identified 93.5% of the time, with a misjudgment rate of just 1.2%. In area 3, the model achieves a congestion identification rate of 94.2% along with a 1.3% misjudgment rate, effectively identifying congestion situations in scenic spots. When comparing the research and design system with the system based on the support vector machine (SVM) algorithm, the results demonstrate that the smart tourism service system designed in this paper, leveraging IoT technology and ML algorithms, achieves higher prediction accuracy than the SVM-based system. These findings can serve as a valuable guide for designing future smart tourism service systems. [ABSTRACT FROM AUTHOR]

In the stochastic and dynamic edge-cloud collaborative environment, the computing resources of the host are limited, and the resource requirements of computing tasks are random and changeable. Therefore, how to efficiently schedule dynamic tasks and improve system performance becomes challenging. The scheduling algorithm based on deep reinforcement learning optimizes the delay and energy consumption of the system by dynamically interacting with the environment, which can solve the problem of dynamic and changeable environment to a certain extent, but there are still problems such as poor model adaptability, low training efficiency, and unbalanced system load. In this paper, aiming at optimizing the average response time of task scheduling and the average energy consumption of the system, a multi-objective task scheduling model is designed, and a task scheduling policy optimization algorithm based on improved asynchronous advantage actor-critic (A3C) is proposed. The residual convolutional neural network (RCNN) improves the network structure of A3C, using asynchronous multi-threaded training methods to interact with the edge-cloud collaborative environment and capturing the random dynamic characteristics of resources required for computing tasks and heterogeneous edge-cloud hosts resource change characteristics to better adapt to random dynamic environments, and it can quickly updating network parameters, improving training speed, and make full use of host resources to solve system load imbalance problems. Simulation results show that the scheduling algorithm proposed in this paper can effectively reduce task response time and system energy consumption in an edge-cloud collaborative environment. [ABSTRACT FROM AUTHOR]

Existing biohashing authentication schemes usually store bioinformation in the cloud, whose storage method can face the problems of bioinformation leakage and tampering, and single point of failure. To address the above problems, a ciphertext speech biohashing authentication scheme based on chaotic system and improved public chain is proposed. First, the fusion features are extracted and generated with the inner product of 3D chaotic system to generate the biohashing sequence. Second, the original speech is encrypted and uploaded to the InterPlanetary file system and then, uploaded to the blockchain after the data are encrypted. Finally, the server matches the hash sequence queried on the chain with the to-be-authenticated hash at the user's end using the Hamming distance. The experimental results show that the biohashing algorithm in this paper has better distinguishability, robustness, security and time efficiency. The key space of the speech encryption algorithm is 2 256 , which can well resist the exhaustive attack and ensure the security during speech authentication. In this paper, the combination of on-chain and off-chain storage of speech information can prevent the problem of bioinformation leakage and tampering, as well as avoiding the problem of single point of failure. [ABSTRACT FROM AUTHOR]

Bioinformatics allows and encourages the application of many different parallel computing approaches. This special issue brings together high-quality state-of-the-art contributions about parallel computing in bioinformatics, from different points of view or perspectives, that is, from high-performance, heterogeneous, and cloud computing. The special issue collects considerably extended and improved versions of the best papers, accepted and presented in PBio 2018 (6th International Workshop on Parallelism in Bioinformatics, and part of EuroMPI 2018). The domains and topics covered in these five papers are timely and important, and the authors have done an excellent job of presenting the material. [ABSTRACT FROM AUTHOR]

Rapid development in Information and Communications Technology (ICT) and its related techniques have caused tremendous impacts to our daily lives. One of the most representative instances can be the applications on Big Data and Internet of Things (IoT). In the onset of Big Data and IoT era, more reliable data can be acquired for reducing the level of uncertainty benefiting the decision-making quality. The paper [[2]] has made a very specific contribution in proposing a novel Internet of Things (IoT) mechanism, including unmanned aerial vehicle (UAV) equipped with sensors as well as the wireless network, for improving the firefighting effectiveness with less risk by collecting the critical information from the fire scene. [Extracted from the article]

The Sunway TaihuLight is the first supercomputer built entirely with domestic processors in China. On Sunway Taihulight, the local data memory (LDM) of the slave core is limited, so data transmission with the main memory is frequent during calculation, and the memory access efficiency is low. On the other hand, for many scientific computing programs, how to solve the storage problem of irregular access data is the key to program optimization. Software cache (SWC) is one of the effective means to solve these problems. Based on the characteristics of Sunway TaihuLight structure and irregular access, this paper designs and implements a new software cache structure by using part of the space in LDM to simulate the cache function, which uses new cache address mapping and conflicts solution to solve high data access overhead and storage overhead in a traditional cache. At the same time, the SWC uses the register communication between the slave cores to share on the different slave core LDMs, increasing the capacity of the software cache and improving the hit rate. In addition, we adopt a double buffer strategy to access regular data in batches, which hides the communication overhead between the slave core and the main memory. The test results on the Sunway TaihuLight platform show that the software cache structure in this paper can effectively reduce the program running time, improve the software cache hit rate, and achieve a better optimization effect. [ABSTRACT FROM AUTHOR]

A. Kireeva et al. contributed another paper in the field of cellular automata: "Parallel simulation of drift-diffusion-recombination by cellular automata and global random walk algorithm". Parallel computing technologies enable the solution of large-scale numerical simulation and data processing problems in science and industry. The previously proposed contention-aware scheduling algorithm is formally described with this model, modifications of the algorithm are proposed: look-ahead technique, chunked data transfers, data caching, and peer-to-peer data transfers. [Extracted from the article]

Intelligent Transportation Systems (ITS) are a promising research area that offers a variety of applications. The objective of these applications is to enhance road safety, to optimize traffic efficiency, and to provide a better driving experience. Yet, the efficiency of ITS applications, such as safety and driver assistance applications, relies essentially on the exchanged data between different entities of the network. Accordingly, trust management models are used to guarantee the quality of the data and to eliminate malicious and selfish nodes to secure vehicular communications. In this paper, we pay a special attention to the requirements of trust management models used in the context of ITS applications. We also dissected the trust model to extract the mechanisms used in the literature to fulfil the identified requirements. Furthermore, we present the most known simulators and evaluation metrics that are used to validate the proposed models. The aim of this study is to provide a global overview of the mechanisms that may be used to fulfil the crucial requirements of trust management models. For this purpose, we employed a systematic mapping study, through which we carefully analysed 60 selected articles. Through our analysis, five main requirements were identified: scalability, accuracy, robustness, privacy preservation, appropriate response time. Different mechanisms and techniques were applied to meet with the identified requirements. Two main findings are reported: (1) The accuracy and robustness requirements are the most considered requirements. On the other hand, the privacy requirement is the least covered by the publications, (2) the majority of the reviewed papers focus on addressing two or three requirements at most. A little number of publications covered all the requirements. Based on the identified research gaps, we highlight some future directions that may be investigated. We provide general recommendations that may serve as a guideline for researchers who want to design trust models that fulfil certain requirements. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an important virtual network that allows remote users to access linked multimedia devices. The development of IoT and its ubiquitous application across various domains of everyday life has led to continuous research efforts. Security is a perceptual concern for researchers involved in IoT as it is a key factor in the acceptance of any innovative technology. Numerous research studies have been conducted concentrating on the level of IoT security on a particular mechanism, on specific applications, or on categorizing vulnerabilities, in order to address a defined situation of securing an IoT network. This present paper aims to comprehensively review potential solutions for securing IoT, between emerging and traditional mechanisms, such as blockchain, machine learning, cryptography, and quantum computing. This study provides a comparative analysis of related papers with their characteristics, pros and cons. Accordingly, it taxonomizes relevant solutions based on their achieved security requirements. Furthermore, the potential benefits and challenges of each of the four mechanisms are discussed. [ABSTRACT FROM AUTHOR]

To improve the quality and efficiency of machine translation (MT) for low-resource languages (LRLs), this paper analyzes the application of genetic algorithm (GA) under the background of cloud computing (CC). By using GA to optimize MT for LRL, this study aims to address the limitations and challenges of traditional methods on LRL. Firstly, GA under the background of the CC platform is analyzed in detail. The CC platform offers powerful computing and storage capabilities that provide the basis for large-scale optimization using GA. GA is an optimization algorithm based on the principle of biological evolution, which seeks the optimal solution by simulating genes' genetic and evolutionary processes. In MT of LRL, GA can be used to optimize model parameters, select translation rules, and improve translation quality, thereby improving translation results. Secondly, the present situation of MT for LRL is described. Finally, the traditional LRL-oriented MT and the GA-optimized LRL-oriented MT are compared and analyzed. The results reveal that the traditional LRL-oriented MT has low accuracy and low customer satisfaction (CS). Considering the translation time, accuracy, and resource utilization rate (RUR), the highest rate is only about 47%. In contrast, GA-optimized LRL-oriented MT significantly improves translation accuracy and CS, reaching approximately 94%. The final evaluation results indicate that the accuracy of the model is usually above 70%, and up to 94%. Therefore, GA plays an important role in optimization for LRL-oriented MT. This paper provides technical support for optimizing LRL-oriented MT and contributes to the further development of MT technology. By applying GA, the limitations of traditional methods in LRL can be overcome and the quality and efficiency of translation can be improved. It is of great significance to improve the development of translation technology of LRL, enhance cross-language communication, and promote global cultural exchange. [ABSTRACT FROM AUTHOR]

Location-based skyline queries in road networks can quickly return the desired data to the user from a large amount of data according to the user's needs and location. However, there is no effective mechanism for calculating the road network distance and judging the dominant relationship between data points in the existing skyline queries algorithms in road networks. In this paper, we propose improved dynamic skyline queries in road networks, which can quickly compute all skyline points in road networks. Firstly, this paper proposes a GD-tree index, which efficiently stores the location information of the target tuple in the road network and speeds up the calculation of the road network distance. In addition, the concept of the optimal scan end point is proposed to reduce the calculation cost. The histogram of frequency statistical is proposed for the non-spatial dimension, and the nearest and farthest distance is calculated for the spatial dimension to select the optimal scan end point. Finally, extensive experiments in real road network datasets demonstrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The metaverse is seen as a future generation of the Internet in which the virtual and the real merge into a common world. Technologies such as IoT, cloud computing, artificial intelligence or the reality–virtuality continuum underpin the metaverse and condition its evolution. This paper presents a bibliometric study on the WoS database (from 1995 to 2022) to obtain a comprehensive and non-subjective understanding of the metaverse. The study identifies the main subject areas and sources, the leading countries and authors. It also analyzes the evolution over time and the core and future research themes. Extended reality, blockchain, artificial intelligence and sensors are identified as core themes, while building information modeling, digital twins and governance emerge as future themes. Based on the bibliometric study, a general layered metaverse architecture is proposed that streamlines open challenges in the metaverse to assist researchers and companies in introducing innovative and disruptive improvements. [ABSTRACT FROM AUTHOR]

Collecting and analyzing data from all devices to improve the efficiency of business processes is an important task of Industrial Internet of Things (IIoT). In the age of data explosion, extensive text data generated by the IIoT have given birth to a variety of text representation methods. The task of text representation is to convert the natural language to a form that computer can understand with retaining the original semantics. However, these methods are difficult to effectively extract the semantic features among words and distinguish polysemy in natural language. Combining the advantages of convolutional neural network (CNN) and variational autoencoder (VAE), this paper proposes an intelligent CNN-VAE text representation algorithm as an advanced learning method for social big data within next-generation IIoT, which help users identify the information collected by sensors and perform further processing. This method employs the convolution layer to capture the local features of the context and uses the variational technique to reconstruct feature space to make it conform to the normal distribution. In addition, the improved word2vec model based on topical word embedding (TWE) is utilized to add topical information to word vectors to distinguish polysemy. This paper takes the social big data as an example to illustrate the way of the proposed algorithm applied in the next-generation IIoT and utilizes Cnews dataset to verify the performance of proposed method with four evaluating metrics (i.e., recall, accuracy, precision, and F1-score). Experimental results indicate that the proposed method outperforms word2vec-avg and CNN-AE in K-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM) classifiers and distinguishes polysemy effectively. [ABSTRACT FROM AUTHOR]

Whether it is an NLP (natural language processing) task or an NLU (natural language understanding) task, many methods are model oriented, ignoring the importance of data features. Such models did not perform well for many tasks based on feature loose, unbalanced tricky data including text classification tasks. In this regard, this paper proposes a classification method called LSTM-SN (long-short term memory RNN fusion social network) based on extremely complex datasets. The approach condenses the characteristics of the dataset. LSTM combines with social network methods derived from specific datasets to complete the classification task, and then use complex network structure evolution methods to discover dynamic social attributes. The experimental results show that this method can overcome the shortcomings of traditional methods and achieve better classification results. Finally, a method to calculate the accuracy of fusion model is proposed. The research ideas of this paper have far-reaching significance in the domain of social data analysis and relation extraction. [ABSTRACT FROM AUTHOR]

Traditional deep learning models prefer large data sets, and in reality small data sets are easier to obtain. It is more practical to build models suitable for small data sets. Based on CNN, this paper proposes the concept of union convolution to build a deep learning model Union-net that is suitable for small data sets. The Union-net has small model size and superior performance. In this paper, the model is tested based on multiple commonly used data sets. The experimental results show that Union-net outperforms most models when dealing with small datasets, and Union-net outperforms other models when dealing with complex classification tasks or dealing with few-shot datasets. The codes for this paper have been uploaded to https://github.com/yeaso/union-net. [ABSTRACT FROM AUTHOR]

On the basis of Wyner–Ziv video coding in transform domain, this paper studies the coding method of key frame and proposes a method of coding mode selection based on partition of frequency band: First of all, the key frames are transformed by DCT and they are divided into low-frequency band and high-frequency band according to the characteristics of frequency band. Then the low-frequency band and high-frequency band are encoded and decoded by Wyner–Ziv and intraframe, respectively. In this paper, the decision of coding mode is transferred to the decoder to be completed, which further improves the performance of the system without increasing the complexity of the encoder. Compared with the reference algorithm, the peak signal-to-noise ratio obtained by the proposed coding mode selection algorithm based on partition of frequency band is improved by 1–5 dB. [ABSTRACT FROM AUTHOR]

In the multi-domain network scenario, in order to improve the survivability of service function chain (SFC) in the face of network failure, most methods solve this problem through virtual network function (VNF) backup mechanism. However, the traditional multi-domain SFC deployment method lacks a SFC partition mechanism for backup resource consumption and does not consider the isolation and privacy requirements between different network domains. In view of the above problems, this paper proposes a reliability partition scheme based on reinforcement learning in SFC partition stage, which can ensure that VNF is backed up while maintaining good load balancing and low inter-domain transmission delay, and improve the reliability of SFC. Then, this paper proposes a VNF backup mechanism with minimum resource fluctuation in the VNF mapping stage and uses the integer linear programming (ILP) model to determine the backup scheme of each VNF, so as to ensure the minimum fluctuation of resource occupancy of the entire network. Finally, this paper proposes a multi-domain SFC deployment and backup algorithm based on Federated learning (FA-MSDB). The experimental results indicate that FA-MSDB can effectively improve the survival rate of SFC, reduce the overall transmission delay, and ensure good inter-domain and intra-domain load balance. [ABSTRACT FROM AUTHOR]

Demeanor evidence refers to the authoritative opinions of experts using analytical instruments to collect and interpret the formation of external physiological symptoms of the entire human body. With the development of modern technology, the observation of demeanor has entered a new stage of multimodal quantification. The quantifiable results of demeanor observation with the help of scientific and technological instruments play an important role in judicial work such as search, interrogation, and evidence review. The use of man-made reasoning in the field of legal settling has become increasingly broad. Specialists are giving increasingly more consideration to the securing of modular proof. This paper proposes the development of a component examination model for modular proof examination applications. The technique for this paper is to apply the guileless Bayes strategy, propose a superior information mining calculation, and lay out a model for proof observation and examination. The function of these methods is to systematically explore the basic theoretical issues of demeanor evidence based on the status quo of judicial application of demeanor evidence. Through the prediction of individual demeanor based on data mining algorithm, the evidence analysis model is designed, the neurobiological experiment is carried out, and the demeanor evidence animal stress model is constructed to verify the scientific basis of multidemeanor evidence observation. The experimental results showed that after repeated stimulation of SD rats, the maximum changes in the expression of HSP70 gene and SAA gene were 10.77 and 14.1 respectively, reflecting the high reliability of demeanor evidence biological experiments. The model can improve the accuracy of evidence use, and the correct use of demeanor evidence can realize the true litigation value of intelligent justice and the concept of human rights protection, and promote the construction of intelligent justice. [ABSTRACT FROM AUTHOR]

This paper discusses radio resource management in the telecommunications industry that utilizes opportunistic scheduling approaches and methods. It proposes a new scheduler called IFMR (Improved Fairness of MaxRate) to extend the opportunistic approach with the proposition of a new scheduling solution that enables to significantly maximize the system throughput. Our goal is to ensure better results that maximize system throughput, ensure perfect fairness at three levels and maximize the number of satisfied users who have achieved their desired throughput. Similarly, the performance of the proposed algorithm is evaluated and compared with reference algorithms: round robin, MaxSNR and proportional fair (PF). The focus of this study is to evaluate and analyze the quality of service guaranteed to each network subscriber, considering the heterogeneous data traffic based on simulations. The algorithms presented in this paper are programmed and simulated using the MATLAB simulation software. The study aims to effectively analyze the impact of radio resource management algorithms and schedulers, including the round robin algorithm, MaxSNR and PF, among others, with the main objective of evaluating the performance of the schedulers used by telecommunication and cellphone network operators. The scheduling phase, which precedes resource allocation and assignment, is a critical aspect of the study. The impact of these schedulers on throughput and fairness will also be evaluated and analyzed based on specific criteria. [ABSTRACT FROM AUTHOR]

Patient data are susceptible and vulnerable when collected through open channels. Since these data are collected through various IoT (Internet of Things) devices, mobile applications, and web-based applications, their security is more important. Many hackers and attackers can be found between the patient, the cloud server, and the data collector. Blockchain is a secure, immutable, and transparent method of collecting and storing data. In this paper, a novel scheme for telehealth data sharing is proposed. In this proposed scheme, we use a property mapping function to map the common properties of participating blockchains. The data-sharing case is decided based on the property mapping score, and a temporary blockchain TempChain is created. Data sharing is done using the safe search data and sharing only the required data with the requester blockchain. This proposed scheme improves the security and privacy of telehealth data sharing. The results of the proposed scheme in different cases show the proposed scheme's efficiency with respect to the available state-of-the-art system. The proposed scheme's security is also examined in this paper. [ABSTRACT FROM AUTHOR]

Channel pruning has recently become a widely used model compression method. However, most existing channel pruning methods only prune to decrease the model size, such as the number of parameters or FLOPs, and hence the decrease in model size does not effectively lead to an improvement in inference performance. To address this problem, this paper proposes a latency-optimized channel pruning method for CNN inference acceleration on GPU platforms by latency stair-step discrimination, two-stage benefit assessment and latency-sharing channel pruning. Compared with recent state-of-the-art model compression methods, it can achieve significant improvements in inference performance with comparable compression rates and model accuracy. The contributions of this paper include the following: first, a three-point latency stair-step discrimination method is proposed for determining the candidate prunable coordinates with the best latency performance adapted to the current hardware. Then, a two-stage benefit assessment method based on interlayer dependencies is proposed for determining the optimal channel pruning rate of each layer in the network. Finally, a latency-sharing channel pruning framework is proposed to accelerate the model pruning adaptation process. The method proposed in this paper can significantly reduce the model inference latency on multiple types of GPU platforms. To verify the effectiveness, we use three general-purpose GPU platforms and two embedded GPU platforms to evaluate the algorithm performance. The experimental results show that for recent state-of-the-art CNNs, the proposed method can achieve a 22.0–6.6% latency reduction and a 1.3 –3.0 inference performance improvement as well as a 1.2–4.3 pruning adaptation speedup with high model accuracy. [ABSTRACT FROM AUTHOR]

Considering the trend of scaling down conventional technologies toward next-generation devices and the importance of new practical and efficient procedures in increasing the efficiency of digital computing circuits, this paper presents three full adder circuits. Suggested circuits, named Design-1 to Design-3, are proposed based on FinFET technology with channel length of 11 nm. In order to optimize energy consumption and area occupation, they have been implemented based on approximate computing with 4, 10, and 12 transistors, respectively. The main characteristics of the presented circuits lie in their utilization of the gate diffusion input technique, which has resulted in the generation of only three errors in their output. Diverse objectives for error generation have been incorporated into these circuits, positioning them as highly accurate competitors when compared to the most state-of-the-art designs. Also, due to the use of low number of transistors, which has led to low internal nodes, these circuits are in a favorable position in terms of power consumption and speed. To evaluate these circuits, extensive simulations are conducted considering various implementations, including single-bit configurations and integration within 4-bit ripple carry adders with 2 and 4 approximate bits. Based on the figure of merits used consist of diverse design parameters, Design-1 with the least number of transistors has a difference of about 85% improvement compared to the nearest competitor, while design-2 and design-3 circuits are also placed in desirable rankings. [ABSTRACT FROM AUTHOR]

The isolation technique of containers introduces uncertain security risks to malware detection in the current container environment. In this paper, we propose a framework called Malware Detection for Container Runtime based on Virtual Machine Introspection (MDCRV) to detect in-container malware. MDCRV can automatically export the memory snapshots by using virtual machine introspection in container-in-virtual-machine architecture and reconstruct container semantics from memory snapshots. Although in-container malware might escape from the isolating measures of the container, our detecting program which benefits from the isolation of the hypervisor still can work well. Additionally, we propose a container process visualization approach to improve the efficiency of analyzing the binary execution information of container runtime. We convert the live processes of in-container malware and benign application to grayscale images and employ the convolutional neural network to extract malware features from the self-constructed dataset. The experimental results show that MDCRV achieves high accuracy while improving security. [ABSTRACT FROM AUTHOR]

In order to solve the problems of slow convergence and low accuracy of the traditional ant colony algorithm in solving the traveling salesman problem (TSP), this paper proposes a heterogeneous ant colony algorithm based on the selective evolution mechanism and game strategy (SGHACA). First, we propose a game mechanism. In each iteration, the best population and the worst population will play a game. At the end of the game, pheromone gains will be given to the participants according to the weight coefficients to motivate the populations to participate in the next round of the game, in order to promote cooperation among different populations and thus improve the diversity of the algorithm. Second, we introduce a selective evolution mechanism. The optimal paths of each iteration are compared with the historical optimal paths to evolve relatively better path fragments, thus improving the convergence speed of the algorithm. Finally, when the algorithm stagnates, we introduce a dynamic shaking mechanism, which is able to dynamically dither the pheromone on the current optimal path according to the number of iterations, increasing the probability that the algorithm chooses other paths, thus helping to jump out of the locally optimal solution. In order to verify the effectiveness of the improved algorithm in this paper, we experimentally verify through a large number of TSP instances that SGHACA is able to improve the convergence of the algorithm and the accuracy of the solution to a certain extent when solving large-scale TSP. Meanwhile, compared with the latest improved algorithms, the accuracy of the solution of SGHACA is better than most of the latest improved algorithms, which indicates that the improved algorithm of this paper has some competitiveness. [ABSTRACT FROM AUTHOR]

Medical images contain sensitive information that will be shared with healthcare workers, patients, and researchers for diagnostic and research purposes. However, the security of these images during transmission is extremely important, especially after the COVID-19 pandemic. In diagnosis, MRI and CT images are two common medical images, and the two medical images complement each other to a certain extent. The fusion of these two images can help doctors diagnose more intuitively. This paper proposes a zero-watermarking encryption scheme with an authentication function based on multimodal medical image fusion. In addition, an improved three-dimensional Chebyshev and ICMIC closed-loop coupling mapping (3D-CICCM) is proposed and applied to the design of medical image encryption. This scheme mainly includes: medical image fusion based on bilateral filtering and local gradient energy, authentication based on the Fresnel transform, double random phase encryption based on the Fresnel transform and shuffle algorithm, and zero-watermark generation algorithm based on stationary wavelet transform. The experimental results prove the effectiveness of this scheme. [ABSTRACT FROM AUTHOR]

OP2 is a domain-specific language-based programming framework for unstructured mesh applications. It supports automatic code generation targeting multiple parallel modes, with CUDA included. However, using OP2 to generate efficient CUDA code for real-world applications is a challenging task. This paper reports our efforts optimizing the CUDA code performance when refactoring an unstructured high-order CFD application (namely HOUR2D) based on OP2. A series of novel methods are realized, including utilizing appropriate execution strategies, using local arrays, and optimizing the OP2 data transfer function, etc. Performance evaluation shows that our optimizations significantly improve the performance of the finally generated CUDA code. The overall performance of our optimized OP2-CUDA code is 13.2 times higher than the unoptimized OP2-CUDA code and 2.4 times higher than the manual CUDA code. Meanwhile, these optimizations do not affect the portability of HOUR2D as an OP2 application. [ABSTRACT FROM AUTHOR]

In recent years, the increasing demand for knowledge services and the challenges of information overload have posed significant problems in delivering personalized and efficient agricultural knowledge services. This paper presents a comprehensive framework that addresses the issues of vague user positioning, serious privacy leakage, and low efficiency in personalized knowledge services within the national agricultural knowledge intelligent service cloud platform. The proposed framework utilizes privacy-protected user portraits based on generative adversarial nets (GAN) and leverages the TextCNN-LSTM algorithm for agricultural knowledge service prediction. By embedding labels into the algorithm and employing data obfuscation techniques, the framework achieves accurate inference of user behavior while preserving user privacy. Experimental results demonstrate the effectiveness and accuracy of the proposed framework, highlighting its potential for regional precise positioning and recommendation of personalized agricultural knowledge services. Experimental data shows that the average absolute error and root-mean-square error of this method are 1.1997 and 1.4143, respectively, and compared with MLP, TextCNN, and LSTM models, and it has higher prediction accuracy. In recent years, the increasing demand for knowledge services and the challenges of information overload have posed significant problems in delivering personalized and efficient agricultural knowledge services. [ABSTRACT FROM AUTHOR]

The reliable prediction of solar energy production and surplus is crucial for the stability of the electricity grid and effective energy distribution, particularly during peak consumption periods. Encouraging solar adoption by customers is also important despite fluctuations in solar energy generation. This paper addresses the need for an intelligent surplus solar energy prediction model, referred to as the Green Model, which aims to accurately predict surplus electrical energy that can be returned to the distribution grid. The Green Model utilizes a combination of a developed long short-term memory (LSTM) neural network and a novel optimization technique called deterministic selection network by gain sharing knowledge (DSN-GSK). The DSN-GSK optimizes the structure of LSTM by determining the optimal number of hidden layers, nodes in each layer, biases, weights, and activation functions based on the most important features that represent the relationship between generated solar energy and weather factors. The Green Model was evaluated and analysed, demonstrating high accuracy in the short-term prediction of surplus electrical energy with a minimal percentage of error. The model shows promising prospects for the development of returned electrical energy prediction. The main novelty of this paper lies in the development of the Green Model, which combines LSTM and DSN-GSK to enhance the accuracy of surplus solar energy prediction. The benefits of the Green Model include improved stability and efficiency of the electricity grid, effective utilization of renewable energy sources, and the reduction of negative environmental impacts. The contributions of this paper include advancing the field of intelligent energy prediction, optimizing the structure of LSTM, and providing valuable insights for the integration of renewable energy into the existing energy infrastructure. [ABSTRACT FROM AUTHOR]

A blockchain is one distributed ledger system and keeps the high redundancy of ledger copies to make the assurance of network security. However, the continuously duplicated full copies also impose a tremendous amount of demand on some nodes for data storage. The development of blockchain technologies in the Internet of things (IoT) application scenario is hampered by the restricted storage capacities of terminal devices used in the IoT edge computing scenario, which makes it difficult to load the full copy with infinite growth. Our paper suggests a collaborative ledger storing model based on Chord Ring to address the issues with lightweight blockchains in data storage. On-chain blocks are split by Chord Ring structure and stored in various node clusters in a decentralized manner, and off-chain blocks at various levels are provided with proof-of-work consensus asynchronously and archived centrally to the cloud storage on a regular basis. The theoretical and experimental analysis indicates that this model can reduce the data storage redundancy of blockchains while ensuring the high availability of data and the high decentralization of the network. [ABSTRACT FROM AUTHOR]

The early and effective diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) has received increasing attention in recent years. However, currently available deep learning methods often ignore the contextual spatial information contained in structural MRI images used for early diagnosis and classification of Alzheimer's disease. This may lead us to miss important structural details by failing to adequately capture the potential connections between each slice and its neighboring slices. This lack of contextual information may cause the accuracy of the network model to suffer, which in turn affects its generalization ability and application in real-life scenarios. To explore deeper the connection between spatial context slices, this research is designed to develop a new network model to effectively detect or predict AD by digging into the deeper spatial contextual structural information. In this paper, we design a spatial context network based on 3D convolutional neural network to learn the multi-level structural features of brain MRI images for AD classification. The experimental results show that the model has good stability, accuracy and generalization ability. Our experimental method had a classification accuracy of 92.6% in the AD/CN comparison, 74.9% in the AD/MCI comparison, and 76.3% in the MCI/CN comparison. In addition, this paper demonstrates the effectiveness of the proposed network model through ablation experiments. [ABSTRACT FROM AUTHOR]

The presence of haze results in diminished image clarity, the loss of image details, color alterations, and a decline in image visibility. This paper proposes an efficient multi-stage feature fusion defogging network based on the attention mechanism for effective defogging in complex environments. To create a multi-branch defogging network, the model integrates several attention techniques to create various sorts of branching network architectures. The image's spatial details and contextual information are supplemented and merged based on the recovered image feature information of different network branches to increase the effectiveness of the network model. The stage attention fusion mechanism created among several network branches can lessen the loss of image data during feature extraction and improve the effectiveness of the image-defogging operation. The experimental results demonstrate that the proposed algorithm has superior defogging performance in both synthetic and real-world scene datasets and performs more admirably in terms of accuracy compared to other sophisticated algorithms, particularly in the RESIDE and O-Haze datasets. The PSNR metrics on the RESIDE and O-Haze datasets are improved by 1.58 dB and 1.61 dB, respectively, compared to the best-advanced technique suggested in this study, and the SSIM metrics on the O-Haze dataset are improved by 3.4%. [ABSTRACT FROM AUTHOR]

The scarcity of defect samples and the imbalance of defect types lead to the fact that achieving defect detection in color-patterned fabrics remains a challenge in the textile industry. Defect detection methods based on traditional auto-encoder are difficult to solve the problem of defect detection in complex color-patterned fabrics. In order to solve the problem of weak feature representation of traditional auto-encoders, this paper proposes an unsupervised method based on triplet attention multi-scale U-shape denoising convolutional auto-encoder (TA_MSUDCAE). The method further enhances the feature representation capability of the auto-encoder by introducing a triplet attention mechanism based on utilizing the multi-scale information of the image. Firstly, the defect-free samples that are added with Gaussian noise are used as inputs to the model in the training stage. The model is trained to reconstruct and repair the defective regions. Secondly, the test image is input to the trained model to obtain a normal reconstructed image, and the residual image is obtained by calculating the difference between the input image and the reconstructed image. Finally, the defect detection and localization results are obtained by threshold segmentation and mathematical morphology processing of the residual image. A large number of experiments have been carried out on a variety of representative color-patterned fabrics, and the results prove the effectiveness of the proposed method in fabric defect detection. [ABSTRACT FROM AUTHOR]

This paper solves the task of unsupervised video object segmentation (UVOS) that segments the objects of interest through the entire videos without any annotation. In recent years, many unsupervised video object segmentation (UVOS) methods have been proposed. Although these methods perform well, they rely on networks with heavy weights, often leading to large model size. In order to reduce the model size while keeping a competitive performance, we propose a saliency-based dual-attention (SDA) method for UVOS in this paper. In our method, we take optical flow and video frames as inputs and extract the appearance information and motion information from optical flow and video frames. We design a two-branch network with appearance information and motion information. The information from these two branches is fused via a saliency-based dual-attention module to segment the primary object in one path. The saliency-based dual-attention module is composed of saliency attention and saliency-based reverse attention. To demonstrate the effectiveness of our network, we tested it on the DAVIS-2016 and SegtrackV2 datasets. Experimental results demonstrate that our method can achieve competitive results in terms of accuracy and model size. [ABSTRACT FROM AUTHOR]

In recent years, the combination of deep learning and side-channel analysis has received extensive attention. Previous research has shown that the key recovery problem can be transformed into a classification problem. The performance of these models strongly depends on the size of the dataset and the number of instances in each target class. The training time is very long. In this paper, the key recovery problem is transformed into a similarity measurement problem in Siamese neural networks. We use simulated power traces and true power traces to form power pairs to augment data and simplify key recovery steps. The trace pairs are selected based on labels and added to the training to improve model performance. The model adopts a Siamese, CNN-based architecture, and it can evaluate the similarity between the inputs. The correct key is revealed by the similarity of different trace pairs. In experiments, three datasets are used to evaluate our method. The results show that the proposed method can be successfully trained with 1000 power traces and has excellent attack efficiency and training speed. [ABSTRACT FROM AUTHOR]

Hardware/software (HW/SW) partitioning is a vital aspect of HW/SW co-design. With the development of the design complexity in heterogeneous computing systems, existing partitioning algorithms have demonstrated inadequate performance in addressing problems relating to large-scale task nodes. This paper presents a novel HW/SW partitioning algorithm based on node resource attributes hovering swarm particle swarm optimization (HSPSO). First, the system task graph is initialized via the node resource urgency partitioning algorithm; then, the iterative solution produced by HSPSO algorithm yields the partitioning result. We present new initialization by combining node resource attribute information and introduce two improvements to the learning strategy of HSPSO algorithm. For the main swarm, a directed sample set and the addition of perturbation particles are designed to direct the main swarm's particle search process. For the secondary swarm, a dynamic particle update equation is formulated. Iterative updates are performed based on previous rounds' prior information using adaptive inertia weight. The experimental results illustrate that, in large-scale systems task graph partitioning with more than 400 nodes, when compared with mainstream partitioning algorithms, the proposed algorithm improves partitioning performance by no less than 10% for compute-intensive task graphs and no <5% for communication-intensive task graphs, with higher solution stability. [ABSTRACT FROM AUTHOR]

To address the path planning problem for unmanned combat aerial vehicle (UCAV) more effectively, a novel two-stage path planning model is proposed. The first stage involves a longitudinal search primarily aimed at predicting the initial path, while the second stage is a horizontal search designed to correct the initial path. Furthermore, to tackle the UCAV path planning issue more effectively, this paper designs an improved elephant clan optimization (IECO) algorithm based on the average sample learning strategy, opposition-based learning, and Lévy flight disturbance strategy. Subsequently, IECO is integrated with the two-stage model (TSIECO) to address the UCAV path planning problem. Additionally, numerical experiments across 15 test functions reveal that IECO outperforms other algorithms in terms of optimization capability and convergence speed. Finally, the UCAV path planning experimental results indicate that the two-stage model based on IECO, as proposed in this paper, has significant advantages over traditional path planning models based on other swarm intelligence algorithms. Specifically, in three different simulated environments, the TSIECO has been tested on a total of 9 maps with varying parameters, yielding paths that are optimal in terms of cost and stability. [ABSTRACT FROM AUTHOR]