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Flat bands in 2D twisted materials are key to the realization of correlation-related exotic phenomena. However, a flat band often was achieved in the large system with a very small twist angle, which enormously increases the computational and experimental complexity. In this work, we proposed group-V twisted bilayer materials, including P, As, and Sb in the β phase with large twist angles. The band structure of twisted bilayer materials up to 2524 atoms has been investigated by a deep learning method DeepH, which significantly reduces the computational time. Our results show that the bandgap and the flat bandwidth of twisted bilayer β-P, β-As, and β-Sb reduce gradually with the decreasing of twist angle, and the ultra-flat band with bandwidth approaching 0 eV is achieved. Interestingly, we found that a twist angle of 9.43° is sufficient to achieve the band flatness for β-As comparable to that of twist bilayer graphene at the magic angle of 1.08°. Moreover, we also find that the bandgap reduces with decreasing interlayer distance while the flat band is still preserved, which suggests interlayer distance as an effective routine to tune the bandgap of flat band systems. Our research provides a feasible platform for exploring physical phenomena related to flat bands in twisted layered 2D materials. [ABSTRACT FROM AUTHOR]

The dual-scrambling scheme that combines position transformation and bit-plane transformation is a popular image encryption scheme. However, such schemes need more key information, and the encryption and decryption processes are complicated. In addition, the existing quantum image dual-scrambling schemes mainly deal with square images. In this paper, we propose a hybrid scrambling encryption scheme for multi-mode quantum image representation (MQIR) images based on random permutation, in which the H × W quantum image is represented in MQIR. A random number generator factor s uniquely associates one of the random permutations of integers from 1 to a positive integer, so as to hybrid scramble both the pixel position and the binarized position of each pixel value. Meanwhile, the quantum circuits and some examples of scrambling are given. Furthermore, various analyses of the performance of this scheme were conducted, including effectiveness, key space, and computational complexity. By modifying the random generation factor to construct multiple binary grayscale images, the simulated results on the IBM Quantum Cloud platform demonstrate that the proposed quantum image encryption scheme is effective. In comparison to existing quantum image dual scrambling schemes, it is both simple and effective, offering a large key space, lower computational complexity, and applicability to non-square quantum images. [ABSTRACT FROM AUTHOR]

In this paper, a new mixed integer programming (MIP) formulation is developed for balancing and scheduling of mixed model assembly lines with disjunctive precedence constraints among assembly tasks. To represent alternative precedence relations, AND/OR assembly graph was adopted. In case of alternative precedence relations, for each product multiple assembly plans exist, which can be represented by a set of alternative precedence subgraphs and only one of such subgraphs should be selected for each product. As the number of subgraphs exponentially increases with the number of disjunctive relations among the tasks, the computational complexity of simultaneous balancing and scheduling along with the assembly subgraph selection increases with the number of alternative precedence relations. Unlike the other MIP approaches known from the literature, the new model does not need the alternative assembly subgraphs to be to explicitly enumerated as input data and then used for indexing the variables. Instead, a new disjunctive precedence selection and task assignment variable and new constraints are introduced to optimally choose one relation for each subset of alternative precedence relations. The optimal solutions for computational examples of balancing and scheduling problems illustrate a superior performance of the new modelling approach. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is a severe retinal condition primarily affecting diabetic people. This is mostly due to excessive blood sugar levels, which induce retinal blood vessel damage. Diabetic Retinopathy must be detected early to avoid vision loss. Exudates on the retina are the primary indicator of diabetic retinopathy. Therefore, their detection would be crucial for automated illness monitoring and grading. This research provides novel classification and segmentation methods for accurately classifying DR images. The process involves four stages: preprocessing, DR classification, Exudates segmentation, and performance computation. In the preprocessing stage, the source RGB retinal images are separated into three bands, R-Image, G-Image, and B-Image, and each sub-band image is then subjected to data augmentation. The data-augmented images are classified through the proposed Hybrid Centric Convolutional Neural Networks (HCCNN) classifier to produce the classification results (Grade 0 or Grade 1). The proposed exudate lesions segmentation method is utilized to predict and find the exudates in the DR image. The exudates segmented retinal Image is then used to do performance computations for sensitivity (Se), specificity (Sp), and accuracy (Acc), as well as computational complexity analysis. The standard datasets (SYSU and HRF) and the real-time dataset are subjected to the HCCNN approach. The mean Se, Sp, and Acc obtained by this HCCNN method are 98.65%, 98.76%, and 98.96% for the SYSU and 98.58%, 98.65%, and 98.6% for the HRF dataset, respectively. Our method obtains an average detection ratio of 91.66% in the real-time dataset. The suggested HCCNN approach produces findings that outperform the conventional methods and help doctors identify the DR disease early to prevent further vision loss. [ABSTRACT FROM AUTHOR]

This study introduces an innovative hybrid approach for personalized trip recommendations, aiming to enhance existing recommender systems by leveraging multidimensional data. Our proposed method integrates user preferences and diverse contextual factors to address challenges related to data sparsity effectively. To overcome this hurdle, our methodology employs a clustering approach, streamlining the extraction of Points of Interest (PoI) and reducing computational complexity. The framework comprises three key components: I) a unique strategy for context assessment, achieved by combining contextual information in vector form through the Term-Frequency-Inverse-Document-Frequency technique, II) the incorporation of tourist demographic information to alleviate the Cold Start problem, and III) the implementation of an asymmetric schema that elevates the traditional similarity paradigm. Moreover, our approach utilizes personalized PoIs in consecutive travel patterns, enabling the retrieval and ranking of an optimal list of potential routes. The experimental results based on Flickr and Yelp datasets reveal that the proposed method surpasses prior work on all three metrics, achieving a significant 8% increase in precision and an 11% increase in F-Score, thereby enhancing the quality metrics of personalized trip recommendations. [ABSTRACT FROM AUTHOR]

Image processing often requires filtering, which can be effectively performed by using B‐spline surface approximation. This article presents a fast method of approximating data in rectangular (image) form using such surfaces. The method considers dynamic changes of data by representing rows and columns in chordal parameterized form. To improve performance, after parametrization, lines are uniformly resampled using linear interpolation. It allows using the same basis functions with uniform parameterization for approximation of all processed lines. After approximation, reconstruction of original parameterization is required, but its computational complexity is also linear. [ABSTRACT FROM AUTHOR]

In the digitalized world, efficient big data clustering is necessary for massive data generation. The clustering algorithm plays an important role in resolving the computational complexity. The big data arriving from various sources are being processed using the MapReduce framework (MRF) by the knowledge of the clustering algorithms. Moreover, the clustering algorithm is useful for mining the significant information from the dataset. Generally, there are several difficulties in applying the clustering approach to big data as its new challenges are based on computation cost and reasonable time. Hence, this research introduced the Competitive Jaya Leader Harris Hawks Optimization assisted Entropy Weighted Power K-Means Clustering (CJayaLHHO_EWPKMC) for big data clustering. In addition, the overall processing of the devised method for big data clustering is carried out in the MapReduce (MR) framework. In mapper, the feature selection is done using Support vector Machine-Recursive Feature Elimination (SVM-RFE) assisted Jaya Leader Harris Hawks Optimization (JayaLHHO). In the reducer, the big data clustering is established using the EWPKMC method, wherein the weight of EWPKMC is modified with the CJayaLHHO algorithm such that the clustering outcome is attained. The proposed method is scalable, simple, cost-effective, and able to integrate with other technologies. The experimental result portrays that the developed method attained a superior presentation than the conventional methods based on the clustering accuracy is 0.937, the Jaccard coefficient is 0.913, and the rand coefficient is 0.912. [ABSTRACT FROM AUTHOR]

The purpose of long-term forecasting is to meet the needs of practical applications, such as the prediction of the development trend of infectious diseases and the planning of electricity consumption. In this paper, we study the long-term forecasting of time series. Studies have shown that previous Transformer-based models have the potential to improve prediction capabilities, but there are also some problems, such as the lack of location information and the slow training speed. To solve these problems, we designed an efficient Transformer-based model called AD-Autoformer, which is specifically designed for long-term series prediction. By introducing position embedding, the model can better understand the patterns and relationships in the sequence, to improve the performance and generalization ability of the model when processing sequence data, and the self-attention distilling mechanism realizes the compression and acceleration of the model by halving the cascading layer input to highlight the dominant attention. This method significantly reduces the computational complexity of the model and improves the training speed of the model while maintaining the performance of the model. Experimental results on five large datasets show that the proposed AD-Autoformer model has different degrees of improvement in MSE and MAE indicators compared with other benchmark methods. [ABSTRACT FROM AUTHOR]

The implementation of turn restrictions represents a critical research challenge in chiplet-based systems, with the objective of achieving deadlock-free communication. Nevertheless, existing methodologies encounter difficulties in terms of computational complexity, which impedes the design process. Moreover, as the scale of the problem increases, the cost of addressing it becomes increasingly untenable. In this paper, we introduce the efficient turn restrictions setting (ETRS) method to reduce the computational cost of implementing turn restrictions for boundary routers in chiplet-based systems. In stage 1, we present a symmetry-based preprocessing algorithm (SBPA). SBPA exploits the symmetry inherent in chiplet topologies by generating multiple sets of identical modes, ensuring that scenarios with the same objective function value are calculated only once per iteration. In stage 2, a heuristic selection algorithm (HSA) for turn restrictions based on NSGA-II is proposed as a means of searching for approximately optimal solutions and of solving the problem quickly. In stage 3, different filtering criteria are introduced to evaluate the Pareto fronts of HSA for making decisions on the placement of boundary routers and their turn restrictions. Evaluation results reveal that the proposed ETRS method surpasses existing solutions in terms of computation efficiency. Moreover, it delivers satisfactory optimal objective values. [ABSTRACT FROM AUTHOR]

Printed circuit boards (PCBs) play a critical role in electronic products. Ensuring these products' long-term reliability and consistent performance requires effective PCB defect detection. Although existing deep learning models for PCB defect detection are not highly accurate, they often neglect capability considerations. This paper introduces a precise, fast, and lightweight defect detection model, CCG-YOLO, based on an enhanced YOLOv5 model to address this issue. The enhancements in CCG-YOLO can be summarized as follows: (1) Improved Backbone network: The feature extraction ability of the Backbone network is enhanced by introducing a C3HB module, which fosters spatial interaction capabilities. (2) Lightweight feature fusion network: A lightweight convolution structure called Ghost-Shuffle Convolution is incorporated in the feature fusion network, remarkably reducing model parameters while maintaining performance. (3) Efficient residual networking: To enhance model performance further, a CNeB module is introduced based on the ConvNeXt network, which replaces the C3 module in the Neck. CNeB improves model detection accuracy and reduces the number of model parameters. The combination of these enhancements results in impressive performance. CCG-YOLO achieves mean average precision (mAP@0.5) of 99.5% and 88.75% in mAP@0.5:0.95 on the TDD-Net public dataset. Compared with the original YOLOv5s algorithm, CCG-YOLO offers a 4.24% improvement in mAP@0.5:0.95, a 1 MB reduction in model size, a 0.472 M decrease in the number of parameters, a 0.6G floating point operation reduction in computational complexity, and a 120 frames per second real-time inference speed. These experimental results underscore that the proposed model excels in accuracy and speed and has a compact size for PCB defect detection. Moreover, CCG-YOLO is easily deployable on low-end devices, making it well-suited for meeting the real-time requirements of industrial defect detection. [ABSTRACT FROM AUTHOR]

In this paper, we study Horn formulas from the perspective of read-once resolution refutations (RORs). A Horn formula is a Boolean formula in conjunctive normal form (CNF), in which each clause contains at most one positive literal. Horn formulas are used in a number of domains, including program verification, logic programming, and econometrics. In particular, deduction in ProLog is based on unification. Unification is based on resolution and instantiation. Resolution is a system used to prove the infeasibility of Boolean formulas. It is important to note that resolution is both sound and complete. However, resolution is inefficient in the following sense: There exist CNF formulas with resolution refutations whose lengths are bounded below by an exponential function of the input size. At the same time, these formulas admit shorter (polynomially bounded) proofs of infeasibility in other proof systems, such as Frege Systems. Despite this inefficiency, resolution is simple and easy to implement and hence used in a wide variety of theorem provers. In this paper, we study two variants of resolution. These are read-once resolution (ROR) and read-once unit resolution (UROR). Both ROR and UROR are sound. However, they are incomplete since there exist infeasible Boolean formulas which do not have either an ROR or a UROR. In this paper, we look at the problems of determining if a Horn formula has an ROR or a UROR. We also examine the problem of finding the optimal length ROR of a Horn formula from both the computational complexity and the approximation perspectives. Finally, we analyze the copy complexity of Horn formulas with respect to URORs. [ABSTRACT FROM AUTHOR]

In the path version of the Travelling Salesman Problem (Path-TSP), a salesman is looking for the shortest Hamiltonian path through a set of n cities. The salesman has to start his journey at a given city s , visit every city exactly once, and finally end his trip at another given city t. In this paper we show that a special case of the Path-TSP with a Demidenko distance matrix is solvable in polynomial time. Demidenko distance matrices fulfill a particular condition abstracted from the convex Euclidian special case by Demidenko (1979) as an extension of an earlier work of Kalmanson (1975). We identify a number of crucial combinatorial properties of the optimal solution and design a dynamic programming approach with time complexity O (n 6). [ABSTRACT FROM AUTHOR]

Summary: Localization algorithms are essential for tracking, fault tolerance, and adding location context to Internet of Things (IoT) networks. Among the various range‐free techniques, DV‐Hop stands out for its lower computational complexity. Despite efforts to minimize manufacturing costs for sensors, they often find themselves deployed in remote and hard‐to‐access locations, making battery replacement a challenging prospect. These sensors can be utilized in a hostile environment, such as a field of battle, fires, floods, and earthquakes. In these types of scenarios, the sensors can become dead and stop communicating with each other. Hence, there is a critical need to improve algorithms that can tolerate and detect faults in network nodes. The faults of the sensor can lower the quality of the algorithm if they are not replaced. In such situations, we need to distribute supplementary target nodes to replace faulty nodes. This paper presented an improved DV‐Hop based method for localization of additional target nodes using grey wolf optimization (GWO). The proposed method will only localize the position of target nodes and minimizes the localization time, which makes the system more energy efficient. Simulation results conclude that the proposed GWO‐based DV‐Hop localization performed well as compared with traditional DV‐hop, genetic DV‐Hop, and Adaptive genetic DV‐hop. [ABSTRACT FROM AUTHOR]

In the emergency rescue and disposal of social public emergencies, supply transportation effectively provides a strong supply foundation and realistic conditions. The trajectory tracking control of emergency supplies transportation robot is the key technology to ensure the timeliness of transportation. In this article, the emergency supplies transportation robot is taken as the research object, based on Koopman operator theory, combined with radial basis function (RBF) neural network disturbance observer and adaptive prediction horizon event‐triggered model predictive control (APET‐MPC) algorithm to investigate the purely data‐driven trajectory tracking control problem of emergency supplies transportation robot when the model parameters and models are unknown. Firstly, the Koopman operator is used to establish a high‐dimensional linear model of the robot. Secondly, the RBF neural network disturbance observer is designed to estimate the disturbance during the robot operation and compensate it to the controller. Thirdly, APET‐MPC is used to optimize the trajectory tracking control of the emergency supplies transportation robot to reduce computational complexity. Finally, the performance of the proposed trajectory tracking controller is verified by Carsim/ Simulink joint simulation. The simulation results show that the model established by Koopman operator theory can achieve the high accuracy approximation of the robot. Compared with the MPC trajectory tracking controller, the APET‐MPC trajectory tracking controller based on RBF neural network disturbance observer (RBF‐APET‐MPC) improves the tracking accuracy of the robot and reduces the total triggering times of the system by more than 50%. [ABSTRACT FROM AUTHOR]

Summary: Power system monitoring relies on the reliability of state estimation (SE) results. SE plays a dominant role in data debugging if sufficient data is available. Criticality analysis (CA) integrates SE as a module in which measurements—taken one‐by‐one or in groups (tuples) of minimal cardinality—are designated crucial. The combinatorial nature of extensive CA (not restricted to identifying low‐cardinality critical tuples) characterizes its computational complexity and imposes challenging limits to go beyond. In simple terms, these limits are established by the number of measurements to be combined, the cardinality of tuples, and the computing time required to check the criticality condition. This paper proposes an innovative computational solution to expand CA limits found to date in the literature. A framework with multi‐threads designed cleverly on a graphics processing unit (GPU) parallel processing environment is built. The conceived architecture favors evaluating massive measurement combinations of diverse cardinality in extensive CA. Numerical results reveal significant speed‐ups with the proposed approach, contrasting with those reported in research efforts published so far. [ABSTRACT FROM AUTHOR]

Emotion plays a vital part in the life of humankind. Emotion Recognition (ER) is the necessary task to find one's inner Emotion through speech, text or face. ER process has inevitable places in applications such as education, healthcare, defence, forensics, automation and scientific-based purposes. However, the recognition models face various issues during the process. Due to the large feature sets, the model's computational time and complexity have been increased; it also produced high errors in recognition, which degrades the efficiency of the system. So, a novel Monkey-based V-Net Framework (MbVF) was developed for this research work to classify the Emotion and sentiment patterns using healthcare data. Initially, the healthcare Artificial Intelligence (AI) conversation data from the Coronavirus Tweets NLP-Text Classification dataset was collected and trained for the system. Next, stemming, stop-words removal, tokenization, and noise feature elimination process were performed in the preprocessing step. Then, the Term Frequency based-Inverse Document Frequency (TF-IDF) features were used to choose the best features depending on the fitness features of the proposed MbVF approach. Finally, the ER process was performed to recognize the emotions, such as positive, neutral and negative, using sentiment analysis with the emotion state transition and emotion pattern. The model attained 99.98% accuracy in predicting emotions, and is higher than the other model. [ABSTRACT FROM AUTHOR]

Developing efficient and secure image encryption techniques for transmitting multiple images has become crucial due to the inadequacy of single-image encryption techniques in handling the increasing volume of big data over unprotected networks. This paper introduces a novel multiple-image encryption (MIE) technique that utilizes mixed image elements in conjunction with the RSA cryptosystem, fractional discrete cosine transform (FrDCT), and Henon chaotic map (HCM). To encrypt k images together, the first step involves making three big images B 1 , B 2 and B 3 from these k images using matrix theory. The three images B 1 , B 2 and B 3 are converted into three indexed images I 1 , I 2 , and I 3 by extracting their color maps. Indexed images I 1 , I 2 and I 3 are then treated as a single RGB image by taking I 1 as a red (R) component, I 2 as a green (G) component and I 3 as a blue (B) component. The RSA cryptosystem is then applied on each component individually, followed by FrDCT and HCM to enhance security and key space. The resulting encrypted image is a single-channel real-valued image that is easy to display, store, and transmit over an unsecured network. The suggested technique offers multi-layer security in frequency, time and coordinate domains. Private keys, their arrangements and parameter positions are critical for decryption. Simulation analysis supports the robustness and effectiveness of the proposed technique. The sensitivity analysis demonstrates its extreme sensitivity towards private keys and their arrangement. Statistical analysis, including measures such as MSE, SSIM, PSNR, NPCR, UACI, entropy analysis, correlation coefficient, histogram analysis, computational complexity and comparison analysis confirm the effectiveness and viability of the introduced algorithm. [ABSTRACT FROM AUTHOR]

Lossless image compression techniques shrink the image size to improve the transmission efficiency and reduce the occupied storage space while ensuring the quality of the image is lossless. Among them, the LOCO‐I/JPEG‐LS algorithm benefits high lossless compression ratio and low computational complexity and thus is widely used for various real‐time applications. However, due to the problems of the context dependency in the LOCO‐I, the parallelism in the algorithm is greatly constrained, which significantly limits the throughput and the real‐time performance of hardware implementations. Existing designs achieve more parallelism by using a lot of hardware costs or straightforward chunking with losing compression ratio. In order to trade off the parallelism and the compression ratio, this paper proposes a chunk‐oriented error modeling scheme for LOCO‐I, which enables parallelism in both compression and decompression and achieves a better compression ratio in chunks. Based on the optimized algorithm, a high‐throughput flexible lossless compression and decompression architecture (HFCD) is proposed, which achieves higher pixel per clock (PPC) with less hardware cost. Additionally, HFCD introduces a parameter sharing mechanism to enable random access of image chunks to improve the flexibility for decompression. Experimental results show that, compared with state‐of‐the‐art works, HFCD achieves 3.02–13.50 times improvement for the PPC of compression. For decompression, benefiting from our optimizations, HFCD achieves 22.4 times speedup compared to the software solution. [ABSTRACT FROM AUTHOR]

The unmanned aerial vehicle (UAV) city patrol is of great significance in ensuring the safety of residents' lives and properties, as well as maintaining the normal operation of the city. However, the detection of UAV images faces challenges such as numerous small-scale objects, complex backgrounds, and high requirements for detection speed. In response to these issues, we introduce a Real-time Small Object Detection network in UAV-vision (RTS-Net), tailored for UAV patrols. Initially, we introduce a multiscale feature fusion module (MFFM) designed to augment the expressiveness of features across scales, thereby enhancing the detection of smaller objects. Subsequently, leveraging attention mechanisms, we present the coordinated attention detection module (CADM), which bolsters the detection model's ability to accurately segregate objects from the background in expansive, complex scenarios. Lastly, a lightweight real-time feature extraction module (RFEM) is crafted to diminish model computational complexity and boost inference speed. On the UAV road patrol image dataset we constructed, our proposed method attains a detection accuracy of 89.9 % mAP, breaking previous records. It surpasses all prevailing detection methods, particularly for small-scale objects. Simultaneously, it achieves an inference speed of 163.9 FPS. The experimental results show that RTS-Net can satisfy the accurate and efficient detection of ground objects by various different UAV platforms in different complex scenarios. [ABSTRACT FROM AUTHOR]

In this paper, a fast L1 formula on tanh meshes is proposed for time fractional Burgers equations with Caputo fractional derivative. The solvability, boundness and convergence of the numerical scheme are rigorously established. Several numerical experiments are provided to support the theoretical results. The results of the experiments showed that the proposed numerical method cannot only effectively deal with the weak singularity of the problem near t = 0, but also significantly reduce the computational complexity of numerical simulation. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Low‐dose computed tomography (LDCT) can mitigate potential health risks to the public. However, the severe noise and artifacts in LDCT images can impede subsequent clinical diagnosis and analysis. Convolutional neural networks (CNNs) and Transformers stand out as the two most popular backbones in LDCT denoising. Nonetheless, CNNs suffer from a lack of long‐range modeling capabilities, while Transformers are hindered by high computational complexity.In this study, our main goal is to develop a simple and efficient model that can both focus on local spatial context and model long‐range dependencies with linear computational complexity for LDCT denoising.In this study, we make the first attempt to apply the State Space Model to LDCT denoising and propose a novel LDCT denoising model named Visual Mamba Encoder‐Decoder Network (ViMEDnet). To efficiently and effectively capture both the local and global features, we propose the Mixed State Space Module (MSSM), where the depth‐wise convolution, max‐pooling, and 2D Selective Scan Module (2DSSM) are coupled together through a partial channel splitting mechanism. 2DSSM is capable of capturing global information with linear computational complexity, while convolution and max‐pooling can effectively learn local signals to facilitate detail restoration. Furthermore, the network uses a weighted gradient‐sensitive hybrid loss function to facilitate the preservation of image details, improving the overall denoising performance.The performance of our proposed ViMEDnet is compared to five state‐of‐the‐art LDCT denoising methods, including an iterative algorithm, two CNN‐based methods, and two Transformer‐based methods. The comparative experimental results demonstrate that the proposed ViMEDnet can achieve better visual quality and quantitative assessment outcomes. In visual evaluation, ViMEDnet effectively removes noise and artifacts, while exhibiting superior performance in restoring fine structures and low‐contrast structural edges, resulting in minimal deviation of denoised images from NDCT. In quantitative assessment, ViMEDnet obtains the lowest RMSE and the highest PSNR, SSIM, and FSIM scores, further substantiating the superiority of ViMEDnet.The proposed ViMEDnet possesses excellent LDCT denoising performance and provides a new alternative to LDCT denoising models beyond the existing CNN and Transformer options. [ABSTRACT FROM AUTHOR]

Testing independence between high-dimensional random vectors is fundamentally different from testing independence between univariate random variables. Taking the projection correlation as an example, it suffers from at least three problems. First, it has a high computational complexity of O { n 3 (p + q) } ⁠ , where n , p and q are the sample size and dimensions of the random vectors; this limits its usefulness substantially when n is extremely large. Second, the asymptotic null distribution of the projection correlation test is rarely tractable; therefore, random permutations are often suggested as a means of approximating the asymptotic null distribution, which further increases the complexity of implementing independence tests. Third, the power performance of the projection correlation test deteriorates in high dimensions. To address these issues, the projection correlation is improved by using a modified weight function, which reduces the complexity to O { n 2 (p + q) } ⁠. We estimate the improved projection correlation with U -statistic theory. Importantly, its asymptotic null distribution is standard normal, thanks to the high dimesnionality of the random vectors. This expedites the implementation of independence tests substantially. To enhance the power performance in high dimensions, we propose incorporating a cross-validation procedure with feature screening into the projection correlation test. The implementation efficacy and power enhancement are confirmed through extensive numerical studies. [ABSTRACT FROM AUTHOR]

The particle swarm optimization-K-Means algorithm is proposed by the related researchers to improve the clustering accuracy of the K-Means algorithm. However, the particle swarm optimization-K-Means algorithm brings more burden to the computation, and the computational efficiency is low when dealing with large data sets. To solve this problem, a parallel particle swarm K-Means algorithm based on MapReduce with multi-threading is proposed. The algorithm performs parallel computation by dividing the particle swarm into several equal-sized sub-populations based on the number of available nodes in the cluster and distributing them to each node. It uses a multi-threaded execution in the evaluation stage, which has the highest computational complexity in the evolutionary process. Experiments show that although splitting the population will affect the optimization effect to some extent, the proposed still can effectively optimize the clustering results of the K-Means algorithm, and the computational efficiency is significantly improved compared with serial particle swarm optimization k-means algorithm and MapReduce-based non-multithreaded particle swarm optimization k-means algorithm, in the experiment with the largest dataset and a configuration of 16 nodes, the proposed algorithm is 58 times faster than the serial algorithm. Furthermore, the computing efficiency can be improved in the clusters with more CPU cores. [ABSTRACT FROM AUTHOR]

Managing the explosion of data from the edge to the cloud requires intelligent supervision, such as fog node deployments, which is an essential task to assess network operability. To ensure network operability, the deployment process must be carried out effectively regarding two main factors: connectivity and coverage. The network connectivity is based on fog node deployment, which determines the network's physical topology, while the coverage determines the network accessibility. Both have a significant impact on network performance and guarantee the network quality of service. Determining an optimum fog node deployment method that minimizes cost, reduces computation and communication overhead, and provides a high degree of network connection coverage is extremely hard. Therefore, maximizing coverage and preserving network connectivity is a non-trivial problem. In this paper, we propose a fog deployment algorithm that can effectively connect the fog nodes and cover all edge devices. Firstly, we formulate fog deployment as an instance of multi-objective optimization problems with a large search space. Then, we leverage Marine Predator Algorithm (MPA) to tackle the deployment problem and prove that MPA is well-suited for fog node deployment due to its rapid convergence and low computational complexity, compared to other population-based algorithms. Finally, we evaluate the proposed algorithm on a different benchmark of generated instances with various fog scenario configurations. Our algorithm outperforms state-of-the-art methods, providing promising results for optimal fog node deployment. It demonstrates a 50% performance improvement compared to other algorithms, aligning with the No Free Lunch Theorem (NFL Theorem) Theorem's assertion that no algorithm has a universal advantage across all problem domains. This underscores the significance of selecting tailored algorithms based on specific problem characteristics. [ABSTRACT FROM AUTHOR]

Steganalysis techniques play a crucial role in forensic investigation, aiding professionals in identifying digital images with unsafe content. This work proposes a novel and improved lightweight deep-learning convolution neural network architecture called CIRNet, which provides universal steganalysis capabilities. CIRNet leverages inverted residual blocks integrated with a self-attention mechanism that effectively minimises the detection error rate and the computational complexity for steganalysis. The inverted residual blocks include lightweight depth-wise and point-wise convolutions supplemented by a self-attention module. This integration strengthens the saliency of feature maps corresponding to the embedding regions while reducing the network's parameters and the total floating-point operations. Consequently, CIRNet achieves superior accuracy, making it an excellent choice for deployment on lightweight smart mobile systems. Experimental results validate the superiority of the proposed CIRNet over several state-of-the-art deep-learning networks commonly employed in steganalysis. The results further demonstrate the enhanced generalisation ability, particularly when applied to diverse datasets generated from BOSSbase 1.01 and BOWS2 using HUGO, WOW, and S-UNIWARD steganography methods with two distinct payloads. These findings establish CIRNet as an efficient and elite performer in the domain of lightweight steganalysis. [ABSTRACT FROM AUTHOR]

Hand pose estimation is a fundamental task in computer vision with applications in virtual reality, gesture recognition, autonomous driving, and virtual surgery. Keypoint detection often relies on deep learning methods and high-resolution feature map representations to achieve accurate detection. The HRNet framework serves as the basis, but it presents challenges in terms of extensive parameter count and demanding computational complexity due to high-resolution representations. To mitigate these challenges, we propose a lightweight keypoint detection network called LGCANet (Lightweight Ghost-Coordinate Attention Network). This network primarily consists of a lightweight feature extraction head for initial feature extraction and multiple lightweight foundational network modules called GCAblocks. GCAblocks introduce linear transformations to generate redundant feature maps while concurrently considering inter-channel relationships and long-range positional information using a coordinate attention mechanism. Validation on the RHD dataset and the COCO-WholeBody-Hand dataset shows that LGCANet reduces the number of parameters by 65.9% and GFLOPs by 72.6% while preserving the accuracy and improves the detection speed. [ABSTRACT FROM AUTHOR]

The Bayesian optimization algorithm uses Bayesian networks as the probability model of its solution space. Although the research on this algorithm has steadily developed, there are still some problems in its application process, such as excessive computational complexity. To solve various problems in Bayesian algorithm, reduce its computational complexity, and enable it to better achieve image segmentation. The study chooses to improve the Bayesian algorithm on the basis of immune algorithm, and solves the problem of computational complexity by reducing the number of Bayesian network construction times, thereby improving the individual fitness of the population. Through simulation experiments, it has been shown that the average number of times the improved Bayesian algorithm reaches the optimal value is 30, which is higher than the traditional algorithm's 20 times. Its excellent optimization ability searches for the optimal threshold to complete image segmentation. The improved Bayesian optimization algorithm based on immune algorithm can effectively reduce computational complexity, shorten computational time, and improve convergence. And applying Bayesian algorithm to image segmentation has broadened the application field of the algorithm and found new exploration directions for image segmentation. [ABSTRACT FROM AUTHOR]

Lunar missions are currently experiencing a significant surge in popularity, presenting expansive opportunities for further exploration and development. To thoroughly explore the design margins and potential of lunar landers, and to foster the development of overall designs driven by comprehensive performance objectives, it is crucial to conduct optimization design considering the coupling between key disciplines such as trajectory and propulsion. Considering the significant increase in computational complexity caused by conducting trajectory/propulsion integrated design optimization, the analytical target cascading method is employed to hierarchically decompose and coordinate optimization of the complex systems. This article presents a phased soft-landing strategy on the manned lunar lander propelled by hybrid rocket motors, utilizing powered explicit guidance and Apollo powered descent guidance, and proceeds with the trajectory/propulsion integrated design optimization involving diverse grain shapes and feed systems. This optimization process is separately undertaken utilizing multidisciplinary feasible method and analytical target cascading method. The analysis reveals that integrating trajectory and propulsion considerations into the optimization process facilitates a 5 % reduction in the overall mass relative to optimizations constrained solely by velocity increment and lack comprehensive trajectory design considerations. This highlights the profound impact of trajectory requirements on propulsion system design and the advantages of powered explicit guidance laws in minimizing fuel consumption. Crucially, the use of analytical target cascading achieves the better optimization results, and significantly reduces subsystem evaluation times, enhancing operational efficiency by 48 %, demonstrating the advantage in handling complex, large-scale systems. On another level, with different β values, the Mean Relative Error of the target values for the three schemes obtained by the analytical target cascading method is 0.0016, indicating good stability and strong robustness. The practical exploration in this article provides methods and frameworks for high-performance optimization design of complex aerospace mission profiles in the future. • The trajectory/propulsion integrated design optimization is conducted. • The two-phase guidance strategy for lunar lander descent is proposed. • Based on the strategy, the analytical target cascading-decomposed framework is set. • The influence of trajectory and propulsion coupling on overall design is revealed. • The optimization effectiveness, convergence rate, and robustness of the ATC is shown. [ABSTRACT FROM AUTHOR]

This work establishes a framework for solving inverse boundary problems with the geodesic-based quadratic Wasserstein distance (W_{2}). A general form of the Fréchet gradient is systematically derived from the optimal transportation (OT) theory. In addition, a fast algorithm based on the new formulation of OT on \mathbb {S}^{1} is developed to solve the corresponding optimal transport problem. The computational complexity of the algorithm is reduced to O(N) from O(N^{3}) of the traditional method. Combining with the adjoint-state method, this framework provides a new computational approach for solving the challenging electrical impedance tomography problem. Numerical examples are presented to illustrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Model order reduction technique provides an effective way to reduce computational complexity in large-scale circuit simulations. This paper proposes a new model order reduction method for delay circuit systems based on Hermite expansion technique. The presented method consists of three steps i.e., first the delay elements are approximated using the recursive relation of Hermite polynomials, then in the second step, the reduced order is estimated for the delay circuit system using a delay truncation in the Hermite domain and in the third step, a multi-order Arnoldi process is computed for obtaining the projection matrix. In the following, the reduced order delay circuit model is obtained by the projection matrix. Moment matching and passivity properties of the reduced circuit system are also analyzed. Two circuit examples with delay components are performed to verify the effectiveness of the proposed MOR approach. [ABSTRACT FROM AUTHOR]

Electronic equipment used on a daily basis now frequently includes active noise cancellation. The adaptive filters, which are positioned within, are essential for noise cancellation. An essential component to take into account for the overall performance is the structural and computational complexity of the filter. The filter's structure has an impact on this. The amount of taps determines the structure. Active noise cancellation filters often have set tap lengths and are lengthy, which causes sluggish convergence and delay. As a result, a trade-off between the filter's length and convergence is required. This is conceivable if there is a flexible filter with a tap length that adapts to the environment while still ensuring acceptable convergence. This study proposes a novel Minimum Mean M-estimate method with changeable tap length and uses a sub-band adaptive filtering technique to shorten the filter's length. In order to maximize the filter's efficiency, the advantages of three approaches are specifically merged in this work. They are the proposed algorithm, the proposed method's variable tap length variant, and the sub-band adaptive filtering. The simulation's findings and recommendations are supported. [ABSTRACT FROM AUTHOR]

Speech enhancement performance has improved significantly with the introduction of deep learning models, especially methods based on the Long–Short-Term Memory architecture. However, these methods face challenges such as high computational complexity and redundancy of input features. To address these issues, we propose a U-Net-based approach that utilizes an encoder/decoder to extract more concise features, thereby enhancing single-channel speech performance and reducing computation complexity. The proposed method includes a Cross-Swin-Transformer block and a masked bottleneck module, which down-samples features while preserving the detailed representation through skip connections and carefully designed blocks. The bottleneck module extracts coarse representations of hidden features as masks. We evaluated our method against other U-Net-based approaches on VCTK and DNS corpora using CBAK, eSTOI, PESQ, STOI, and SI-SDR metrics. The results demonstrate that the proposed method achieves promising performance while significantly reducing computational complexity. [ABSTRACT FROM AUTHOR]

In this paper, a sparse smooth twin support vector regression (Sparse‐STSVR) model for power amplifier (PA) behavioral modeling is obtained by pruning the kernel matrix based on Cholesky decomposition. Based on the primal smooth twin support vector regression (STSVR) model, the Nystrom approximate matrix of the kernel matrix is found to replace the original kernel matrix, thus simplifying the Newton iterative parameter extraction process of the primal STSVR model and accelerating the convergence of the algorithm. In addition, the new rank approximation kernel matrix has the characteristic of sparse parameters, which further reduces the computational complexity of the feedforward link of the digital predistorter. The 100 MHz 5G New Radio (NR) signal is used for verify the effect of PA modeling and digital predistortion (DPD) experiment. The results show that the proposed method can improve the normalized mean square error (NMSE) by about 2 ~ 3 dB with fewer coefficients compared with the previously proposed machine learning model, and the predistortion linearization effect improves by nearly 3 dB on the adjacent channel power ratio (ACPR), which achieves a good trade‐off between model performance and computational complexity. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

The multi-state flow network (MSFN) serves as a fundamental framework for real-life network-structured systems and various applications. The system reliability of the MSFN, denoted as Rd, is defined as the probability of successfully transmitting at least d units of demand from a source to a terminal. Current analytical algorithms are characterized by their computational complexity, specifically falling into the NP-hard problem to evaluate exact system reliability. Moreover, existing analytical algorithms for calculating Rd are basically designed for predetermined values of d. This limitation hinders the ability of decision-makers to flexibly choose the most appropriate based on the specific characteristics of the given scenarios or applications. This means that these methods are incapable of simultaneously calculating system reliability for various demand levels. Therefore, this paper develops a simulation-based algorithm to estimate system reliability for all possible demand levels simultaneously such that we can eliminate the need to rely on repeat procedures for each specified d. An experimental investigation was carried out on a benchmark network and a practical network to validate the effectiveness and performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the interval coupled Sylvester matrix equations which include the well-known (generalized) Sylvester and Lyapunov matrix equations in both real and interval forms. Assuming that certain matrices are simultaneously diagonalizable, we present a fast and efficient approach for enclosing the solution set of the interval coupled Sylvester matrix equations. Our approach, which is a modification of the Krawczyk operator, enables us to reduce the computational complexity considerably. Some numerical tests are given to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Graph matching is a commonly used technique in computer vision and pattern recognition. Recent data-driven approaches have improved the graph matching accuracy remarkably, whereas some traditional algorithm-based methods are more robust to feature noises, outlier nodes, and global transformation (e.g. rotation). In this paper, we propose a graph neural network (GNN) based approach to combine the advantage of data-driven and traditional methods. In the GNN framework, we transform traditional graph matching solvers as single-channel GNNs on the association graph and extend the single-channel architecture to the multi-channel network. The proposed model can be seen as an ensemble method that fuses multiple algorithms at every iteration. Instead of averaging the estimates at the end of the ensemble, in our approach, the independent iterations of the ensembled algorithms exchange their information after each iteration via a 1 × 1 channel-wise convolution layer. Experiments show that our model improves the performance of traditional algorithms significantly. In addition, we propose a random sampling strategy to reduce the computational complexity and GPU memory usage, so that the model is applicable to matching graphs with thousands of nodes. We evaluate the performance of our method on three tasks: geometric graph matching, semantic feature matching, and few-shot 3D shape classification. The proposed model performs comparably or outperforms the best existing GNN-based methods. [ABSTRACT FROM AUTHOR]

This article presents a self-adjustable branch-and-bound algorithm for globally solving a class of linear multiplicative programming problems (LMP). In this algorithm, a self-adjustable branching rule is introduced and it can continuously update the upper bound for the optimal value of LMP by selecting suitable branching point under certain conditions, which differs from the standard bisection rule. The proposed algorithm further integrates the linear relaxation program and the self-adjustable branching rule. The dependability and robustness of the proposed algorithm are demonstrated by establishing the global convergence. Furthermore, the computational complexity of the proposed algorithm is estimated. Finally, numerical results validate the effectiveness of the self-adjustable branching rule and demonstrate the feasibility of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Existing sea surface small target detection methods typically rely on intricate feature extraction techniques on transformed radar returns. However, these approaches suffer from issues of high computational complexity and low real-time performance. Temporal Convolutional Network (TCN) can enable direct processing of radar time-series echo data without the need for elaborate feature extraction, thus substantially improving computational efficiency. Building upon this, this paper presents a novel target detection algorithm based on Multi-layer Attention Temporal Convolutional Network (MA-TCN). The proposed algorithm processes the amplitude information in the original echo signals, and comprehensively extracts sequence feature information through the construction of stacked residual modules. Additionally, it integrates multi-layer attention mechanisms to adaptively adjust the output weights of each residual module, thereby further enhancing detection accuracy. Experimental results demonstrate that the proposed approach achieves significant improvements in both detection performance and efficiency compared to existing methods. [ABSTRACT FROM AUTHOR]

In recent years, the utilization of unmanned aerial vehicles (UAVs) for aerial target detection has gained significant attention due to their high-altitude perspective and maneuverability, which offer novel opportunities and tremendous potential in this field. However, detecting targets in UAV aerial images remains highly challenging due to the presence of numerous small targets with limited feature information, as well as issues like target occlusion and complex backgrounds that severely impact detection accuracy. To address these challenges, we propose a detection model called BDC-YOLOv8 that aims to enhance accuracy for small targets while minimizing computational complexity. Specifically, we augment the YOLOv8 architecture by incorporating a dedicated detection head tailored for small targets to improve performance when encountering such objects. Additionally, we restructure the neck network of the model to better extract and fuse feature information from targets with significant scale variations. Furthermore, we introduce the concept of DynamicHead to enhance the detection head by incorporating various attention mechanisms suitable for our task ahead of the original detection head, thereby enhancing the model's capability to detect objects of different scales and complex backgrounds. Moreover, we introduce Convolutional Block Attention Module (CBAM) to identify regions of interest in densely populated areas. Extensive experiments conducted on the VisDrone2019 dataset yield promising results where our model achieves a mean Average Precision (mAP) score of 38% and an AP50 score of 59.6%. Compared to the original YOLOV8 model, improvements are observed with increases in mAP by 2.5% and AP50 by 3.7%, respectively. Notably, our model demonstrates a significant enhancement in detecting small targets with an increase in APs evaluation metric by 4.1%. [ABSTRACT FROM AUTHOR]

Lattice metamaterials, a class of structures utilized for geometric lightweighting and enhancing structural integrity, feature interconnected elements arranged in specific patterns. These patterns confer unique properties beneficial for improving stiffness, damping, energy absorption, and strength across various applications. However, computational simulation of lattice structures for design and optimization presents significant challenges due to nonlinear and elastic/inelastic effects like instabilities, contacts, rate-dependence, and plasticity. Current methods heavily rely on finite element analysis (FEA), yet they entail high computational complexity and do not fully align with experimental observations. Moreover, additive manufacturing (AM) technologies introduce additional computational complexity due to process-induced anisotropic behaviors. This paper proposes a computational model to construct an effective descriptor by integrating metamaterial topological information with AM conditions. This descriptor serves as a surrogate for FEA models. Additionally, a database is established to correlate the descriptor with experimentally acquired mechanical responses of additively manufactured metamaterials. The bidirectional operation of the envisaged descriptor fulfills two objectives: informing the mechanical response of novel metamaterial models and guiding design and AM processes based on desired outcomes. Model validation demonstrates significant concurrence between predicted and experimental results, evidencing the model's capability to capture inherent nonlinearity in both design and process. [ABSTRACT FROM AUTHOR]

Point cloud semantic segmentation is a key step in 3D scene understanding and analysis. In recent years, deep learning-based point cloud semantic segmentation methods have received extensive attention from researchers. Multi-scale neighborhood feature learning methods are suitable for inhomogeneous density point clouds, but different scale branching feature learning increases the computational complexity and makes it difficult to accurately fuse different scale features to express local information. In this study, a point cloud semantic segmentation network based on RandLA-Net with multi-scale local feature aggregation and adaptive fusion is proposed. The designed structure can reduce computational complexity and accurately express local features. The mean intersection-over-union is improved by 1.1% on the SemanticKITTI data set with an inference speed of nine frames per second, while the mean intersection-over-union is improved by 0.9% on the S3DIS data set, compared with RandLA-Net. We also conduct ablation studies to validate the effectiveness of the proposed key structure. [ABSTRACT FROM AUTHOR]

This work investigates the less conservative stability conditions for linear systems with a time-varying delay. At first, augmented Lyapunov–Krasovskii functionals(LKFs) are constructed with state vectors that have not been utilized in the existing works, and an augmented zero equality that can be derived according to the augmented vector is proposed. By utilizing them, a stability condition is proposed in the form of a linear matrix inequality. And, by using novel delay-dependent LKFs and the introduced ones, improved results are obtained than the previous result. The addition of the delay-dependent LKFs increases the number of decision variables in the results. Therefore, any vectors of integral inequalities utilized in the proposed criterion are appropriately adjusted to reduce computational complexity. To check the excellence and validity of the proposed results, several numerical examples are applied. • The improved stability conditions for the linear system with a time-varying delay are proposed. • The proposed Lyapunov-Krasovskii functionals(LKFs) have more extended augmented vectors to obtain less conservative results. And LKFs based on an integral inequality (Lee et al., 2023) is utilized. • The novel LKFs which are the delay-dependent forms are proposed. The superiority of the results is checked according to the presence or absence of the forms. • The computational complexity is reduced by adjusting any vector of the integral inequality utilized in the proposed criteria. [ABSTRACT FROM AUTHOR]

Regarding the suppression of radar mainlobe jamming, a blind extraction method for suppressing mainlobe jamming based on maximizing the signal to interference plus noise ratio (SINR) is proposed. Unlike blind separation, blind extraction can extract interested components from multiple mixed signals, making it more suitable for jamming suppression in complex electromagnetic environments with multiple sources and channels. This method establishes an optimization model for maximizing SINR in the distance domain. The target echo signal is solved and extracted using the particle swarm optimization (PSO) algorithm to achieve mainlobe jamming suppression. Simulation testing shows that the proposed method improves the jamming suppression effect compared to traditional blind separation jamming suppression methods. It does not require estimation of the number of sources and has lower requirements for the number of channels, making it applicable in underdetermined scenarios. Additionally, it reduces computational complexity, making it more suitable for complex electromagnetic environments. [ABSTRACT FROM AUTHOR]

Deep graph clustering (DGC) has been a promising method for clustering graph data in recent years. However, existing research primarily focuses on optimizing clustering outcomes by improving the quality of embedded representations, resulting in slow-speed complex models. Additionally, these methods do not consider changes in node similarity and corresponding adjustments in the original structure during the iterative optimization process after updating node embeddings, which easily falls into the representation collapse issue. We introduce an Efficient Graph Auto-Encoder (EGAE) and a dynamic graph weight updating strategy to address these issues, forming the basis for our proposed Fast DGC (FastDGC) network. Specifically, we significantly reduce feature dimensions using a linear transformation that preserves the original node similarity. We then employ a single-layer graph convolutional filtering approximation to replace multiple layers of graph convolutional neural network, reducing computational complexity and parameter count. During iteration, we calculate the similarity between nodes using the linearly transformed features and periodically update the original graph structure to reduce edges with low similarity, thereby enhancing the learning of discriminative and cohesive representations. Theoretical analysis confirms that EGAE has lower computational complexity. Extensive experiments on standard datasets demonstrate that our proposed method improves clustering performance and achieves a speedup of 2–3 orders of magnitude compared to state-of-the-art methods, showcasing outstanding performance. The code for our model is available at https://github.com/Marigoldwu/FastDGC. Furthermore, we have organized a portion of the DGC code into a unified framework, available at https://github.com/Marigoldwu/A-Unified-Framework-for-Deep-Attribute-Graph-Clustering. [ABSTRACT FROM AUTHOR]

Network intrusion detection systems -NIDS are significant in potentially analyzing cyber-security and accurately categorizing attacks in current networks. Given the present circumstances, selecting an appropriate combination of anomaly detection features holds greater significance within NIDS. Diverse optimization algorithms offer a better solution: correctly choosing a near-optimal variety of features to achieve an improved NIDS. The Bacterial Forage Optimization (BFO) algorithm is an intelligent swarm algorithm commonly used for optimization problems. This paper proposes NIDS with optimal feature selection using Bacterial Forage Optimization techniques to detect the anomaly in the IDS (BFOFSIDS). Besides, it established Machine Learning-based Network Intrusion Detection and offered better performance for detecting User Remote (U2R Remote-to-Local (R2L), Probe and Denialof-service (DoS) attacks. The proposed model aims to identify anomaly detection features for an intrusion detection system, thus achieving a promising performance. The proposed method expands the BFO-based optimal features selection techniques, enhancing the accuracy among two high-dimensional intrusion detection datasets, such as NSLKDD and UNSW-NB15. Experimental results demonstrate that the proposed model achieves an accuracy of 91.8% on the NSL-KDD dataset and 91% on the UNSW-NB15 dataset. Additionally, the BFOFSIDS model significantly reduces the average processing time, with values recorded at 13,756 ms for NSL-KDD and 13,748 ms for UNSW-NB15, outperforming state-of-the-art methods. The precision and F-score for BFOFSIDS are also notably higher, indicating its effectiveness in improving detection accuracy while minimizing computational complexity. [ABSTRACT FROM AUTHOR]

Medical image encryption has currently drawn particular attention because of the sensitive nature of medical data, the lack of efficient image encryption employing innovative encryption methods. To enhance the encryption of medical images, a number of encryption approaches were suggested and created. This research proposes a hyperchaotic map as well as a deep learning (DL) based selective medical image encryption technique. Initially, the 3D_Att_ResU-Net segments more sensitive region of interest (ROI) which is the brain tumours. The process continues with a hyperchaotic map for encrypting the ROI of plain medical image. On the other hand, the region of non interest (RONI) is compressed based on discrete cosine transform (DCT). The encrypting of ROI as well as compression of the background could provide the best possible balance between encryption performance and security. The samples for the simulation included a variety of common medical image types as well as a few normal images. The outcomes have been analysed using standard image cryptanalysis techniques. The findings demonstrated that cipher-images had uniformly distributed histogram, high information entropy, low correlation between neighbouring pixels, and good visual quality. The method has a large key space as well as low time complexity, yet it is sensitive to the plain image and the initial key. Interestingly, it has been found that the values of NPCR and UACI are higher than 99.75% and 36. 53%, respectively besides large key space registered as an order of 2339. The entropy of the Region of Interest (ROI) is roughly estimated to be 8, which is significantly represent a better security for the current system. The estimated encryption time required for the ROI of size 8.09 KB is 0.3s and to compress the RONI of size 73.81 is 1.02s while the total time required to encrypt whole image of size 81.9 is 2.4737s making it suitable for real time applications. The results depict that the current presented encryption scheme gives high security with less computational time and complexity. [ABSTRACT FROM AUTHOR]