A prevalent cancer, skin cancer, requires precise segmentation for effective diagnosis and treatment. Despite the availability of numerous effective, lightweight neural network models, there remains a significant gap in their application in resource-limited settings, particularly in mobile healthcare. This study aims to bridge this gap by developing a model combining high efficiency and robust performance in such environments. This study introduces the Dynamic Spatial Group Enhanced Network (DSEUNet), significantly reducing the computational loads and parameter quantities while ensuring competitive segmentation accuracy . Our essential contribution is developing a model that excels in segmentation accuracy and addresses computational and parameter efficiency challenges in mobile healthcare applications. In extensive trials on the ISIC2017 and ISIC2018 datasets, DSEUNet not only surpasses traditional models like UNet in accuracy but does so with far fewer parameters and reduced computational demand. This research contributes to developing more accessible and efficient tools for medical image analysis, particularly in resource-constrained environments. As far as we know, this is the first model with 23.306 KB parameters and 9.497 M FLOPs.

Semisupervised object detection (SSOD) has garnered significant interest for its capability to enhance the detection performance by leveraging large amounts of unlabeled data. However, current SSOD methods primarily focus on detecting horizontal objects, with little research devoted to the detection of arbitrary-oriented objects in remote sensing images. Drawing inspiration from this limitation, this article proposes a semisupervised oriented object detection framework (S$^{2}$O-Det) to reduce annotation costs while improving detection performance in a semisupervised manner. Initially, the proposed task-consistent learning aims to alleviate the inconsistencies between classification and localization, which provides consistent confidence for the pseudolabels. Subsequently, the introduced coarse-to-fine sample mining employs dense prediction for pseudolabel assignment, adopting a divide-and-conquer approach to independently identify consistent and reliable labels for both classification and localization tasks. Finally, a probabilistic distillation loss ensures the harmonization of the probability distributions across the teacher and student feature domains, thereby reciprocally enhancing the learning competencies. Experimental results on the DOTA-v1.0 and DOTA-v1.5 datasets demonstrate that S$^{2}$O-Det achieves promising performance across different labeling ratios.

Grey wolf optimization (GWO) is a widely used meta-heuristic method. It has limited searching potential when solving the majority of function optimization problems. This paper proposes a new variant of GWO, named SCGWO, which combines GWO with an improved spread strategy and a chaotic local search (CLS) mechanism to overcome these performance limitations. In detail, a spread strategy is introduced into the basic GWO to change the search agent's ability to avoid the local optima, the global exploration capability, and the individual movement's randomness. Then, a CLS mechanism is adopted to accelerate the convergence rate of the evolving agents. This method's effectiveness is illustrated by comparing the proposed SCGWO method with various algorithms, including seven GWO variants and eight well-known state-of-the-art algorithms on a comprehensive set of benchmark functions with the type of unimodal, multimodal, and composition functions. The experimental results confirmed that the established SCGWO algorithm has apparent advantages in processing unimodal, multimodal, and composition functions. Additionally, the proposed algorithm was utilized for finding the approximate optimal feature subset when applied to the feature selection problems on a set of 32 real-world data sets from the UCI machine learning repository. The results show that the binary variant also reveals a very competitive performance in dealing with feature selection. Our findings and analysis suggest that the proposed method can be a very suitable tool for realizing the optimal solutions to global optimization and wrapper-based feature selection tasks.

Gradient-based optimizer (GBO) is a metaphor-free mathematic-based algorithm proposed in recent years. Encouraged by the gradient-based Newton's method, this algorithm combines with population-based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare-bones mechanism, an opposition-based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO-KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO-KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO-KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to https://aliasgharheidari.com for any guidance about the proposed GOMGBO method.

With the increasing demand for security in many sectors, such as defense and health systems, developing secure Internet of Things (IoT) networks is a matter of great urgency. Looking at a potential solution for secure IoT systems, we investigate the physical layer security of cooperative nonorthogonal multiple access (NOMA) systems. After decoding information signal, the idea that a strong IoT node can serve as a relay node for other weak IoT nodes in enhancing their signal reception reliability, is known as cooperative NOMA. We consider both single-antenna and multiantenna aided transmission scenarios, where the base station (BS) communicates with two IoT nodes of different strengths. In the multiantenna scenario, artificial noise (AN) is generated at the BS and the strong IoT node for improving the security of the system. In order to characterize the secrecy performance, we derive new exact expressions of the security outage probability for both the IoT nodes under both the single-antenna and multiantenna aided scenarios. For the single-antenna scenario, we show that the power optimization at the BS and the strong IoT node can enhance the secrecy performance to some extent. For this case, we further study the secrecy diversity order of the overall system, which is mainly determined by the IoT node with the worse channel condition. For the multiantenna scenario, we derive the asymptotic secrecy outage probability (SOP) when the number of antennas tends to infinity. Extensive simulations have been conducted to verify the accuracy and effectiveness of the proposed analytical derivations. The presented results verify that the security performance of the cooperative NOMA-based IoT network can be improved through an appropriate power control scheme and by generating AN at the BS and the strong IoT node. The simulation results further illustrate that the asymptotic SOP is close to the exact one.

In the industry 4.0 era, the visualization and real-time automatic monitoring of smart cities supported by the Internet of Things is becoming increasingly important. The use of filtering algorithms in smart city monitoring is a feasible method for this purpose. However, maintaining fast and accurate monitoring in complex surveillance environments with restricted resources remains a major challenge. Since the cognitive theory in visual monitoring is difficult to realize in practice, efficient monitoring of complex environments is accordingly hard to be achieved. Moreover, current monitoring methods do not consider the particularities of the human cognitive system, so the remonitoring ability of the process/target is weak in case of monitoring failure by the monitoring system. To overcome these issues, this article proposes a novel human short-long cognitive memory mechanism for video surveillance in smart cities. In this mechanism, a memory with a high reliability target is used as a “long-term memory,” whereas a memory with a low reliability target is used as a “short-term memory.” During the monitoring process, the “short-term memory” and “long-term memory” alternation strategy is combined with the stored target appearance characteristics, ensuring that the original model in the memory will not be contaminated or mislaid by changes in the external environment (occlusion, fast motion, motion blur, and background clutter). Extensive simulations showcase that the algorithm proposed in this article not only improves the monitoring speed without hindering its real-time operation but also monitors and traces the monitored target accurately, ultimately improving the robustness of the detection in complex scenery, and enabling its application to IoT-assisted smart cities.

Analyzing surveillance videos is mandatory for the public and industrial security. Overwhelming growth in computer vision fields has been made to automate the surveillance system in terms of human activity recognition, such as behavior analysis and violence detection (VD). However, it is challenging to detect and analyze the violent scenes intelligently to fulfill the notion of Industrial Internet of Things (IIoT)-based surveillance buoyed by constrained resources to reduce computational power. To tackle this challenge, in this article, an artificial intelligence enabled IIoT-based framework with VD-Network (VD-Net) is proposed. First, the input video frames are passed to light-weight convolutional neural network model for important information collection including humans or suspicious objects such as knives/guns. Upon suspicious object detection, an alert is generated as an earlier VD in IIoT network while the information is shared with concern departments. Only the frames with objects are forwarded to cloud for detail investigation where features are extracted using convolutional long short-term memory (ConvLSTM). The latter from ConvLSTM is propagated to gated recurrent unit for final VD. The conducted experiments and ablation study on the existing surveillance and nonsurveillance datasets empirically validate the effectiveness of the proposed VD-Net by improving 3.9% increase in the accuracy compared with the state-of-the-art VD methods.

Feature selection commonly refers to a process of using the candidate algorithm to detect the optimal feature sets during the preprocessing steps in machine learning and data mining. This procedure can optimize the dataset's features to be analyzed and maximize the classification performance based on the selected optimal feature combination. In this work, a hybridization model is developed and utilized to select optimal feature subset based on an innovative binary version of moth-flame optimizer and the K-Nearest Neighbor Classifier (KNN) for classification tasks. The proposed new technique, abbreviated as MFeature or ESAMFO, applies several strategies, including two types of transfer functions, ensemble strategy, simulated annealing (SA) disturbance mechanism, and crossover scheme to improve the equilibrium between the global exploration and local exploitation capabilities of the basic MFO. Each individual in the proposed algorithm is evaluated by the size of selected features and the error rate of the KNN classifier. The proposed model's efficacy is assessed on 30 benchmark datasets with different dimensions from the UCI repository and compared with other KNN based feature selection algorithms from the literature. The comprehensive results via various comparisons reveal the efficiency of the proposed technique in decreasing the classification error rate compared with other feature selection algorithms, ensuring the capability of ESAMFO in exploring the feature space and selecting the most informative features for classification purposes. For post publications that support this research, readers can refer to https://aliasgharheidari.com .