Low resolution remote sensing object detection with fine grained enhancement and swin transformer by Zhijing Xu, Xin Wang, Kan Huang, Ren Chen

“…These results highlight its superior detection performance under low-resolution conditions.…”
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Neural Networks Detection of Low-Amplitude Components on ECG Using Modified Wavelet Transform by A. V. Mnevets, N. G. Ivanushkina

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MSF-GhostNet: Computationally Efficient YOLO for Detecting Drones in Low-Light Conditions by Maham Misbah, Misha Urooj Khan, Zeeshan Kaleem, Ali Muqaibel, Muhamad Zeshan Alam, Ran Liu, Chau Yuen

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Enhanced Magnetic Wireless Sensor Network Algorithm for Traffic Flow Monitoring in Low-Speed Congested Traffic by Haji Said Fimbombaya, Nerey H. Mvungi, Ndyetabura Y. Hamisi, Hashimu U. Iddi

“…One of the challenges facing such deployment is the development of effective detection signal-processing algorithm in low-speed congested traffic based on the Earth’s magnetic fields. …”
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Enhanced intrusion detection model based on principal component analysis and variable ensemble machine learning algorithm by Ayuba John, Ismail Fauzi Bin Isnin, Syed Hamid Hussain Madni, Farkhana Binti Muchtar

“…Several IDS models have various security problems, such as low detection accuracy and high false alarms, which can be caused by the network traffic dataset's excessive dimensionality and class imbalance in the creation of IDS models. …”
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LR-FHSS Transceiver for Direct-to-Satellite IoT Communications: Design, Implementation, and Verification by Sooyeob Jung, Seongah Jeong, Jinkyu Kang, Gyeongrae Im, Sangjae Lee, Mi-Kyung Oh, Joon Gyu Ryu, Joonhyuk Kang

“…Moreover, we apply a robust synchronization scheme against the Doppler effect and co-channel interference (CCI) caused by LEO satellite channel environments, including signal detection for the simultaneous reception of numerous frequency hopping signals and an enhanced soft-output-Viterbi-algorithm (SOVA) for the header and payload receptions. …”
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MEFA-Net: Multilevel Feature Extraction and Fusion Attention Network for Infrared Small-Target Detection by Jingcui Ma, Nian Pan, Dengyu Yin, Di Wang, Jin Zhou

“…Infrared small-target detection encounters significant challenges due to a low image signal-to-noise ratio, limited target size, and complex background noise. …”
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Loss reduction optimization strategies for medium and low-voltage distribution networks based on Intelligent optimization algorithms by Nian Liu, Yuehan Zhao

“…Methodology In order to reduce line losses, a loss optimization model for low and medium voltage distribution networks based on an improved Gray Wolf optimization support vector machine is proposed. …”
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Malware detection approach based on improved SOINN by Bin ZHANG, Lixun LI, Shuqin DONG

“…To deal with the problems of dynamic update of detection model and high computation costs in malware detection model based on batch learning,a novel malware detection approach is proposed by combing SOINN and supervised classifiers,to reduce computation costs and enable the detection model to update dynamically with the assistance of SOINN′s incremental learning characteristic.Firstly,the improved SOINN was given.According to the whole alignment algorithm,search the adjusted weights of neurons under all input sequences in the learning cycle and then calculate the average value of all adjusted weights as the final result,to avoid SOINN′s stability under different input sequences and representativeness of original data,therefore improve malware detection accuracy.Then a data preprocessing algorithm was proposed based on nonnegative matrix factor and Z-score normalization to transfer the malware behavior feature vector from high dimension and high order to low dimension and low order,to speed up and avoid overfitting and further improve detection accuracy.The results of experiments show that proposed approach supports dynamic updating of detection model and has a significantly higher accuracy of detecting unknown new samples and lower computation costs than tradition methods.…”
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Campus risk detection using the S-YOLOv10-SIC network and a self-calibrated illumination algorithm by Qiang Zhao, Sha Liu, Shihao Zhang, Baijuan Wang

“…The self-calibrated illumination algorithm is integrated to enhance the detection performance of the model under low light conditions. …”
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Weight-Based Clustering Decision Fusion Algorithm for Distributed Target Detection in Wireless Sensor Networks by Haiping Huang, Lei Chen, Xiao Cao, Ruchuan Wang, Qianyi Wang

“…However, the current judgment fusion rules such as Counting Rule (CR) and Clustering-Counting Rule (C-CR) have the characteristics on high energy consumption and low detection precision. Consequently, this paper proposes a novel Weight-based Clustering Decision Fusion Algorithm (W-CDFA) to detect target signal in wireless sensor network. …”
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Series Arc Fault Detection Based on Improved Artificial Hummingbird Algorithm Optimizer Optimized XGBoost by Lichun Qi, Takahiro Kawaguchi, Seiji Hashimoto

“…Based on the wide variety of electrical appliances, it is difficult to detect similar current waveforms when different appliances experience arc faults due to insufficient extraction of fault arc characteristics and low detection accuracy. …”
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DECISION TREE WITH HILL CLIMBING ALGORITHM BASED SPECTRUM HOLE DETECTION IN COGNITIVE RADIO NETWORK by N Suganthi, R Meenakshi, A Sairam, M Parvathi

“…The approach integrates a Decision Tree (DT) algorithm for rapid initial classification of Primary User (PU) activity, followed by a Hill Climbing (HC) optimization algorithm that fine-tunes the detection based on a fitness function. …”
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Anomaly Detection and Performance Analysis With Exponential Smoothing Model Powered by Genetic Algorithms and Meta Optimization by Ali Kerem Guler, Huseyin Fuat Alsan, Taner Arsan

“…The proposed approach, which achieved scores of 54.41 for ‘Standard’, 53.95 for ‘reward_low_FP_rate’, and 69.61 for ‘reward_low_FN_rate’, indicates improvements of 3.67%, 4.45%, and 2.63%, respectively, compared to the average scores of the NAB algorithms. …”
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Detection of <i>Aspergillus flavus</i> in Figs by Means of Hyperspectral Images and Deep Learning Algorithms by Cristian Cruz-Carrasco, Josefa Díaz-Álvarez, Francisco Chávez de la O, Abel Sánchez-Venegas, Juan Villegas Cortez

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Local outlier factor algorithm based on correction of bidirectional neighbor by Xiaohui YANG, Xiaoming LIU

“…A local outlier factor algorithm based on bidirectional neighbor correction was proposed to solve the problems of existing outlier detection algorithms such as difficulty in parameter selection,poor efficiency and low accuracy.The bidirectional neighbor searching algorithm was used to reduce the neighbor search time.Then the bidirectional neighbor pruning algorithm was used to reduce the number of parameters and unnecessary calculations.And the correction factor based on bidirectional neighbors was used to improve the calculation accuracy.Experimental results show that the proposed algorithm has better performance in parameter selection and time efficiency than other outlier detection methods.The correction factor improves the accuracy of the algorithm,in the synthetic data set and UCI data set.…”
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Quantitative study on weak magnetic detection defects of metal structure based on IWOA-BP algorithm by FAN Meng, TONG Bo, GAO Chen, YAO Zhongyuan, ZHANG Yu, HU Bo

“…In view of the poor effect and low efficiency of BP neural network in parameter adjustment, the improved whale optimization algorithm (IWOA) based on Sine chaotic mapping was adopted to optimize the BP neural network parameter adjustment mode, giving consideration to global optimization while improving the local optimization ability, and then the optimal parameters searched by IWOA were assigned to BP neural network, improving the quality of initial network parameters.The length, width and depth of the artificial rectangular slot were quantified by inversion. …”
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Optimizing Feature Selection and Machine Learning Algorithms for Early Detection of Prediabetes Risk: Comparative Study by Mahmoud B Almadhoun, MA Burhanuddin

“…This study aims to compare the effectiveness of machine learning (ML) algorithms in predicting prediabetes and identifying its key clinical predictors. …”
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Multi-User Detection Algorithm for Asynchronous Non-Orthogonal Multiple Access Based on Gaussian Approximation by Peisen WANG, Yueneng WANG, Boming CHENG, Yue YANG, Neng YE

“…Code domain non-orthogonal multiple access (NOMA) is one of the potential technologies to realize ubiquitous access in the future satellite internet.Considering that satellite-to-ground transmission has the characteristics of large propagation delay and high channel dynamics, strict timing synchronization between ground devices will lead to unbearable time resource overhead.Loosed synchronization relationship are required to achieve multi-user access in satellite internet.However, most of the existing multi-user detection algorithms in code domain NOMA are based on the assumption of synchronous transmission, which cannot accurately model and eff ectively alleviate the complex interference introduced by asynchronous multi-user transmission.Therefore, a novel multi-layer factor graph model by introduced the relative delay among users into the traditional two-part factor graph was constructed, and a low-complexity asynchronous multi-user detection algorithm based on gaussian approximation was proposed.The proposed algorithm iterated between each user's estimates and propagated the estimated information between users to improved the approximation.Simulation results showed that in high SNR region, the performance of the asynchronous code-domain NOMA with the proposed algorithm even outperforms its synchronous counterpart.…”
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AS-Faster-RCNN: An Improved Object Detection Algorithm for Airport Scene Based on Faster R-CNN by Zhige He, Yuanqing He

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