NID-DETR: A novel model for accurate target detection in dark environments by Qingyuan Pan, Qiang Liu, Wei Huang

“…Furthermore, existing vision Transformer models demonstrate high computational complexity, indicating a need for further optimization and enhancement. …”
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Deep learning-based ensemble stacking for enhanced intrusion detection in IoT-edge platforms by P. R. Chithra Rani, K. Baalaji

“…Abstract The ever-rising deployment of Internet of Things (IoT) applications has thrown new security challenges primarily due to the complexity of network and resource constraints on an edge platform. …”
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Fast SVM-based Multiclass Classification in Large Training Sets by M. Yu. Kurbakov, V. V. Sulimova

“…Classical Support Vector Machines (SVM) is a popular, convenient and well-interpreted classification method, but it has a high computational complexity of a training stage in a nonlinear case and a low data parallelism. …”
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LPCF-YOLO: A YOLO-Based Lightweight Algorithm for Pedestrian Anomaly Detection with Parallel Cross-Fusion by Peiyi Jia, Hu Sheng, Shijie Jia

“…To address the issue of high complexity in current pedestrian anomaly detection network models, which hinders real-world deployment, this paper proposes a lightweight anomaly detection network called LPCF-YOLO (Lightweight Parallel Cross-Fusion YOLO) based on the YOLOv8n model. …”
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Enhancing the Efficiency of Unsupervised Network Alignment Using Quotient Graph by Lei Zhang, Feng Qian

“…To address these limitations, this paper proposes ENAMOR (Efficient Network AlignMent via Quotient gRaph), an unsupervised framework incorporating three key components: 1) multi-scale representation learning that hierarchically aggregates local and global structural patterns through GNN layers; 2) embedding-driven graph coarsening via hashing-based quotient graph construction, reducing computational complexity by 60–80% while preserving topological and attribute information; and 3) Matched Neighborhood Consistency (MNC) optimization, which iteratively refines alignment matrices by enforcing structural congruence constraints. …”
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Predictability of Lifetime Nonsuicidal Self-injury by Symptoms of Sleep Disorders Using a Neural Network Model by Shakiba Rezaei, Azita Chehri, Saeede Sadat Hosseini, Mokhtar Arefi, Hassan Amiri

“…Future research can test the complexity of sleep disorders connected to NSSI comorbid with other psychiatric conditions.…”
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YOLO-APDM: Improved YOLOv8 for Road Target Detection in Infrared Images by Song Ling, Xianggong Hong, Yongchao Liu

“…The design requirements of the high-precision detection of infrared road targets were achieved while considering the requirements of model complexity control.…”
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Implementation of Image Enhancement and Edge Detection Algorithm on Diabetic Retinopathy (DR) Image Using FPGA by Mumtahina Orthy, Sheikh Md. Rabiul Islam, Faijah Rashid, Md. Asif Hasan

“…The blood vessels of the retina, a layer of light-sensitive tissue located at the posterior aspect of the ocular globe, are adversely impacted. …”
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Experimental demonstration of third-order memristor-based artificial sensory nervous system for neuro-inspired robotics by See-On Park, Hakcheon Jeong, Seokho Seo, Youna Kwon, Jongwon Lee, Shinhyun Choi

“…Incorporating an additional resistive switching TiOx layer into the HfO2 memristor exhibits third-order switching complexity and non-volatile habituation characteristics. …”
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Robust Model Predictive Control-Based Recurrent Neural Networks for Autonomous Vehicles in Avoidance Collisions by Hung Duy Nguyen, Duc Thinh Le, Tung Lam Nguyen, Minh Nhat Vu

“…However, due to its computational complexity, we employ a data-driven approach by collecting measurements under different road adhesion conditions to train deep neural networks with a long short-term memory layer (DNN-LSTM). …”
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Multi-Hop Upstream Anticipatory Traffic Signal Control With Deep Reinforcement Learning by Xiaocan Li, Xiaoyu Wang, Ilia Smirnov, Scott Sanner, Baher Abdulhai

“…Although agent communication using neural network-based feature extraction can implicitly enhance spatial awareness, it significantly increases the learning complexity, adding an additional layer of difficulty to the challenging task of control in deep reinforcement learning. …”
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A Learning Probabilistic Boolean Network Model of a Manufacturing Process with Applications in System Asset Maintenance by Pedro Juan Rivera Torres, Chen Chen, Sara Rodríguez González, Orestes Llanes Santiago

“…Probabilistic Boolean Networks (PBN) can model the dynamics of complex biological systems, as well as other non-biological systems like manufacturing systems and smart grids. …”
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MCFNet: Multi-Scale Contextual Fusion Network for Salient Object Detection in Optical Remote Sensing Images by Jinting Ding, Yueqian Quan, Honghui Xu

“…By facilitating cross-layer interactions and adopting a multiscale refinement strategy, CIM enriches texture representations while suppressing background interference, leading to smoother object boundaries and more precise delineation of salient regions. …”
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Maksimovka I Grave Field (Forest-Steppe Volga Region): Results of the 2019 Excavations by Arkadii I. Korolev, Anton A. Shalapinin

“…To facilitate this, the paper shall describe and characterize the investigated archaeological complexes, establish their cultural and chronological attributions. …”
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Secure edge-based smart grid communication using lightweight authentication modeling with autoencoders and real-world data by Omar Abdullah Saleh, Mesut Cevik

“…This paper presents a light-weight authentication scheme based on a five-layer deep autoencoder for anomaly-based authentication, facilitating secure and efficient communication in resource-limited edge-based smart grids. …”
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Enhanced Channel Estimation for RIS-Assisted OTFS Systems by Introducing ELM Network by Mintao Zhang, Zhiying Liu, Li Wang, Wenquan Hu, Chaojin Qing

“…Nevertheless, the integration of RIS into OTFS systems increases the complexity of channel estimation (CE). Utilizing the benefits of machine learning (ML) to address such intricate issues holds the potential to reduce CE complexity. …”
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A Deep Learning Algorithm for Multi-Source Data Fusion to Predict Effluent Quality of Wastewater Treatment Plant by Shitao Zhang, Jiafei Cao, Yang Gao, Fangfang Sun, Yong Yang

“…The operational complexity of wastewater treatment systems mainly stems from the diversity of influent characteristics and the nonlinear nature of the treatment process. …”
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Effective feature selection based HOBS pruned- ELM model for tomato plant leaf disease classification. by M Amudha, K Brindha

“…The classification module integrates a Hessian-based Optimal Brain Surgeon (HOBS) approach with a pruned Extreme Learning Machine (ELM), optimizing network parameters while reducing computational complexity. The proposed pruned model gives an accuracy of 95.73%, Cohen's kappa of 0.81%, training time of 2.35sec on Plant Village dataset, comprising 8,000 leaf images across 10 distinct classes of tomato plant, which demonstrates that this framework effectively reduces the model's size of 9.2Mb and parameters by reducing irrelevant connections in the classification layer. …”
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A Lightweight GCT-EEGNet for EEG-Based Individual Recognition Under Diverse Brain Conditions by Laila Alshehri, Muhammad Hussain

“…The proposed model maintains low parameter complexity while keeping the expressiveness of representations, even with unseen subjects.…”
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Hierarchical proxy consensus optimization for IoV based on blockchain and trust value by Baoqin ZHAI, Jian WANG, Lei HAN, Jiqiang LIU, Jiahao HE, Tianhao LIU

“…With the rapid development of Internet of vehicles, 5G and artificial intelligence technologies, intelligent transportation has become the development trend of transportation technology.As a vehicle-vehicle and vehicle-road information interaction platform, the Internet of vehicles is the basic support platform for intelligent traffic information sharing and processing.At the same time, the security of Internet of vehicles has attracted much attention, especially data security which may cause user privacy leakage.The blockchain technology has become a solution, but it still faces new challenges in efficiency, security and other aspects.With the increase of vehicle nodes and information, how to efficiently achieve information consensus in high-speed vehicle moving environment has become a key problem.Then a bottom-up RSU (road side unit) chain consensus protocol was proposed based on blockchain and trust value.Several typical consensus structures were compared, and bottom-up two-layer consensus structure was adopted according to the actual scenarios of the Internet of vehicles.Moreover, a group leader node election algorithm was proposed which is based on node participation, work completion and message value.The system security was ensured by assigning trust value to each vehicle.Following the consensus structure and algorithm work mentioned above, the specific process of the protocol was comprehensively described, which was divided into six steps: region division, group leader node selection, local consensus, leader primary node selection, global consensus, and intra-domain broadcast.Then the experiments were analyzed from four aspects: security, communication complexity, consensus algorithm delay and fault tolerance rate.Experiments showed that, compared with other schemes, the proposed protocol can effectively reduce communication complexity and shorten consensus delay under the condition of resisting conspiracy attack, witch attack and other attacks.On the premise of security, the protocol improves fault tolerance rate and enables more nodes to participate in information sharing to satisfy the requirements of Internet of vehicles scenarios.…”
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