Deep learning has become a research hotspot in the field of network intrusion detection. In order to further improve detection accuracy and performance, we proposed an model based on improved deep belief (DBN). Traditional neural training methods, like Back Propagation (BP), start train with preset parameters such as randomly initialized weights thresholds, which may bring some issues, e.g., attracting local optimal solutions, or requiring long period. We use Kernel-based Extreme Learning...

Intrusion detection ensures that IoT can protect itself against malicious intrusions in extensive and intricate network traffic data. In recent years, deep learning has been extensively effectively employed intrusion detection. However, the limited computing power storage space of devices restrict feasibility deploying resource-intensive systems on them. This article introduces DL-BiLSTM lightweight model. By combining neural networks (DNNs) bidirectional long short-term memory (BiLSTMs),...

There are many tricky optimization problems in real life, and metaheuristic algorithms the most effective way to solve at a lower cost. The dung beetle algorithm (DBO) is more innovative proposed 2022, which affected by action of beetles such as ball rolling, foraging, reproduction. Therefore, A based on quasi-oppositional learning Q-learning (QOLDBO). First, quantum state update idea cleverly integrated into increase randomness generated population. And best behavior pattern selected adding...

For the wireless sensor networks (WSNs) heterogeneous node deployment optimization problem with obstacles in monitoring area, two new flower pollination algorithms (FPA) are proposed to deploy network. Firstly, an improved algorithm (IFPA) is based on FPA, aiming at shortcomings of convergence speed slow and precision not high enough FPA. The nonlinear factor designed correct scaling Tent chaotic map effectively maintains diversity population late iteration, a greedy crossover strategy...

The non-destructive detection of soluble solids content (SSC) in fruit by near-infrared (NIR) spectroscopy has a good application prospect. At present, the portable devices is more common. construction an accurate and stable prediction model key for successful device. In this study, visible (Vis/NIR) spectra Korla fragrant pears were collected commercial measurement Different pretreatment methods used to preprocess raw spectra, partial least squares (PLS) was constructed predict SSC...

Offline reinforcement learning (RL), which aims to learn an optimal policy using a previously collected static dataset, is important paradigm of RL. Standard RL methods often perform poorly in this regime due the function approximation errors on out-of-distribution actions. While variety regularization have been proposed mitigate issue, they are constrained by classes with limited expressiveness that can lead highly suboptimal solutions. In paper, we propose representing as diffusion model,...

Topology impacts important network performance metrics, including link utilization, throughput and latency, is of central importance to operators. However, due the combinatorial nature topology, it extremely difficult obtain an optimal solution, especially since topology planning in networks also often comes with management-specific constraints. As a result, local optimization hand-tuned heuristic methods from human experts adopted practice. Yet, cannot cover global design space while taking...

Objectives Hepatic osteodystrophy (HOD) is an important public health issue that severely affects human health. The pathogenesis of HOD complex, and exposure to environmental pollutants plays role. Di-(2-ethylhexyl) phthalate (DEHP) a persistent endocrine toxicant present in many products, the liver target organ for its toxic effects. Our research aimed investigate effects DEHP on HOD, reveal underlying mechanisms potential key preventive approaches. Methods daily intake EDI bone density...

It is feasible and safe to use unmanned aerial vehicle (UAV) as the data collection platform of Internet things (IoT). In order save energy loss make UAV perform work effectively, it necessary optimize deployment UAV. The objective problem minimize sum lost transmission devices. key solving calculate location docking points number when working collect data. This paper proposes a coding scheme based on swarm intelligence optimization, which encapsulates position into dimension, so be...

Cutting-edge embedded system applications, such as self-driving cars and unmanned drone software, are reliant on integrated CPU/GPU platforms for their DNNs-driven workload, perception other highly parallel components. In this work, we set out to explore the hidden performance implication of GPU memory management methods architecture. Through a series experiments micro-benchmarks real-world workloads, find that under different may vary according application characteristics. Based...

Lifetime is a vital performance metric for wireless sensor networks (WSNs), with current hierarchical routing protocols suffering from the premature death of overloaded nodes, affecting network's lifetime. Spurred by this problem, we propose low-energy first electoral multipath alternating multihop (LEMH) scheme. To avoid overloading these are given contention radius that adaptively changes topology and affords nodes to combine remaining energy vote those participating in cluster heads...

Phycobilisomes and chlorophyll-a (Chla) play important roles in the photosynthetic physiology of red macroalgae serve as primary light-harvesting antennae reaction center for photosystem II. Neopyropia is an economically macroalga widely cultivated East Asian countries. The contents ratios 3 main phycobiliproteins Chla are visible traits to evaluate its commercial quality. traditional analytical methods used measuring these components have several limitations. Therefore, a high-throughput,...

The combination of deep learning and intrusion detection has become a hot topic in today’s network security. In the face massive, high-dimensional traffic with uneven sample distribution, how to be able accurately detect anomalous is primary task detection. Most research on systems based focused supervised learning; however, process obtaining labeled data often requires lot time effort, as well support experts. Therefore, it worthwhile investigating development label-free self-supervised...