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Fault diagnosis of high-frequency synchronous full-power data based on multi-source data acquisition and deep neural network
To meet the need for high-frequency synchronous full-power data fault diagnosis in new power systems, this study proposes an innovative method combining multi-source data acquisition technology and deep neural networks for accurate power system fault identification and efficient fault location. Firstly, it integrates multi-source heterogeneous data from WAMS, SCADA, and meteorological sensors to form a holistic sensing network covering electrical parameters, environmental conditions, and equipment operating conditions, creating a multi-dimensional feature space. Secondly, deep neural networks extract features and recognize patterns in the collected full-power data to identify fault types, locate faults, and analyze fault causes. Finally, the research shows that this method has made significant breakthroughs in data synchronization accuracy, diagnosis accuracy, and adaptability to complex scenarios.
Research on Multi-objective Engineering Project Resource Optimal Allocation and Schedule Collaborative Management Model Based on NSGA-Ⅲ Algorithm
This study focuses on the impact of climate change on agricultural production. By comprehensively analyzing the temperature fluctuation and crop yield data in the past decade, and the significant correlation between temperature rise and crop yield, a generative adversarial network model of multi-objective optimization strategy is proposed, which is dedicated to the prediction of safety accident risks in architectural engineering. By optimizing the architecture of GAN, the model enhances its adaptability and effectiveness in practical engineering risk prediction scenarios. The experimental results show that compared with the traditional prediction model, the accuracy rate of this model in safety risk prediction of large-scale construction projects is as high as 92%, far exceeding the accuracy rate of the traditional model of 78%. The model also shows good predictive ability on key performance indicators such as recall rate and F1 score, reaching 90% and 86%, respectively. It can effectively prove the significant advantages of the model based on multi-objective optimization GAN in the field of safety incident risk prediction in architectural engineering.
Computer-Assisted Multimodal Translanguaging Analysis in English Classrooms: A Deep-Learning and NLP Framework
Contemporary “all-English” instructional models often marginalize learners’ mother tongues, impeding both comprehension and participation in English-medium classrooms. This paper introduces an end-to-end computer-assisted multimodal framework for the automatic analysis and assessment of translanguaging practices—instances where learners dynamically interweave native-language elements into English discourse. Our system integrates (1) multimodal teaching materials (video, audio, text) designed via expert-driven instructional design; (2) a speech-to-text pipeline for capturing student utterances; (3) NLP preprocessing modules for tokenization, part-of-speech tagging and interlingual code-switch detection; and (4) a fine-tuned BERT-based semantic analyzer that quantitatively scores fluency and naturalness in cross-language segments. We evaluated the platform with a controlled study involving 120 secondary-level learners: the experimental group (multimodal–deep-learning support) outperformed controls by an average of 3.61 % (86 vs. 83 points) across fluency, lexical sophistication and pragmatic appropriateness metrics. These results demonstrate that coupling multimodal interfaces with deep NLP models yields measurable gains in both receptive and productive language skills. Our contributions span cognitive linguistics (quantitative modeling of translanguaging), educational informatics (design of adaptive multimodal content), and applied machine learning (novel BERT-based cross-language evaluator), forging a replicable path for data-driven language teaching and assessment.
Research on English Listening Comprehension Model Design Based on Transformer-ResNet Hybrid Model
With the increasing complexity of English listening comprehension tasks, the traditional single acoustic model has made it difficult to cope with the high noise interference and multi-level semantic understanding requirements in complex speech environments. Based on the research on the design of the English listening comprehension model based on the Transformer-ResNet hybrid model, an innovative architecture combining residual convolutional network and self-attention mechanism is proposed, aiming to improve the model's performance in long-term dependency modeling and local acoustic pattern recognition. A parallel dual-stream feature extraction architecture is designed, using ResNet to extract fine-grained acoustic features and the Transformer self-attention mechanism to capture long-term semantic dependencies. In order to solve the alignment problem between phoneme-level and semantic-level features, a cross-layer connection strategy is proposed, and the robustness of the model is improved by multi-scale feature fusion. Due to the limitation of real-time and computing resources, model compression and distillation technology are adopted to optimize computing efficiency, and an efficient end-to-end speech understanding system is realized by combining the pre-trained language model. The optimized hybrid model performed outstandingly on the test set, with an overall accuracy rate of 78.9%, an increase of 10.1 percentage points compared with the baseline model. The Transformer module for long-term dependence modeling contributed a 32% performance gain. At the same time, ResNet's local feature extraction capability enabled the model to maintain a time series consistency score of 66.6 in 44 sets of consecutive speech frame processing. It is worth noting that the model can still maintain a low word error rate of 9.8 in 87% of multi-speaker scenarios, indicating its robustness advantage in complex auditory environments. The experimental data verify the effectiveness of the complementary design of Transformer and ResNet in improving English listening comprehension tasks.
Artificial Intelligence and Automated Monitoring of Marine Wind Power and Ecosystems
At present, there is a lack of intelligent means to monitor the impact of marine wind power on ecosystems, and there is an urgent need to develop artificial intelligence-assisted monitoring technology. This paper discusses the application of artificial intelligence in marine wind power and ecosystem monitoring, focusing on technologies such as intelligent sensing, data collection, deep learning-driven monitoring models, intelligent decision-making and adaptive control systems. Marine sensor network optimization and anti-jamming underwater communication protocols based on edge computing improve the stability and reliability of data acquisition. This research paper focuses on developing and applying advanced artificial intelligence technologies for the intelligent monitoring of marine wind power and ecosystems. The novelty lies in the integration of multiple AI - enabled techniques, such as intelligent sensing, deep - learning - driven monitoring models, and intelligent decision - making systems, to address the complex challenges in real - time monitoring, fault diagnosis, and ecological impact assessment of marine wind farms. The wind farm can generate 870,000 kilowatt-hours of electricity annually, with an energy conversion efficiency of 16.3%. Although the conversion efficiency is relatively low, it has shown outstanding results in ecological protection. Through intelligent monitoring equipment, the protected area of marine life habitats has increased by 55%, while the survival rate of endangered species has been dramatically improved from the original 33% to 72.1%. These results were obtained through extensive experiments conducted at an actual marine wind farm equipped with our intelligent monitoring system. Specifically, the 42.5 MW wind turbines and automatic monitoring systems were installed in the test area, and data was collected continuously over a period of 12 months. The collected data was then analyzed using the methods described in the following sections to obtain the presented numerical results, such as the energy conversion efficiency, the increase in the protected area of marine life habitats, and the improvement in the survival rate of endangered species.
Research on Intelligent Stress Analysis and Safety Assessment Model of BIM-FEM Formwork Support System
With the rapid development of the construction industry, the safety and stability requirements of formwork support systems during construction have become increasingly strict. The purpose of this study is to integrate BIM (Building Information Modeling) and FEM (Finite Element Method) to develop an intelligent force analysis and safety assessment model to improve the design and construction safety of formwork support systems. Through BIM technology, the accurate modelling of the bracket system is realized and combined with FEM for force analysis, the model shows high-precision performance under various load conditions, and the force analysis error is reduced by 15% compared with traditional methods. After the machine learning algorithm is introduced, the model can automatically optimize the parameters of the bracket, and the experimental results show that the displacement of the optimized bracket under the ultimate load is reduced by 20%, significantly improving the bracket's stability. In the comprehensive safety assessment test, the model successfully identified three potential safety hazards and provided effective rectification suggestions, effectively preventing safety accidents in practical application. This study not only promotes the application of BIM and FEM in formwork support systems but also provides a new technical path for the intelligent development of the construction industry.
Integrating Apriori Mining and Speech Recognition for Intelligent and Secure Online Classroom Interaction
Real-time structuring of speech and anomaly detection remain critical challenges in online classroom interactions, especially under noisy and multi-speaker conditions. This study proposes an integrated framework that combines Automatic Speech Recognition (ASR) with Apriori-based pattern mining to enhance the intelligence and security of online classrooms. The system first applies multi-speaker ASR with acoustic feature separation to achieve robust transcription, speaker labeling, and noise suppression. The transcribed text is pre-processed through Chinese word segmentation and stop-word filtering to construct a transactional dataset. Frequent Pattern Growth (FP-Growth) is then employed to generate frequent itemsets and extract high-confidence association rules, forming a reference speech-pattern library. Local anomaly factors are introduced to quantify deviations in support and confidence between new corpora and the rule library, thereby enabling early detection of sensitive or off-topic speech. Experimental validation on 120 classroom sessions demonstrates an 80.2% recognition accuracy in highly noisy environments, with rule coverage and confidence reaching 87% and 89%, respectively. The proposed framework significantly improves anomaly warning efficiency, reducing sensitive-speech combinations by 87.0%. These results highlight the feasibility and effectiveness of integrating ASR and Apriori mining for intelligent speech structuring, pattern extraction, and anomaly detection in secure online classroom environments.
Traffic Flow Optimization for Rail Transit Based on Multi-Agent Reinforcement Learning for Network-Based Train Dispatching
Since the urban rail transit has developed rapidly, train scheduling and traffic flow optimization is a challenging task for improving the operational efficiency and service quality in developing networks and complicated networks. We can overcome the limitations of traditional centralized methods towards coping with massive-scale, high-density settings using multi-agent reinforcement learning (MARL) in network-based train dispatching, building a decentralized decision space where trains are treated as smart agents to optimize dispatching cooperatively through interaction with the environment. The paradigm maximizes traffic best, conserves computational complexity, and improves real-time disruption adaptability, achieving impact like reduced train delays and better energy efficiency. Experimental results indicate that the suggested MARL solution reduces average train delay (ATD) to 59% compared to FIFO during heavy-traffic scenarios, improves throughput (TP) to 148 trains/hour, lowers energy consumption (EC) by 11%, and lowers stability index (SI) by 43% without sacrificing near-real-time computation time (CT) to less than 1.5 seconds. The experimental results provide effective suggestions to intelligent rail transit dispatching and propose new directions for constructing intelligent transportation systems.
Dynamic Optimization of the Full-link Process of Unified Production Architecture Based on Digital Twin Technology
Information islands and response lags are common in current production systems, which seriously restrict the collaborative efficiency and dynamic optimization capabilities of each link. To solve this problem, this paper constructs a unified production architecture based on digital twin technology, realizes the digital mapping of the physical production process through a real-time state perception model driven by multi-source data, and uses the model predictive control (MPC) algorithm to dynamically schedule and optimize the full-link process. The control strategy is continuously iterated in the virtual space and fed back to the physical system to realize a closed-loop control mechanism of virtual-real synchronization. The experimental results show that the MPC optimization strategy controls the task switching time between 2.32 and 2.43 seconds, the system response delay is stabilized within 0.83 seconds, and the process response delay stability score is improved from 0.72 to 0.94. The method in this paper effectively connects the virtual-real connection of each link in the production system, realizes real-time optimization and intelligent decision-making of the whole process, and provides a feasible path for the collaborative control of intelligent manufacturing systems.
Methodology for evaluation and selection of software and hardware for creation of a cloud environment with a single information space based on fuzzy cluster analysis
The study aimed to develop a theoretical methodology for evaluating and selecting software and hardware for creating a cloud environment with a single information space, based on the use of fuzzy cluster analysis. The research issue was the difficulty of choosing the optimal solution in the face of uncertainty, which was typical for the process of cloud infrastructure creation. The methodology included an analysis of existing approaches, the formation of a system of evaluation criteria, theoretical justification and construction of a mathematical model for fuzzy evaluation of alternatives and involved the use of fuzzy cluster analysis to group alternative solutions based on multi-criteria assessments, including parameters such as performance, cost, scalability, reliability and security. Testing was conducted on real data from five leading cloud platform providers. The results demonstrated that the use of fuzzy cluster analysis increased the accuracy of cloud infrastructure options assessment by 23% compared to traditional methods. The application of the methodology on real-life examples demonstrated its effectiveness in the decision-making process when creating cloud environments, which was confirmed by the achieved increase in productivity and reduction in the cost of implementing and maintaining the system. The fuzzy cluster analysis identified three optimal hardware and software configurations that used computing resources 18% more efficiently. The developed algorithm demonstrated the processing capabilities of uncertainty in input data, reducing the impact of the subjectivity of expert opinions by 31%. The conclusions of this study emphasised that the fuzzy cluster analysis method is a substantial technology for evaluating and selecting software and hardware in modern IT projects, ensuring adaptability and accuracy in decision-making and increasing the validity of decision-making in planning corporate cloud environments, reduce the risks of choosing suboptimal configurations, and ensure more efficient use of resources in a single information space.
Optimizing Graphics Rendering and Illumination Simulation using Enhanced L-BFGS Algorithm
In computer graphics, photorealistic lighting simulation and efficient rendering technology have always faced the dual challenges of computational complexity and visual fidelity. Traditional global illumination algorithms rely on many ray sampling and iterative calculations. For example, path tracing needs to emit thousands of rays per pixel to converge. However, joint optimization problems of hundreds of dimensions, such as light source parameters and material reflectivity in dynamic scenes, often cause traditional gradient descent methods to fall into local optimization. The L-BFGS algorithm stores historical gradient information through a limited memory strategy and constructs an iterative model approximating the inverse of the Hessian matrix. While maintaining the fast convergence characteristics of second-order optimization, the memory consumption is reduced to the order of O (mn) (m is the number of memory steps), which provides a new idea for large-scale lighting parameter optimization. Experimental results demonstrate that the L-BFGS optimization achieves convergence of the energy function to 10-6 within 500 iterations in scenes with dynamic light sources and complex materials, reducing computation time by 38% compared to traditional BFGS. When integrated into NeRF training, the hybrid L-BFGS strategy reduces geometric reconstruction error to 0.12 mm, improving accuracy by 52% over pure stochastic gradient descent. In real-time rendering, GPU-accelerated L-BFGS optimizes shadow mapping parameters for 256 virtual point lights per frame, maintaining 60 FPS at 4K resolution with 1.2 GB VRAM usage. For mobile AR, a quantized L-BFGS variant achieves material reflection calibration in 8.3 ms with ±0.5% azimuth accuracy, while the Monte Carlo-L-BFGS framework reduces indirect illumination precomputation from 14.6 hours to 2.3 hours with 98.7% visual fidelity. These technological advances provide a new paradigm for integrating movie-level offline and real-time rasterized rendering pipelines and promote the development of efficient visualization in emerging fields such as digital twins and the metaverse.
Research on optimization of cold source group control strategy in data center
Cooling systems play a vital role in maintaining optimal operating conditions in modern data centers (DCs). Efficient control of cold source groups, such as chillers and air handling units, is essential for reducing energy consumption while ensuring temperature stability. This research introduces a novel data-driven approach to optimize the control strategy for cold source groups in DCs by leveraging extensive real-time monitoring data. The control problem is formulated as an energy cost minimization task subject to strict temperature constraints. Addressing these issues, this research proposes an end-to-end group control algorithm based on deep reinforcement learning (DRL). The research suggests a new algorithm called Artificial Gorilla Troops Optimizer-driven Controlled Deep Q-Network (AGTO-CDQN) for dynamically coordinating the operation of multiple cold source units. The research involves collecting both historical and real-time data from DC sensors, including temperature readings, power consumption of cooling units, and server workloads. Experimental results demonstrate that AGTO-CDQN considerably increases the power savings above 15% for IT power consumption, cooling power consumption, total power consumption, and average zone air temperature. These findings highlight the approach’s potential for practical deployment in energy-efficient DC cooling management.
Optimization of Wireless Resource Management Algorithm Based on Deep Q-Network
With the continuous and rapid growth of the number of access devices, traditional resource management algorithms can no longer meet the actual needs. In order to solve the problems of poor coordination of wireless network resources and uncoordinated service quality in traditional wireless resource management, a new wireless network resource management algorithm is proposed based on the actual network structure of the Industrial Internet of Things (IIoT), combined with the Markov decision-making process and the improved deep Q network algorithm. The results show that the performance of the proposed algorithm is significantly improved after training, surpassing the time limit of Aloha algorithm and random algorithm, achieving high channel utilization and system rate, and the convergence speed is faster than that of traditional deep Q network algorithms. When the arrival rate is greater than 1.0, the channel rate and average sum rate of the research algorithm reach 94% and 25bits/s/Hz, respectively, which are significantly better than the comparison algorithms. As the bandwidth of the system increases, the latency of each algorithm decreases. Among them, the performance of the research algorithm is better than that of the comparison algorithm, the system latency and computational cost are lower, and the system latency is the lowest under different numbers of blockchain nodes. The results show that the proposed resource management algorithm can realize IIoT dynamic wireless resource management and promote the efficient utilization of wireless network resources.
Financial Risk Early Warning Model for Imbalanced Data using SGD-GSVM
This work offers a new method for early warning of financial risk by combining SGD optimisation with Gaussian SVM and employing adaptive oversampling for data balancing. The findings show that SGD-GSVM is the best model because it strikes a balance between high accuracy and computational economy. Financial organisations can create real-time risk management plans with the help of the suggested technique. For additional performance improvements, hybrid deep learning approaches might be investigated in future studies.
Integration of Multiscale Fusion and Cross-scale Attention Refinement for Enhanced Target Detection Using MSFNet
Object recognition across varying scales remains a persistent challenge in computer vision, especially in scenes with occlusion, low contrast, and diverse spatial resolutions. Conventional convolutional neural networks with fixed receptive fields often fail to capture both fine-grained details and high-level contextual cues. This study focuses on developing a scale-adaptive detection framework to overcome these limitations. The proposed MSFNet (Multiscale Fusion Network) employs a Dual-Stream Convolutional Backbone to extract low-level and high-level features in parallel. A Scale-Adaptive Feature Fusion Module (SAFFM) integrates multiscale representations through dynamic, scale-aware weighting. A Cross-Scale Attention Refinement (CSAR) module enhances discriminative features and suppresses irrelevant or redundant information. The architecture operates in an end-to-end fashion and is optimized for detection accuracy and real-time inference speed. Experimental evaluation on MS COCO 2017 and PASCAL VOC 2012 reports 47.3% AP and 81.5% mAP, respectively. Performance exceeds Faster R-CNN, YOLOv5, and RetinaNet by +3.8%, +4.5%, and +3.2% AP on the COCO benchmark. MSFNet provides a scalable, accurate, and computationally efficient approach for multiscale object recognition, enabling deployment in real-time applications such as autonomous driving, intelligent surveillance, and remote sensing.
Application of Building Information Modeling in Subway Foundation Pit Support Risk Management
To address issues such as low safety factors and difficulty in predicting the force variation of support systems in subway station foundation pit construction, this study develops a subway foundation pit support risk management system based on a risk data monitoring method combining Building Information Modeling technology and gated recurrent neural networks. In constructing the risk management system, a fuzzy analytic hierarchy process model is also used to assess and analyze the foundation pit risks. Experimental results show that the hybrid risk data monitoring method records a displacement of 0.28mm at monitoring point 6, while other methods—Cooperative Game Empowerment, Grey Relational Analysis, and Relativistic Conditional Generative Adversarial Network —record displacements of 0.16mm, -0.21mm, and 0.17mm, respectively. Additionally, empirical analysis of the constructed foundation pit support risk management system reveals that the hybrid subway foundation pit support risk management system measures a settlement value of -10.83mm, while the Generative Adversarial Network and Grey Relational Analysis-based systems measure maximum settlements of -10.11mm and -9.23mm, respectively, all of which are less accurate than the hybrid risk management system. These results demonstrate that the hybrid subway foundation pit support risk management system performs well in risk control and monitoring, ensuring the stable operation of subway construction. This research contributes to future control of foundation pit excavation support safety and the improvement of dynamic risk management levels.
Analysis of fusion of machine learning model and economic factors in electricity market price prediction
The volatility of electricity market prices poses significant challenges for forecasting models, particularly amid dynamic economic conditions and fluctuating energy demands. This research introduces a novel model for electricity market price prediction that combines advanced machine learning (ML) techniques with economic energy factors to enhance forecasting accuracy and reliability. The proposed model integrates key economic energy indicators such as fuel prices (natural gas, coal, crude oil), inflation rates, currency exchange rates, industrial production indices, and electricity demand-supply ratios. These economic variables are combined with historical electricity price and load data to capture both short-term market fluctuations and broader economic influences. Data preprocessing using Z-score normalization was applied to standardize the input features, ensuring consistent scaling and improved model stability. The forecasting architecture employs a multi-model ensemble strategy, utilizing a Least Squares Support Vector-fused Adaptive Random Forest (LSSV-ARF) model for electricity price prediction. The LSSV component captures nonlinear short-term fluctuations, while the ARF component adapts to evolving economic patterns. Empirical validation is conducted using real-world market data. The model is implemented in the Python platform, and the proposed method demonstrated substantial improvements over traditional models, such as MAE (0.79) and the MAPE (5.43%). These results confirm the effectiveness of integrating economic energy indicators with ML algorithms for electricity price forecasting, providing valuable insights for market participants, grid operators, and policymakers in managing energy pricing strategies.
Exploration of the Development Path of Rural Smart Agriculture Driven by Artificial Intelligence
Against the backdrop of global climate change and increasing constraints on agricultural resources, developing smart agricultural technologies is a key path to achieving food security and sustainable development. This study aims to investigate the development path of rural smart agriculture driven by artificial intelligence, with a focus on addressing the bottlenecks in traditional agricultural prediction models in terms of accuracy, efficiency, and resource consumption. This study focuses on winter wheat and summer corn rotation farmland, and collects environmental data through a Pico W+ESP32-CAM sensor network. After wavelet denoising, outlier detection, and Kalman smoothing interpolation preprocessing, the soil moisture prediction effects of various models are compared. Based on the predicted fruit, rule-based irrigation control is implemented. The core indicators include crop yield, protein and vitamin content, water and fertilizer usage, and model performance (response time, error, trainable parameters, etc.). Quantum Neural Network-Back Propagation model is constructed by combining an IoT multi-sensor data acquisition system. This study innovatively integrates quantum bits and parameterized quantum gates into the prediction module. The results showed that in terms of prediction, the accuracy of the soil moisture prediction model reached 97.48%, which was 10.29% higher than that of the traditional Back Propagation Neural Network (87.19%). The model response time was only 1.27 seconds, and the computational resource utilization rate has been reduced to 23.18%, meeting the real-time decision-making needs of farmland. The irrigation strategy based on the predicted results increased water resource utilization efficiency by 47.57%, reduced fertilizer usage by 19.41%, and increased economic benefits per unit area by 28.30%. The research method has achieved high-precision dynamic prediction of soil moisture. This research verified the feasibility of artificial intelligence algorithms in agricultural edge computing scenarios, provided a closed-loop solution of "perception decision execution" for rural smart agriculture, and promoted the transformation of agricultural production to precision and low-carbon.
A CNN-Enabled Context-Aware Wireless Sensor Network for Smart Banana Cultivation
Banana cultivation, a critical component of tropical agriculture, is highly vulnerable to environmental fluctuations, water stress, and disease outbreaks. Traditional wireless sensor networks (WSNs) used in precision farming lack the adaptability required for dynamic field conditions. This paper proposes a novel context-aware WSN framework integrated with a Convolutional Neural Network (CNN) to address these limitations in banana farming. The system captures and analyzes real-time environmental and soil data—such as temperature, humidity, soil moisture, and pH—using a multi-sensor architecture. A CNN-based model interprets this data to classify field conditions into actionable contexts, enabling intelligent irrigation scheduling, disease risk alerts, and nutrient deficiency detection. Field experiments demonstrate that the framework achieves a context classification accuracy of 95.3%, improves water-use efficiency by 28%, and reduces disease incidence through early intervention. Compared to rule-based systems, the proposed model significantly enhances adaptability, scalability, and resource optimization. This study contributes a robust, scalable approach to smart agriculture, offering a transferable model for other high-value, climate-sensitive crops.
Image Information Extraction and Optimization Algorithm for Visual Communication Integrating Object Detection and Deep Learning
In response to the dual challenges of insufficient generalization of traditional image processing methods and high computational complexity of deep learning models in industrial visual scenes, this study proposes an innovative solution that integrates object detection and deep learning. This scheme constructs a two-stage processing method, uses the Darknet-19 backbone network to achieve multi-scale feature fusion, and combines depth separable convolution and channel rearrangement mechanisms to achieve network lightweighting, significantly improving computational efficiency while ensuring accuracy. The experiment showed that this method achieved 97% pixel level accuracy and 5 mm positioning error in water level detection tasks, which was 12% more accurate than the suboptimal model. The character recognition rate for encoding recognition tasks reached 97%, with a false detection rate of only 5%. In multi-tasking scenarios, the interference level was reduced to 0.12, the video memory usage increased by 62%, and the edge device latency remained stable at 25 ms. In terms of lighting robustness, the multi-scale photometric transformation achieved a lighting invariance index of 0.93 and improved the signal-to-noise ratio by 8.7 dB. In lightweight deployment verification, the computational density was 1.26 GMACs/mm², and the 72-hour failure rate was less than 0.1%. This study provides a solution for industrial vision systems that balances accuracy and efficiency, and has important application value in fields such as smart security and intelligent manufacturing. Future work will further optimize deployment adaptability through adaptive calibration techniques and dynamic pruning strategies.
Spatially-Guided CNN Filter Modeling for Multi-Noise Localization and Active Noise Cancellation in Bedroom Environments
Accurate multi-noise localization and real-time active noise cancellation (ANC) are critical for enhancing audio quality and comfort in smart-bedroom environments. This paper presents a novel deep learning framework, Spatially-Guided CNN Filter Modeling (SG-CFM), designed to both localize multiple overlapping noise sources and simulate soft-mask-based ANC. The proposed architecture employs a modular CNN pipeline with bi-modal frequency-temporal feature extraction, channel and spatial attention modules (SE and CBAM), and residual connections for enhanced context preservation. The network integrates an Enhanced Bi-Modal Convolution Block, Residual Temporal Squeeze Block, Dilated Temporal Convolution Block, and a Hierarchical Temporal Aggregation Block, which collectively model both local and long-range acoustic dependencies. The system is trained using a domain-specific dataset of bedroom noises, annotated with overlapping sound events such as traffic, fan noise, door movement, and background TV. The extracted MFCC features are passed through the spatially-tuned CNN, which outputs both multi-label predictions and a spectro temporal suppression mask simulating ANC. Experimental results demonstrate high multi-label classification performance with an average F1-score of 0.81, and effective ANC simulation by reducing noise energy in critical temporal segments. The model is lightweight, real-time capable, and suitable for deployment in embedded IoT devices, making it a strong candidate for next-generation smart home audio systems focused on sleep quality, acoustic privacy, and ambient intelligence.
Instance Segmentation of Human Body Parts Using Deep Learning Yolov8 Model
Image segmentation constitutes an essential initial stage in digital image processing, wherein the primary objective is to distinguish and separate meaningful objects from the background to facilitate more accurate analysis and recognition. In the context of human body detection, the precise identification and delineation of specific anatomical regions—such as the head, torso, and arms—is particularly important due to the distinct structural and functional characteristics of each part. Aim of the present study introduces a methodological approach and the development of a web-based segmentation system that leverages the YOLOv8 instance segmentation model to partition human body images into four key regions, enhancing both accuracy and usability in downstream applications. A total of 107 images, each manually annotated, were employed and systematically divided into training, validation, and testing datasets. Upon evaluating the model's performance, the highest mean Average Precision (mAP) achieved was 0.979 after 200 training epochs. Additionally, the model attained a precision of 0.914, a recall of 0.995, and an F1-score of 0.95. These results indicate that the proposed instance segmentation framework delivers robust accuracy and dependable performance in segmenting human body parts.
ELRP: A Trust and IDS-Integrated Opportunistic Routing Protocol for MANETs with NS2-Based Evaluation
Mobile Ad Hoc Networks (MANETs) are widely used in diverse situations where secure communication and reliability are necessary. MANETs without a centralized infrastructure are prone to various security threats. This paper presents an Enhanced Lightweight Trusted Routing Protocol (ELRP) to ensure secure connectivity and reliability in MANETs. The primary goal of ELRP is to prevent the inclusion of malicious hosts in the route and reliably convey data. Trust metrics provide secure communication by leveraging quality of service parameters to optimize trust computation. Opportunistic routing enhances data reliability and consistency. It allows each node to choose an intermediate node dynamically based on trust levels. Depending on the trust factor, ELRP enables nodes to select the forwarder at runtime. ELRP features an Intrusion Detection System (IDS) designed to recognize and mitigate black and grey hole attacks. ELRP is scalable as it uses local information and does not consume computational resources. Simulations conducted in NS2 demonstrate that ELRP improves throughput by approximately 22%, increases packet delivery ratio by 14.4%, and reduces end-to-end delay by 37.1% compared to standard AODV under malicious attack scenarios.
Static Malware Detection through Ensemble Feature Selection and Supervised Classification
In a digital landscape where malicious software evolves faster than traditional defenses, intelligent and proactive detection has become essential. This study presents a machine learning framework for static malware detection based on the analysis of 138,047 Portable Executable samples, including both malware and benign files. The dataset comprises 56 static structural features extracted without code execution. Four supervised classifiers—Backpropagation Neural Network, Decision Tree, Random Forest, and Support Vector Machine—were evaluated following the Knowledge Discovery in Databases process. Ensemble-based feature selection methods (Random Forest and Extra Trees) were applied to identify the most informative attributes, while random undersampling was used to mitigate class imbalance. Experimental results show that the Random Forest classifier achieved the best performance, reaching 99.45% accuracy and a 0.9909 F1-score on imbalanced data, and 99.32% accuracy on the balanced dataset. These findings highlight the reliability and scalability of tree-based models for static malware detection. Overall, the proposed framework demonstrates that careful feature selection and balance adjustment can significantly enhance the performance and interpretability of cybersecurity classification systems.
A Reliability Prediction Model For Power Outage Planning In Distribution Networks Combining Genetic Algorithms And LSTM
Distribution network power outages are frequent and unpredictable, threatening infrastructure reliability and energy security. Traditional models with static risk evaluations or rule-based logic cannot adjust to complex spatiotemporal outage patterns. This research presents GAPO-LSTM (Genetic Algorithm–Powered Outage prediction with LSTM), a novel hybrid reliability prediction model that integrates GA optimization with LSTM sequence modeling. This study aims to create a comprehensive, data-driven system that correctly forecasts localized power failures and improves distribution network outage planning. The model dynamically learns from previous outage records and optimizes input characteristics and LSTM hyperparameters for forecasting accuracy. The suggested method preprocesses and geographically clusters power outage data to group similar locations. Genetic algorithms choose ideal feature subsets, sequence lengths, and learning parameters for LSTMs. The attention-enhanced LSTM network is trained on temporal outage sequences to predict cluster outage risks. In high-risk zone classification, GAPO-LSTM reduces RMSE by 18.6% and increases F1-score by 12.4%, outperforming vanilla LSTM and Random Forest. Sensitivity research shows the model's resilience to cluster density and outage volumes. Experimental results show the model can learn outage dynamics in large-scale distribution contexts while scaling. Finally, GAPO-LSTM provides a powerful, intelligent solution for proactive outage management and reliability improvement in modern power systems.
Data-driven Clustering analysis in E-commerce User Segmentation
E-commerce user segmentation is crucial for enterprises aiming to develop precise marketing strategies and efficiently manage their customer base. Traditional clustering algorithms often face challenges due to noisy data and isolated points, which can degrade segmentation quality. This investigation focuses on improving e-commerce user segmentation through innovative cluster analysis techniques that enhance traditional clustering methods by effectively excluding noisy and isolated data points and selecting high-quality initial cluster centers. A novel Efficient Coral Reefs Optimized Kernel Density-Based Spatial Clustering of Applications with Noise (ECRO-Kernel DBSCAN) algorithm is useful for user segmentation based on the customer behavior on e-commerce platforms. The dataset includes customer transaction and interaction data such as purchase history, transaction frequency, monetary value, browsing behavior, and other engagement metrics. First, Kernel DBSCAN is used to detect core dense regions and remove noise; then, ECRO is applied to the core points to identify clusters. The analysis reveals five distinct customer groups: Platinum, Gold, Silver, Copper, and Iron segments asked their purchasing behavior and engagement levels. A detailed estimation of these segments informs the design of customized marketing strategies tailored to each group's unique characteristics. The effectiveness of this targeted marketing approach is further assessed through enhancements in sales performance and platform user satisfaction. Experimental outcomes demonstrate 94% accuracy, 93% precision, 92% recall, and 94% F1-score, highlighting superior segmentation robustness and practical effectiveness in targeted marketing and customer engagement compared to traditional clustering methods. This research provides valuable insights into e-commerce user segmentation, offering a practical framework for businesses to optimize customer targeting, increase engagement, and drive sustainable growth through data-driven cluster analysis.
Developing Estimation Algorithms for the Punching Shear Strength of SlabColumn Connections
This exploration introduces a data-oriented scheme for projecting the punching shear resistance (Vn) linked to failure modes (FMs) in slab-column connections with shear reinforcement. Because reinforced concrete (RC) slab-column connections exhibit a simple construction approach, slabs rest directly on columns without beams. In slab-column connections with shear reinforcement, Vn linked to FMs has only very seldom been stated in research up to this point using machine learning approaches. Two accepted and reliable models were considered for estimation: random forests (RF) and adaptive neuro-fuzzy inference system (ANFIS). Nine input parameters about the punching shear mechanism are found using 327 test results from a computational database. The dataset's learning set (70%) and evaluation set (15%) were used to construct, validate, and test the suggested scheme. Its accuracy is greatly affected by the RF and ANFIS hyperparameters, which need to be selected using metaheuristic enhancement tactics. For this, the Prairie Dog Algorithm (PDA) is used. Based on the logic and assessment of analytical justifications, it can be concluded that both models are exact and dependable, with ANF-PDO being marginally superior compared to RF-PDO.
Determine the Value of Maximum Dry Density by Gaussian Process Regression in Individual and Hybrid Models
This investigation introduces a novel method for utilizing the GPR technique to project the MDD within soil stability mixes. The established method is based on creating precise and thorough schemes that link a variety of natural soil characteristics, like linear shrinkage, plasticity, particle size variation, and the type as well as quantity of stabilization additives, to the MDD of stabilized soil. The schemes are created and assessed using a variety of soil types derived from earlier published destabilization outcomes from tests. Two meta-heuristic schemes, the Dingo Optimization Algorithm (DOA) and the COOT Optimization Algorithm (COA), were included in the study in this article. As a result, two hybrid schemes, consisting of GPDO and GPCO, were created. The GPCO model has a high R2 = 0.9905 value and the most propitious RMSE = 26.13 during the training stage, showing that this model has a better predictive capability and generalization ability compared to other schemes developed in this investigation. In summary, this tactic offers a viable solution for the accurate anticipation of the MDD of soil-strengthening blends employing GPR and integrating meta-heuristic schemes, which may be useful in various engineering applications.
Improving Tropical Forest Biomass Predictions with Multi-Output Deep Learning Models for Above- and Belowground Estimates
Accurate biomass prediction in tropical forests is a key factor in understanding carbon sequestration and forest health. However, traditional methods lack the capability to deal with complex ecological data. The current study seeks to evaluate the Extra Trees algorithm as a novel optimization technique to improve the reliability of simultaneous predictions for aboveground biomass, belowground biomass, and total biomass in tropical forests, and to perform against other machine learning models derived from a dataset of 175 tree samples collected across 27 plots in the Central Highlands ecoregion, Vietnam. It also compares the performance of ET against other machine learning models such as GB, Bagging, and LSTM networks. Our findings showed that ET had the best performance regarding prediction accuracy, since the training VAFs obtained were 99.90391 for BGB, 99.87924 for AGB, and 99.88903 for TB when combined with R-squared (R²) values of 0.999039, 0.998792, and 0.99889, respectively. Additionally, ET was found to have lower MAE and RMSE values than the results from other models tested in this work. This makes ET a very useful tool for biomass estimates. This result acknowledges the potential of ET for giving more accurate and reliable biomass prediction than traditional methods; thus, it would be contributing significantly to forest management and conservation strategies.
Application of Artificial Intelligence in Supply Chain Management: Empirical Analysis of Optimization and Efficiency Enhancement
In an era of increasing global uncertainty and rapidly evolving market dynamics, supply chains have become more vulnerable to disruptions caused by pandemics, geopolitical conflicts, natural disasters, and demand variations. The analysis is based on surveyed firms, which limit applicability across all industries and regions. Furthermore, reliance on self-reported data could affect measurement accuracy. The framework examines how artificial intelligence (AI) is used in supply chain management (SCM), with an importance on efficacy improvement and optimization through empirical analysis. Data were gathered through surveys from 534 firms, and exploratory analyses were conducted to validate the construct structures. The relations between the variables were investigated using correlation analysis, and the proposed processes and mediation effects were tested using partial least squares structural equation modeling (PLS-SEM). Reliability and validity tests were performed to guarantee measurement accuracy, providing robust empirical insights into AI-driven supply chain performance. The approach examines how a company's propensity to use AI is impacted by supply chain cooperation, environmental uncertainty, and the relative benefits of AI technology. It further assesses how AI adoption influences supply chain optimization, effectiveness enhancement, resilience, and overall performance. EU shows a high correlation with ASU (0.74), suggesting that improved ease of use strongly supports usability, underscoring its central role in supply chain performance. Moreover, AI adoption positively impacts optimization and efficiency through improved forecasting, inventory control, and logistics coordination, while also enhancing supply chain resilience, enabling firms to respond more effectively to external disruptions. The framework connections the gap between AI adoption theory and practical supply chain developments, presenting actionable insights for enterprises seeking to integrate AI into their supply chain strategies to achieve sustainable efficiency and performance gains.