International Journal of Wireless and Mobile Computing (24 papers in press)

Regular Issues

• Research on a laser cutting path planning method based on improved ant colony optimisation  
  by Naigong Yu, Qiao Xu, Zhen Zhang 
  Abstract: Laser cutting path planning for fabric patterns is critical to cutting efficiency. The ant colony optimisation algorithm commonly used in this field is constrained by the complete cutting and cannot plan a true global optimal path, resulting in large empty strokes. To solve this problem, this paper proposes an ant colony optimisation method based on virtual segmentation of multiple feature points for path planning of laser cutting. The method first changes the feature point selection strategy of traditional ant colony optimisation and increases the number of feature points in a single pattern. Then the single closed pattern is virtually divided into multiple open contours. Finally, the optimal cutting path is planned based on the solution of the travelling salesman problem. Experiments show that the cutting planning path obtained by the proposed method has a higher degree of compression on the idle stroke and significantly improves the laser cutting efficiency.
  Keywords: laser cutting; path planning; ant colony optimisation; virtual segmentation.
  
  
• Performance analysis of downlink precoding techniques in massive MIMO under perfect and imperfect channel state information in single and multi-cell scenarios  
  by Chanchal Soni, Namit Gupta 
  Abstract: The novel Optimised Max-Min Zero forcing precoder (OM2ZFP) scheme is proposed in this work. The optimization is incorporated with the chimp optimization strategy (CPO) to maximise the spectral efficiency, achievable sum rate, max-min rate, and minimise BER. The designed precoder model is contemplated under single cell perfect CSI, single-cell imperfect CSI and multiple cells perfect CSI, multi-cell imperfect CSI. Three pre-coding schemes, zero forcing (ZF), Maximum Ratio Pre-coding (MRT) and Minimum Mean Square Error (MMSE) precoder techniques, are implemented in the Matlab platform to manifest the effects of the novel designed precoder. The performance of the achievable sum rate is analysed under three cases, namely case I (fixed users and varying antenna), case II (fixed and varying) and case III (varying channel estimation error). The results show that the increasing number of antenna and users enhance the spectral efficiency, downlink transmits power and achievable sum rate performance.
  Keywords: massive MIMO; precoder; downlink transmission; antenna; optimisation; spectral efficiency; achievable sum rate.
  
  
• An improved fuzzy clustering log anomaly detection method  
  by Shuqian He, WenJuan Jiang, Zhengjie Deng, Xuechao Sun, Chun Shi 
  Abstract: Logs are semi-structured text data generated by log statements in software code. Owing to the relatively small amount of abnormal data in log data, there is a situation of data imbalance, which causes a large number of false negatives and false positives in most existing log anomaly detection methods. This paper proposes a fuzzy clustering anomaly detection model for unbalanced data, which can effectively deal with the problem of data imbalance and can effectively detect singular anomalies. We introduce an imbalance compensation factor to improve the fuzzy clustering method, and use this method to build an anomaly detection model for anomaly detection of real log data. Experiments on real data sets show that our proposed method can be effectively applied to log-based anomaly detection. Furthermore, the proposed log-based anomaly detection algorithms outperform other the state-of-the-art algorithms in terms of the accuracy, recall and F1 measurement.
  Keywords: distributed information system; log data; anomaly detection; artificial intelligence for IT operations; fuzzy clustering; imbalanced datasets; unsupervised learning; machine learning.
  
  
• Research on wireless routing problem based on dynamic polycephalus algorithm  
  by Zhang Yi, Yang Zhengquan 
  Abstract: The efficiency of the traditional Physarum Polycephalum Model (PPM) is low for wireless planning problems. Also, other heuristic algorithms are easy to fall into local optimum and usually require a large training set to find the optimal parameter combination. Aiming at these problems, we propose a new dynamic model of Physarum Polydynia (DMOP2) algorithm combined with PPM in this paper. This algorithm can judge the irrelevant nodes according to the traffic matrix after each iteration and then delete them and re-establish a new distance matrix when solving the routing problem. The improvements not only reduce the time consumed by calculation but also improve the accuracy of calculation pressure. Simulation experiments in random network and real road network prove the feasibility and effectiveness of the proposed algorithm in solving the path planning problem, and the experimental results show that the efficiency is significantly improved compared with PPM.
  Keywords: wireless planning; Physarum Polycephalum model; dynamic model.
  
  
• A trusted management mechanism based on trust domain in hierarchical internet of things  
  by Mingchun Wang, Jia Lou, Yedong Yuan, Chunzi Chen 
  Abstract: Existing trusted models usually authenticate the identity and behaviour of sensing nodes, without considering the role of sensing nodes in the process of interaction and transmission of information. Therefore, in view of the hierarchical wireless sensor network architecture of the internet of things, this paper proposes a new hierarchical trusted management mechanism based on trusted domain. The mechanism abstracts different nodes in the hierarchical structure of the internet of things, gives them different identities, and calculates the trust value of the sensing nodes by using similarity weighted reconciliation method. The experimental results show that the proposed scheme is feasible and effective.
  Keywords: trusted domain; trusted management; similarity weighted reconciliation; trust value; hierarchical structure.
  
  
• Automatic modulation recognition based on channel and spatial attention mechanism  
  by Tianjun Peng, Guangxue Yue 
  Abstract: With the complexity of the wireless communication environment, automatic modulation recognition (AMR) of wireless communication signals has become a significant challenge. Most existing researches improve the model recognition performance by designing high-complexity architectures or providing supplementary feature information. This paper proposes a novel AMR framework named CCSGNet. The convolutional neural network (CNN) and bidirectional gate recurrent unit (BiGRU) are employed in CCSGNet to reduce the spectral and time variation of the signals, furthermore, the channel and spatial attention are employed to fully extract local and global features of signals. In order to reduce the training time cost of the model, we propose a piecewise adaptive learning rate tuning method to improve the training of the model. The comparisons with several common learning rate tuning methods on CCSGNet show that the proposed method achieves convergence in 25 training epochs, reducing the training time cost of the model. Moreover, CCSGNet improves the recognition accuracy of 16QAM and 64QAM by 6.47%-50.95% and 4.54%-25.66%, respectively.
  Keywords: automatic modulation recognition; attention mechanism; learning rate; deep learning.
  
  
• Optimisation of a high-speed optical OFDM system for indoor atmospheric conditions  
  by B. Sridhar, S. Sridhar, Naresh K. Darimireddy 
  Abstract: VLC provides high security and broadband functionality for optical communication in free space. In particular, this proposed work focuses on analysing receiving power distribution patterns and signal-to-noise ratios for indoor and vehicle applications. The optical systems of indoor communications are more suitable than wireless radio systems. The significant advantage of optical wireless communication (OWC) is providing high-speed data up to 2.5 Gbps at a low cost. In indoor areas such as auditoriums and public places, the OWC systems are more suitable. But optical signals are distorted by the signal propagation effects due to obstacles, walls, etc. The proposed system is an OFDM-based system that can transmit multiple channels and connects many modems over a given indoor area. Proposed methods initially focus on the LED/LD transmitter sources placement at the ceiling of indoor space and observed signal power distribution; in an IM/DD-based OWC system, the information signal must be accurate and nonnegative. The proposed asymmetric optical OFDM (ACO-OFDM) system is implemented for indoor communications, and the system's performance is evaluated with the Bit error rate. In particular, the performance of the specific M-QAM ACO-OFDM method with adaptive frequency is assessed by using theoretical analysis and simulations. Compared to the M-QAM ACO-OFDM method, the ACO-OFDM and DCO-OFDM showed lower spectral efficiency performance for the OWC system in the frequency selective channel.
  Keywords: ACO-OFDM; indoor networks; power distribution; clipping; bit error rate.
  
  
• How does common institutional ownership affect the high-quality development of private manufacturing firms: based on the perspective of R&D investment  
  by Jie Wang, Xusheng Fang, Haiming Zhang, Jiangjun Yuan 
  Abstract: The high-quality development of private manufacturing firms is important for enhancing Chinas international competitiveness. In this paper, we test how common institutional ownership affects private manufacturing firms R&D investment, a decision that relates to firms long-term value creation. Using data from listed private manufacturing firms in China from 2009 of 2022, we find that common institutional ownership significantly promotes private firms R&D investment, a finding that still holds after robustness tests using propensity score matching, replacing key variables and son on. The mechanism analysis suggests that common institutional ownership mainly plays a resource effect, which promotes R&D investment by alleviating the financing constraints of private firms. Finally, we also find that common institutional ownership can play a greater role when economic policy uncertainty is high and the level of analyst following is high. This study provides new empirical evidence that firms can benefit from common institutional ownership and provides a reference for authorities to guide common institutional investors.
  Keywords: common institutional ownership; R&D investment; financing constraints.
  DOI: 10.1504/IJWMC.2025.10073550
   
  
• Cognitive distraction identification using physiological signals-based enhancing road safety through attributed multi-order graph convolutional network  
  by P.S. Soumya, S. Mythili 
  Abstract: Driver distraction, particularly cognitive distraction is a major cause of road accidents. While visual and manual distractions manifest through observable physical behaviors, the cognitive distraction presents unique detection challenges. The existing methods are not easily scalable due to the high cost of data acquisition devices. In this paper, an Enhancing Road Safety through Attributed Multi-Order Graph Convolutional Network-Based Cognitive Distraction Identification Using Physiological Signals (ERD-AMGCN-CDI-PS) is proposed. The ERD-AMGCN-CDI-PS utilizes physiological signals from the DEAP and WESAD datasets. The input signals are pre-processing utilizing Regularized Bias-Aware Ensemble Kalman Filter (RBAEKF) to remove noise and artifacts. The Synchro-Transient-Extracting Transform (STET) is used to extract visual features from pre-processing signals. These features are given to the Attributed Multi-Order Graph Convolutional Network (AMGCN) to identify cognitive distraction. The ERD-AMGCN-CDI-PS method achieves 19.56%, 10.88% and 19.60% higher accuracy and 19.83%, 11.57% and 19.65% lower false positive rate (FPR) over the existing techniques.
  Keywords: attributed multi-order graph convolutional network; cognitive distraction; regularised bias-aware ensemble Kalman filter; road safety; synchro-transient-extracting transform.
  DOI: 10.1504/IJWMC.2025.10074665
   
  
• Research on load balancing optimisation of cloud disk application storage system for cloud teaching  
  by Qi Xu 
  Abstract: The traffic load of cloud disk application storage system increases gradually, which reduces the effect of resource scheduling. Therefore, a traffic load optimization strategy of cloud disk application storage system based on WPM-LPT-LSTM is proposed. Firstly, greedy algorithm based on loosely-linked proof tree (LPT) idea is adopted. Then, Weighted Partial Migration (WPM) algorithm is introduced. Finally, long short-term memory network (LSTM) is added in WPM-LPT algorithm. Moreover, the proposed strategy uses WPM-LPT algorithm to deal with data segment arrangement and traffic difference, and utilizes LSTM network to predict data popularity. Experimental results reveal that average Jain index of this policy has always remained at about 12.9%, and its average end-to-end latency is only 4.8 ms. Therefore, the proposed policy can realize the traffic load balancing of the system.
  Keywords: cloud disk application; storage system; load balancing; greedy algorithm; weighted partial migration algorithm.
  DOI: 10.1504/IJWMC.2025.10075371
   
  
• Chiral nanostructure prediction model for chiral characteristic optical materials in biological art  
  by Xin Dong, Xinyi Wang 
  Abstract: Chiral features are a common physical structural feature in nature and are often applied in biological art. Traditional chiral nanostructure prediction methods have low efficiency in dealing with complex optical materials and are difficult to meet high-precision requirements. To address this issue and improve the accuracy of predicting chiral nanostructures, this study utilizes machine learning algorithms to construct a deep learning-based convolutional neural network model for predicting the circular dichroism of chiral nanostructures. Moreover, by fusing multiple deep learning models, the accuracy of the prediction model has been improved. Results showed the Stacking ensemble model achieved 94.15% accuracy, 11.99% to 18.48% higher than other models, and closely matched actual circular dichroism spectra. This study enhances the prediction accuracy of chiral nanostructures, providing crucial support for the design and application of nanomaterials in biological art.
  Keywords: biological art; chiral characteristics; optical materials; nanostructures; convolutional neural network.
  DOI: 10.1504/IJWMC.2025.10075434
   
  
• A differential privacy-preserving framework for real-time object detection systems in IoT  
  by Jianhui Huang, Jianbiao Zhang, Kaicheng Xu 
  Abstract: With the explosive growth of IoT traffic demand, real-time object detection systems have a large number of computation-intensive tasks, but limited by their device computing power, the tasks need to be offloaded to edge servers. However, the raw data summarised in the object detection system have much sensitive information, which can cause serious data privacy and security issues once directly released. A novel differential privacy framework DPPF-RODS (Differential Privacy Protection Framework for Real-time Object Detection Systems) is designed for IoT-based real-time object detection systems. The solution addresses critical challenges of sensitive data exposure during computational offloading and inefficient model convergence through a dual adaptive methodology: dynamic noise scaling strategically adjusts privacy protection levels to optimise the balance between data utility and confidentiality, while adaptive training cycles enhance convergence efficiency without compromising detection precision. Three fundamental mechanisms establish comprehensive end-to-end security: constrained local updates maintain consistency across distributed edge devices, noise-injected global aggregation safeguards server-side operations, and optimised public data sampling improves initial model parameters. Finally, a real large-scale dataset is used for evaluation to verify the effectiveness of the DPPF-RODS privacy preserving framework. The result showed that convergence speed of the model has been improved by about 10%.
  Keywords: differential privacy; real-time object detection systems; computational resource offloading optimisation.
  DOI: 10.1504/IJWMC.2025.10075450
   
  
• Optimal cuckoo-based resource allocation in wireless network  
  by S. Mary Evanchalin, R. Ravi 
  Abstract: Efficient resource allocation in wireless networks is becoming essential due to the growing demand for wireless communication services. However, the usual resource allocation is not sufficient for wireless networks. Methods: The Cuckoo-based Flooding Protocol (CbFP) is presented in this research as an innovative method to improve resource allocation and data transmission in wireless networks. The key objective of this work is allocating the resource in an optimal way; for that, the cuckoo search fitness function was utilised. The data packets are sent to the target node by the nodes based on the allocated resources. Results: The effectiveness of the framework was evaluated using the ns3 tool. To validate the improvements, the results were compared with existing models. The model achieved an impressive throughput of 50 Mbps with a minimal transmission delay 10 ms and a packet drop 3%. Hence, the introduced model is highly suitable for the wireless network application to allocate resources optimally.
  Keywords: cuckoo search fitness function; flooding protocol; resource allocation; wireless network.
  DOI: 10.1504/IJWMC.2025.10075619
   
  
• An evolutionary game theory model for emergency supply allocation in disaster relief  
  by Liang Zhao, Junmei Li 
  Abstract: In the face of increasingly frequent emergencies such as natural disasters and public health crises, the efficiency and fairness of emergency supply allocation has become a critical concern. This paper proposes an evolutionary game model involving three key stakeholders the government, relief workers and disaster victims to analyse their strategic behaviours in emergency supply distribution. Under bounded rationality and information asymmetry, the model constructs a tripartite evolutionary game framework, derives replicator dynamic equations and identifies evolutionarily stable strategies for each party. Results show that emergency stockpiles, transportation efficiency and surplus levels significantly influence strategic choices. In particular, the shift to innovative disaster relief becomes stable when its capacity exceeds the combined capacity and surplus of the traditional model. Simulations further confirm that optimised reserves and cooperative distribution enhance system stability and promote rational responses from victims.
  Keywords: emergency material deployment; evolutionary game; strategy selection; evolutionary stabilisation strategy.
  DOI: 10.1504/IJWMC.2025.10075620
   
  
• An optimised radial-based flooding routing mechanism for energy management and security in wireless network  
  by S. Mary Evanchalin, R. Ravi 
  Abstract: The advancement of the wireless network has increased the use of communication and information-sharing technologies in numerous applications. The key issues that suppress the efficiency of wireless networks are energy utilization and security. Often high energy consumption node remains malicious. Several prediction and energy efficient models were existing, but it lacks in energy management due to limitation of control features and node’s random behavior. So, the intelligent attributes are required for the node monitoring and energy management. Therefore, a novel Pufferfish-based Radial Flooding Routing (PbRFR) model is developed in this research to enrich the energy efficiency and security of wireless networks. Primarily, the required communication nodes were established in the network. The model analyzes the behavior of the nodes to detect the higher energy-utilizing malware nodes. Subsequently, the detected nodes were removed from the wireless communication environment and gained the finest outcome.
  Keywords: wireless network; security; pufferfish optimisation; cluster head; routing.
  DOI: 10.1504/IJWMC.2025.10075621
   
  
• Foreign language teaching methods based on cross-temporal and cross-cultural virtual simulation technology  
  by Xingying Fan 
  Abstract: With the acceleration of the countrys opening-up to the outside world, the importance of foreign language teaching is becoming increasingly prominent. In response to the low efficiency and lack of opportunities for real-world practice in traditional foreign language teaching methods, a foreign language teaching model based on cross-temporal and cross-cultural virtual simulation technology is proposed. A new target recognition algorithm combining back propagation neural network and seagull optimisation algorithm is designed. The algorithm optimises the parameters of BP neural network by simulating the migration and predation behaviour of seagulls, and finally, a new cross-temporal and cross-cultural virtual simulation technology is constructed. The results showed that the system response time was only 250 ms, the resource consumption rate was 45% and the error processing time was 5 s. Experiments showed that volunteers using the designed system significantly improved their English scores, with an average score increasing from 68.3 to 80.2. The conclusion indicates that the proposed foreign language education model can effectively improve the quality of foreign language teaching, providing strong support for cultivating talents with cross-cultural competence and cultural confidence.
  Keywords: virtual simulation; foreign language teaching; cross-cultural; cross-temporal; virtual reality.
  DOI: 10.1504/IJWMC.2025.10075622
   
  
• Corrosion of reinforcement detection system based on wireless sensor network and multi-sensor data fusion  
  by Zi Yang Shang, Along Yu, Hong Bing Sun, Lei Huang 
  Abstract: In order to monitor the internal status of steel bars in a timely manner and improve the monitoring system, this article proposes a data fusion method based on improved Beetle Antenna Search Algorithm based on Back Propagation to predict steel bar corrosion. Combined with wireless sensing technology, the algorithm improves the single beetle optimized in the Beetle whisker algorithm(IBAS-BP) to a group of beetles.The improved algorithm was compared with the other three algorithms in terms of training and testing, and the results showed that the IBAS-BP algorithm based prediction model for steel corrosion in reinforced concrete had faster convergence speed and higher prediction accuracy. And through the Raspberry Pi gateway, a steel bar corrosion monitoring system was built using the built-in Python processing algorithm. The experimental results show that the system can effectively monitor the corrosion of reinforcing steel bars in concrete, which can provide a reliable basis for timely and effective maintenance.
  Keywords: improved BAS-BP algorithm; monitoring system; wireless sensor network; multi-sensor.
  DOI: 10.1504/IJWMC.2024.10075624
   
  
• Sentiment analysis model for evaluating foreign language courses in colleges and universities based on improved Bi-LSTM and attention mechanisms  
  by Ran Li 
  Abstract: With growing demand for sentiment analysis in evaluating college foreign language courses, traditional rule- or statistics-based methods struggle with multilingual mixing, cultural differences, and domain-specific vocabularies. To overcome these challenges in classifying fine-grained sentiment (positive, neutral, negative) in feedback, this study proposes a Bi-LSTM with CNN sentiment model. Experiments show the model achieves high classification accuracy: 100%, 99.3%, and 98.8% for positive, neutral, and negative sentiments, respectively. Compared with traditional methods, accuracy improved by 15%. The model also achieved P, R, and F1 scores of 98.31%, 97.89%, and 98.23% for positive, 97.56%, 97.34%, and 98.06% for negative, and 95.13%, 95.65%, and 95.66% for neutral sentiments. In conclusion, the model provides an efficient, accurate tool for evaluating foreign language courses, enhancing sentiment analysis in higher education.
  Keywords: Bi-LSTM; CNN; attention mechanism; course evaluation; sentiment analysis.
  DOI: 10.1504/IJWMC.2025.10075691
   
  
• Detection and assembly of components based on automatic obstacle avoidance algorithm and BIM  
  by Xuan Deng 
  Abstract: To improve the assembly effect of buildings, a component detection and assembly method based on automatic obstacle avoidance and BIM building information model is proposed. After constructing the kinematic model of robot arm using BIM, the improved RRT algorithm and the improved YOLOv5 network are respectively utilized for the automatic obstacle avoidance of robot arm and accurate detection of precomponents. The results show that after the integration of the two algorithms, the obstacle avoidance accuracy and detection accuracy of building components have been significantly improved. The collaboration of the two methods can further improve the actual construction efficiency of prefabricated building components, and reduce construction costs, thus achieving the maximization of construction benefits.
  Keywords: BIM; automatic obstacle avoidance; detection of building prefabricated component; RRT algorithm; YOLOv5 network.
  DOI: 10.1504/IJWMC.2025.10075869
   
  
• MultiParamNet: a multi-parameter fusion approach for anomaly detection in DC power supply systems  
  by Yu Zhang, Chuanqi Shen, Jinlong Zhang, Lutong Zhang, Luansong Yue, Mingyue Fan, Wei Liu, Qing Lei, Shengnan Cui 
  Abstract: To address the limitations of traditional abnormal state detection methods for Direct Current (DC) power systems, this paper proposes an intelligent anomaly detection approach based on multi-parameter fusion. The proposed method fully exploits the temporal dependencies and cross-domain correlations among multiple heterogeneous parameters, including voltage, current, temperature, battery state, communication signals and control logic, thereby constructing a unified high-dimensional feature representation. A deep temporal modelling network is developed by integrating Gated Recurrent Units (GRU) with a multi-scale attention mechanism, enabling accurate perception of dynamic operating states and early identification of abnormal behaviours. Furthermore, a contrastive learning-based self-supervised pre-training strategy is introduced to enhance the models generalisation capability and feature discrimination under limited labelled data. An isolation forest algorithm is then employed for graded classification and interpretability analysis of multiple types of anomalies. Experiments conducted on real-world data sets from a DC power supply system demonstrate that the proposed method achieves a high anomaly detection accuracy of 95.2%, significantly outperforming traditional statistical models and state-of-the-art deep learning approaches.
  Keywords: multi-parameter fusion; anomaly detection; deep temporal modeling; self-supervised learning; operational state assessment.
  DOI: 10.1504/IJWMC.2025.10075924
   
  
• Comparative analysis of MU detector in LDPC coded LS MIMO OFDM  
  by Shefin Shoukath, P.A. Haris 
  Abstract: Large Scale Multiple Input Multiple Output (LS MIMO) combined with Orthogonal Frequency Division Multiplexing (OFDM) is a key technique by prevaling the fading effects of wireless channel. The application of Low Density Parity Check codes (LDPC) to LS MIMO OFDM systems with hundreds of antennas at the receiver has been proposed in this paper. MIMO technology with channel coding like Low Density Parity Check codes (LDPC) is a promising solution in achieving data rate transmission with low probability of error. The multiuser signals transmitted to the Base Station (BS) are detected by low complexity linear MMSE detector. LDPC coded LS MIMO OFDM system is designed so as to to decode the symbols at low complexity with linear minimum distance which thus enables to attain low bit error rate. In this paper a comparative analysis of the performance of multiuser detector in LS MIMO OFDM is presented. Characteristics of multiuser detection in LDPC coded LS MIMO OFDM is analysed by Bit Error Rate (BER), outage probability and radiation pattern of the detected signal. Simulation outcomes indicates the detector performance of LDPC coded system outperform that of an uncoded system.
  Keywords: low density parity check coding; LS MIMO OFDM; multiuser detection; MMSE; scattering channel; BER; outage probability.
  DOI: 10.1504/IJWMC.2023.10075929
   
  
• Optimisation of BiLSTM for end mills wear predictive study  
  by Chunlong Zou, Lin Zhou, Chen Wang, XuXIang LU 
  Abstract: An effective method of milling tool wear monitoring is important to improve product quality and extend the life of milling tools. A Genetic Algorithm (GA) optimised Bidirectional Long- and Short-Term Memory Neural Network (BiLSTM) deep learning model (GA-BiLSTM) is proposed to monitor the wear value of end mills. First, the important time-domain, frequencydomain and time-frequency features of the cutting tool wear signal are extracted by wavelet transform, and then processed by cross-validation and correlation analysis to obtain the input time-series samples for the prediction model. Then, the model parameters of the BiLSTM are optimised by GA, mainly optimising the learning rate (Learning Rate), generation (Epoch) and loss rate (Dropout). At last, the comparison experiment of different models is carried out: GA-BiLSTM is better than RNN, LSTM, CNN-BiLSTM and its evaluation index MAE and RMSE are lower than the comparison model, and the model operation time cost is in the reasonable scope. It shows that GA-BiLSTM method is effective and feasible, and improves the precision of wear prediction.
  Keywords: milling tool wear; genetic algorithm; BiLSTM; wear monitoring.
  DOI: 10.1504/IJWMC.2025.10076158
   
  
• Construction of an online vocal teaching model based on node influence and knowledge graph  
  by Ming Tian 
  Abstract: In response to the cold start and data sparsity issues faced by online teaching resource recommendation methods, this study first proposes using a node influence model to measure the influence of entities in the knowledge graph. Then, a vocal teaching resource recommendation model and learning effectiveness evaluation model based on long short-term memory network were established. The results show that the recommendation accuracy of the proposed model is 68.23%, and the area under the curve is 73.76%, which is 5.06% and 7.34% higher than the traditional RippleNet model, respectively. The accuracy and recall of the proposed learning effect prediction model are 0.525 and 0.224, respectively, which are 87.43% and 25.45% higher than traditional long short-term memory networks. The experimental results have demonstrated the recommendation and evaluation performance of the proposed model, which helps to improve teaching effectiveness and promote the development of online vocal teaching.
  Keywords: node influence; vocal teaching; knowledge graph; resource recommendation.
  DOI: 10.1504/IJWMC.2025.10076197
   
  
• Progression in FPGA logic blocks towards efficient hybrid architectures  
  by Neeti Malik, Sunita Dahiya 
  Abstract: Ever since the introduction of FPGAs in 1980s, they have witnessed a tremendous growth and are being widely accepted as a compelling alternative medium for the design of digital circuits due to their re-programmability and faster time to market. However, the FPGAs suffer from a major drawback that they have significantly less logic density and lower speed performance as compared to standard cells. These two important FPGA metrics, i.e., speed and area-efficiency are primarily influenced by the logic block architecture. Hence, the proper selection of FPGA logic block architecture is must for addressing the speed area deficiencies. In this paper, a comprehensive literature survey over three decades of FPGA logic block architectures is presented, keeping in view their impact on devices speed and area efficiency. It is observed that there has been a considerable shift in the choice of FPGA designers from identical logic blocks towards hybrid logic blocks comprising different types of logic resources.
  Keywords: LUT; reconfigurable; logic block; hybrid; heterogeneous; fracturable.
  DOI: 10.1504/IJWMC.2024.10076339