Optical Memory and Neural Networks最新文献

{"title":"Energy-Efficient Tree-Based Routing Algorithm with Attention Based Kolmogorov–Arnold Networks for Attack Detection in WSN","authors":"Neha Jagwani,&nbsp; Poornima Govindaswamy","doi":"10.3103/S1060992X25601058","DOIUrl":"10.3103/S1060992X25601058","url":null,"abstract":"<p>Wireless Sensor Network (WSN) is made up of sensors that simultaneously sense, process, and transmit data. WSN routing protocols are vulnerable to the security risk and it is ineffective at managing dynamic network situations, like fluctuating node mobility or shifting ambient conditions. While routing, the early failure of node in WSN is caused by the imbalanced consumption of power and a dissimilar distribution of sensor nodes. So, to overcome these challenges Energy Efficient Tree based Routing Algorithm (EETRA) combined with deep learning algorithm is developed to achieve reliable and secure data transmission. Initially, the nodes are placed in specific locations and grouped into both local and global clusters, and identifies the cluster head using Dual tier zonal stable election protocol (DTZSEP). Energy Efficient Tree based Routing Algorithm (EETRA) is used for data transmission between cluster head to base station to minimize network energy consumption. WSNs are highly vulnerable to security attacks, so the attack detection is crucial in WSN. To perform attack detection, the network characteristics is subjected to pre-processing which is done through Attention-Based Generative Adversarial Networks (ATTN-GAN) and Max Normalization (MA) for imputing the missing values and normalizing the data. Finally for classifying the attack, the Modified Attention based on Kolmogorov–Arnold Networks (MAKAN) is implemented in which the attention layer is integrated by the shuffle attention layer to extract the features efficiently. This proposed Energy Efficient Tree-Based Routing Algorithm (EETRA) was compared with the existing models, which attains higher performance such as average residual energy has 17.4J, the throughput value is 388Gbps, 98% packet delivery ratio and 0.001ms of transmission delay. In the classification, the proposed MAKAN attains the accuracy, selectivity, NPV and error values of 98.30, 96.20, 97.70 and 1.70% respectively which are further compared with existing approaches. These proposed integrated technique enables data transmission in secure and efficient manner by accurately detecting the attack in wireless sensor networks.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 1","pages":"78 - 97"},"PeriodicalIF":0.8,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147560977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stylize Aesthetic Mechanism Based QR Generation and Two-Step Multimodal Biometric Authentication System using PINN-FORM for Secure Healthcare Data","authors":"M. Shailaja,&nbsp; Satish Thatavarti","doi":"10.3103/S1060992X26700037","DOIUrl":"10.3103/S1060992X26700037","url":null,"abstract":"<p>Biometric authentication powered by Artificial Intelligence (AI) has arisen as a vital solution for ensuring secure access to digital healthcare data. By leveraging advanced AI-driven algorithms, such systems can accurately recognize and verify users based on unique biological traits. However, various existing authentication method suffer from limitations such as noise distortion, poor illumination handling, redundant features, weak multimodal fusion and reduced capability in distinguishing between genuine and fake biometric inputs. To address these challenges, physics inspired deep learning based two-step biometric authentication verification framework is developed to enhance healthcare data protection. The system integrates originality verification of iris and fingerprint modalities to achieve high reliability and precision in healthcare user verification. Initially, biometric images like fingerprint and iris are pre-processed using Wavelet-Inspired Invertible Network (WINNet) for Denoising and the Detail-Enhanced Attention Network (DEA-Net) for illumination and contrast enhancement. Textural features are then extracted using the Hexadecimal Local Adaptive Binary Pattern (HLABP) technique. Both finger print and iris features are adaptively combined through the Adaptive Feature Fusion Mechanism (AFFM) to minimize redundancy and improve representational strength. Finally, the Physics-Informed Neural Network based First-Order Reliability Method (PINN-FORM) classifiers performs biometric recognition to differentiate real from fake data. Upon successful verification, a secure QR code is generated through the Stylize aEsthEtic (SEE) mechanism exclusively for legitimate users. The users then scan the QR code for further biometric verification. If the biometric matches, the user can access the data, otherwise the request is declined. The suggested multimodal biometric authentication framework demonstrates exceptional performance, achieving a precision rate of 97.87%, a F1-Score of 97.82%, and an accuracy of 97.77%. This proposed approach significantly improves accuracy and reliability, ensuring stronger digital data security in healthcare systems.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 1","pages":"190 - 205"},"PeriodicalIF":0.8,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147560980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CWG-YOLOv8: a Navel Orange Detection Model Based on Improved YOLOv8 in an Agricultural Environment","authors":"Changgeng Yu,&nbsp;Jinfeng Guo,&nbsp;Quansheng Pan","doi":"10.3103/S1060992X24601325","DOIUrl":"10.3103/S1060992X24601325","url":null,"abstract":"<p>The performance of picking robots for fruit object detection is crucial in agricultural environments. However, most existing detection models struggle to perform well in agricultural settings due to problems in detection accuracy, computational resource consumption, and real-time processing. To address these challenges, we propose a navel orange detection model called CWG-YOLOv8 based on YOLOv8, which can achieve accurate detection of navel oranges in agricultural environments. Firstly, we introduce a Convolutional Block Attention Module (CBAM) to enhance the backbone network and improve the generalization ability of the model. Secondly, Wise-IoU (WIoU) v3 as the bounding box regression loss function is employed, and a wise gradient allocation strategy is incorporated to emphasize high-quality samples, thus enhancing the model’s localization capability. Finally, we design a lightweight GhostNet module that effectively integrates shallow and deep features to reduce computational cost and speed up detection. The experimental results show that the number of parameters and the number of floating-point operations (FLOPs) of our model are reduced by 42.35 and 36.59% compared with the original model. After optimization and parameter training, the mean average precision average (mAP50) and mAP50∼95 of the model reached 95.1 and 83.3%, respectively. Compared with other mainstream models, our model demonstrates significant advantages in terms of detection accuracy, speed, and lightweight design, which meets the requirements of high real-time navel orange detection in agricultural environments.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 1","pages":"147 - 155"},"PeriodicalIF":0.8,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147561407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From One to Many: Adaptive Multi-Agent Pathfinding in Heterogeneous Environments","authors":"M. Nesterova,&nbsp;A. Skrynnik,&nbsp;A. Panov","doi":"10.3103/S1060992X26700025","DOIUrl":"10.3103/S1060992X26700025","url":null,"abstract":"<p>Multi-agent pathfinding (MAPF) is a common abstraction of multi-robot trajectory planning problem, where multiple homogeneous robots simultaneously move in the shared environment. This paper addresses the heterogeneous MAPF problem, where a group of adaptive agents interacts with other agents (called impostors) that behave differently. The task remains cooperative, all agents should have the opportunity to reach their goals. We investigate how homogeneous methods can be enhanced for heterogeneous settings through three distinct approaches: planning-based, sampling-based, and learning-based methods. Our experimental framework employs the POGEMA benchmark to evaluate adaptive agents interacting with impostors following different policies (A* and PIBT). Our results demonstrate that all methods show significant performance improvements primarily with large agent populations, where frequent encounters with impostors necessitate conflict resolution. These findings indicate that while predictive modeling can enhance non-specialized algorithms when online training is impractical, learning-based methods offer superior adaptability to novel agent types in dynamic heterogeneous environments.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 1","pages":"43 - 54"},"PeriodicalIF":0.8,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147560976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Time and Secure Patient Monitoring in WBAN-IoMT Using Intelligent Routing and Threat Detection for Low-Latency Communication and Intrusion Prevention","authors":"U. Hariharan,&nbsp;Chin-Shiuh Shieh,&nbsp;Mong-Fong Horng","doi":"10.3103/S1060992X25603057","DOIUrl":"10.3103/S1060992X25603057","url":null,"abstract":"<p>Wireless Body Area Networks (WBANs) are precisely defined as wireless networks comprising various sensors strategically positioned on the human body, these sensors have been either worn externally on the body or surgically implanted beneath the skin. Sensitive information is susceptible in many ways when it is transmitted across unsecure networks, therefore robust security measures are necessary to guard against possible attackers. Thus, the proposed model developed a secure and efficient patient monitoring using ElGamal-LCA for encryption with routing algorithm and MSDGCN based intrusion detection system. The process begins with a WBAN employing 12 sensors such as ECG, EMG, PPG, EEG, temperature, blood pressure, SPO2, respiration rate, accelerometer, glucose, gyroscope and galvanic skin response for capturing vital physiological signals from the human body. Then these readings are sent to a control unit which further aggregates the sensor data. For securing the data transmission Elgamal-Lightweight Cryptography Algorithm (Elgamal-LCA) is employed. Elgamal cryptosystem handles key generation while lightweight encryption encrypts the data. The data transmission causes interchannel interference due to overlapping signal from same or adjacent channels which are mitigated by utilizing a Stochastic Learning Algorithm (SLA) to prevent data loss and collisions. Once if interference is mitigated, data is transmitted to base station using Quality of service (QoS) based Minimal Latency Routing Strategy. At the base station intrusion detection was performed and the process involves preprocessing using Hyperbolic Tangent (HT) normalization and Slim Generative Adversarial Imputation Network (SGAIN) for imputing missing data followed by classification utilizing Modified Spatial Dynamic Graph Convolutional Network (MSDGCN) with Dynamic Composable Multi-Head Attention (DCMHA) for effective detection. Finally, alerts are sent if an intrusion is found otherwise data is stored securely in the cloud. The proposed approach achieves an execution time of 77.51 sec, packet loss of 4.7%, accuracy of 99.17% and F1_Score of 98.68%. The proposed approach provides an effective transmission in WBAN-IoMT ensure secure data forwarding and enabling low latency communication with intrusion prevention for enhanced patient care.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 1","pages":"168 - 189"},"PeriodicalIF":0.8,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147561438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secure Cloud-Enabled IoT Framework for Colon Cancer Prediction and Diagnosis using ARNN with Levenberg–Marquardt Optimization","authors":"Ashish Tripathi,&nbsp;Anuradha Misra,&nbsp;Kuldeep Kumar","doi":"10.3103/S1060992X25602350","DOIUrl":"10.3103/S1060992X25602350","url":null,"abstract":"<p>Colon cancer is a type of cancer that affects the colon (large intestine) or rectum. It is one of the most common kinds of cancer worldwide and can cause severe harm and death<b>.</b> Early detection of colon cancer is especially difficult because of cancer cells overlap, making identification more difficult. Also, classifying cancerous cells in histopathology images is difficult due to the complex inter-class and intra-class dependencies. It can be challenging to distinguish between normal and cancerous cells because the underlying tissue structures often merged and have similar morphological structures. This convolution of structural features contributes additional complexities that hinder accurate evaluation and identification. To address these drawbacks proposed an Artificial Recurrent Neural Network with Levenberg-Marquardt Method (ARNN-LMM) based elapid encryption to improve security and predict colon diseases using IoT-enabled devices. Initially, Colon Cancer Histopathological Images are collected to serve as the input image. In order to reduce the disturbances in the background, the pre-processing of the raw images is done first using a pixel-wise thresholding (PWT) method, which is used to provide the image with a better appearance in addition to reduction of the noise. A wavelet domain transformer (WavEnhancer) is then used to enhance clarity at the pixel level and hence effectively enhance the overall image quality. Circular Mesh Network (CirMNet) is a shape based feature extraction technique, which extracts structural, statistical, and property-based features of image. The refined features are fed into a classification employing ARNN-LMM to detect colon abnormality. The trained net model is encrypted using elapid encryption and stored in cloud for ensuring secure access. In Disease Prediction Phase, an Internet of Things (IoT) device captures patient data and transmitted to the cloud, where the model is decrypted and analyze the features to predict the patient has disease or non-disease by a trained model. An suggested model attains 97.45%, 2.55% and 97.44% accuracy, FPR and specificity in detecting the colon disease. Similarly the model enhances colon disease detection effectively capturing statistical features along with enhancing the security of the trained model with cryptography algorithm.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 1","pages":"206 - 222"},"PeriodicalIF":0.8,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147560981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FN-DeepCNN: Facial Expression Recognition Using Fine-Tuned Deep Convolutional Neural Network","authors":"Maryam Knouzi,&nbsp;Fatima Zohra Ennaji,&nbsp;Imad Hafidi","doi":"10.3103/S1060992X25600740","DOIUrl":"10.3103/S1060992X25600740","url":null,"abstract":"<p>In human-computer interaction, Facial Emotion Recognition (FER) is essential, particularly in fields like behavioral analysis and psychological therapy. Perceiving emotions accurately from facial expressions can enhance communication and interaction between humans and machines. However, the wide range of human faces and image variations, including different lighting conditions and facial poses, makes it difficult to achieve accurate and robust FER using computer models. In this work, we chose to work exclusively on the FER2013 dataset to address its complications and complexities in terms of feature extraction. To analyze its impact, we employed a Deep Convolutional Neural Network (DCNN) and pre-trained models that have been adjusted specifically for emotion recognition. This work takes into account a pre-processing step that concentrates on image resolution, histogram equalization, and data augmentation. We achieved a higher accuracy of 76% with the Inception model using the FER2013 dataset.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 1","pages":"156 - 167"},"PeriodicalIF":0.8,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147560979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Raga Recognition of Indian Classical Music Based on Audio Processing and Stacking Based Ensemble Learning Algorithm","authors":"J. Jayanthi,&nbsp;V. Upendran","doi":"10.3103/S1060992X25600594","DOIUrl":"10.3103/S1060992X25600594","url":null,"abstract":"<p>Raga identification is a significant issue in the field of Indian art music since ragas are essential to the composition and performance of the music and are vital to its preservation, education, and retrieval. There aren’t many studies that have looked into this task using techniques like Machine Learning (ML), signal processing, or, more recently, Deep Learning (DL). All of these researches, however, leave open a crucial question: do these ML/DL techniques learn and comprehend Ragas similarly to human experts? Furthermore, a major obstacle to this research is the lack of a large number of rich, labeled datasets, which is what motivates these ML/DL-based techniques. Advanced techniques like deep learning-based Bahdanau Attention-augmented Bidirectional LSTM (BAA-BiLSTM) is proposed to mitigate these drawbacks by better capturing raga nuances. Initially, audio recordings of various ragas from a music collection are collected and pre-processed using Particle Filter (PF) to minimize noise and Double Side Band Amplitude Modulation (DSBAM) for maintaining equal frequency and amplitude. The audio is segmented using Non-Stationarity-Based Adaptive Segmentation (NSAS) to separate pitch and vocals from noise and silence. These segmented signals features are extracted using Gammatone Frequency Cepstral Coefficients, Code Excited Linear Prediction, spectrum flux, short-term energy, and Recurrence Quantification Measure. Then the features are further given to the deep learning ensemble classifier which contains Bahdanau Attention-augmented Bidirectional LSTM (BAA-BiLSTM) meta-classifier with Recursive Tensor Neural Network (RTNN) and Jordan Neural Network (JNN) to identify ragas accurately. As a result of proposed model accuracy is 98.4%, precision is 92.7% respectively. Consequently, this method is highly suitable for real-time applications to identify Indian classical music’s ragas based on audio signals.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 1","pages":"130 - 146"},"PeriodicalIF":0.8,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147561324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Study of the Relationship between Dynamics of Subsurface Radon Concentration and Seismicity Using Neural Network Approach","authors":"I. E. Shepelev,&nbsp;A. V. Zhegulin,&nbsp;E. O. Makarov,&nbsp;P. O. Kosenko,&nbsp;D. V. Chebrov,&nbsp;V. N. Kiroy,&nbsp;F. V. Arsenyev","doi":"10.3103/S1060992X25601137","DOIUrl":"10.3103/S1060992X25601137","url":null,"abstract":"<p>Temporal changes of radon (²²²Rn) concentration in subsurface air are analyzed in comparison with ensuing seismicity on the basis of neural network methods. The data for analysis are represented by five-year series of one-site radon measurements, seismic activity observations and meteorological data. Changes in radon concentration are described quantitatively by windowed estimates of temporal, statistical and complexity features, which serve as material for the training neural network classification method to divide into conditionally “strong” and “weak” daily seismicity following in time. The multilayer perceptron based classification model tuning involves selection of informative features, search for optimal sizes of their estimation windows, and analysis of the seismicity categorization threshold. Two types of classification models were studied, which differed by the method of partitioning into conditionally “strong” and “weak” seismic events – partitioning based on the magnitude of events and intensity in points. It is shown that the best classification is achieved on a limited set of statistical and complexity features and reaches 83% for the neural network model based on intensity in points. We conclude that accurate feature extraction from temporal changes of subsurface radon concentration can give implicit precursors of earthquakes.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 1","pages":"31 - 42"},"PeriodicalIF":0.8,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147561439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secure and Energy-Efficient Data Transmission in Wireless Sensor Networks Using ANN and Enhanced LEACH Protocol","authors":"Saziya Tabbassum,&nbsp; P. Rajesh Kumar","doi":"10.3103/S1060992X25601447","DOIUrl":"10.3103/S1060992X25601447","url":null,"abstract":"<p>Wireless Sensor Networks (WSNs) consist of numerous small, multifunctional sensor nodes deployed in target areas to collect and transmit environmental data. Due to the energy constraints of battery-powered nodes, balancing energy consumption with network performance remains a significant challenge. To address this, an approach integrating Artificial Neural Network (ANN)-based outlier detection with a secure and energy-efficient data transmission mechanism using an enhanced LEACH protocol is proposed. Nodes are initialized in the sensing region to gather informations. Then Artificial Neural Network is utilized to identify the outliers from the deployed nodes. Then, cluster formation and cluster head selection are done by employing a stable and secure LEACH protocol. A hybrid reputation-based secure data transmission (HRSDT) is employed to transfer data from its base station after CH selection. Community and QoS reputation rates are employed for sensor node reputation calculation. Among all nodes, the node with the highest HRSDT is regarded as the forward node for safe data transfer. According to the assessment results, the proposed approach obtains 92% PDR and 1.82 Mbps throughput. Therefore, the proposed technique is the optimum option for WSN data transfer as it is simultaneously secured and energy-efficient.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 1","pages":"98 - 112"},"PeriodicalIF":0.8,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147560978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}