Optical Memory and Neural Networks最新文献

{"title":"Interconnection Tensor Rank and the Neural Network Storage Capacity","authors":"B. V. Kryzhanovsky","doi":"10.3103/S1060992X25600272","DOIUrl":"10.3103/S1060992X25600272","url":null,"abstract":"<p>Neural network properties are considered in the case of the interconnection tensor rank being higher than two (i.e., when in addition to the synaptic connection matrix, there are presynaptic synapses, pre-presynaptic synapses, etc.). This sort of interconnection tensor occurs in realization of crossbar-based neural networks. It is intrinsic for a crossbar design to suffer from parasitic currents: when a signal travels along a connection to a certain neuron, a part of it always passes to other neurons’ connections through memory cells (synapses). As a result, a signal at the neuron input holds noise—other weak signals going to all other neurons. It means that the conductivity of an analog crossbar cell varies proportionally to the noise signal, and the cell output signal becomes nonlinear. It is shown that the interconnection tensor of a certain form makes the neural network much more efficient: the storage capacity and basin of attraction of the network increase considerably. A network like the Hopfield one is used in the study.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 2","pages":"181 - 187"},"PeriodicalIF":0.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161092","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":"Design and Analysis of Compact All-Optical XOR and XNOR Gates Employing Microring Resonator","authors":"Manjur Hossain","doi":"10.3103/S1060992X24601362","DOIUrl":"10.3103/S1060992X24601362","url":null,"abstract":"<p>The manuscript includes the analysis and implementation of compact XOR and XNOR gates all-optically using microring resonator. Research on simultaneous logic and its inverse operation in a single circuit is crucial and productive in the field of optical computing. In addition, energy-efficient circuits are becoming more and more crucial. XOR and XNOR logic gates are designed and analyzed at about 260 Gbps using MATLAB. The same design has also been verified by “Ansys Lumerical finite difference time domain (FDTD)” software. Footprint of the FDTD design is only 47.7 μm × 18.8 μm. This proposed XOR and XNOR are particularly useful for digital signal processing because of its small architecture and faster response times. The evaluation and analysis of a few performance-indicating variables includes “extinction ratio”, “contrast ratio”, “amplitude modulation”, “on-off ratio”, and “relative eye opening”. Optimized design parameters are chosen to implement the design experimentally.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 2","pages":"229 - 238"},"PeriodicalIF":0.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161094","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":"Heart Disease Prediction and Classification Using LSTM Optimized by Butterfly Optimization","authors":"C. Usha Nandhini,&nbsp;P. R. Tamilselvi","doi":"10.3103/S1060992X25700043","DOIUrl":"10.3103/S1060992X25700043","url":null,"abstract":"<p>Heart disease is a primary cause of disability and premature mortality globally. Coronary heart disease is the most prevalent kind of heart disease, which happens when plaque builds up inside the arteries that feed blood to the heart, making blood circulation difficult. Heart disease prediction is a difficult task in clinical machine learning. However, various existing systems are utilized to detect the type of heart disease but those approaches are time-consuming and inaccurate to detect the disease at early stages. To address various issues, a deep learning framework has been developed to achieve accurate disease classification. Initially, data’s are collected and pre-processed using a Sequential K-Nearest Neighbors (SKNN) technique for missing value replacement. The data is then subjected to decimal scaling normalization to enhance its integrity and uniformity. Then, reducing the dimension of the feature vector by applying Multilinear Principal Component Analysis (MPCA). Butterfly optimization (BOA) is employed to determine the ideal quantity of components to enhance the accuracy of the proposed model. In order to determine the different forms of cardiac disease, characteristics are classified subsequently using Long Short-Term Memory (LSTM). To evaluate the planned model’s performance, performance measures from the proposed and existing models are compared. Performance measures include Sensitivity, MCC, Negative Predictive Value (NPV), False Discovery Rate (FDR), Accuracy, Precision, Error, Specificity, F1-score, False Negative Rate (FNR), False Positive Rate (FPR), False Negative Rate (FNR), and False Positive Rate (FPR) attained for the proposed model is 96.5, 95, 3.5, 95.9, 95.5, 94.7, 95.7, 2.8, 3.7, 90.9, 93.2, 95.7 and 2.9%. In comparison to other existing techniques, the proposed technique performs better. In order to determine the type of heart disease, the created model is the best choice.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 2","pages":"272 - 284"},"PeriodicalIF":0.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145160995","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":"Short Term Solar Power Generation Prediction Based on IOT Using SMA Optimized DNN55","authors":"Saziya Tabbassum","doi":"10.3103/S1060992X25700018","DOIUrl":"10.3103/S1060992X25700018","url":null,"abstract":"<p>Short-term forecasting includes predictions for a period of one to six hours, is essential for scheduling the power generated using solar plant. To maintain a balanced and all-encompassing operation, models that enable the reliable short-term projection of solar PV generation in the future must be developed. These circumstances increase the level of uncertainty in this solar parameter forecast. Examining deep neural networks as a potential solution for the problem of anticipating electricity demand 24 h in advance is the primary objective of this study. The real time dataset was gathered from the solar farm which contains temperature, irradiance, power, using sensors. The data quality is then improved by pre-processing which contains missing values and normalization. Then elastic net and chi-square are utilized for feature selection and validation process in the pre-processed data. These specific datasets are used to assess and train the improved DNN55 classifier, which predicts solar power. The accuracy of the DNN55 short-term power forecasting is increased by using data augmentation. Using performance metrics including accuracy, precision, recall, and specificity, the efficacy of the proposed methodology is evaluated; results show 95.29, 95.36, 95.37, and 97.63% respectively. Thus, the proposed deep learning technique has been optimized to detect solar power in the short term more accurately.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 2","pages":"217 - 228"},"PeriodicalIF":0.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161093","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":"Sentiment Analysis of Twitter Big Data Using Hybrid HNN-BiGRU and Semantic Lexicons with Puffer Fish Optimized Glove","authors":"Bondili Naga Sai Bhavya Charitha,&nbsp; Ramanchi Radhika","doi":"10.3103/S1060992X25700080","DOIUrl":"10.3103/S1060992X25700080","url":null,"abstract":"<p>Twitter has millions of active users and is a significant microblogging platform. These users use Twitter to give their thoughts on various events using hashtags also to make status updates known as tweets. As a result, Twitter is regarded as a significant real-time streaming source as well as a reliable and accurate opinion indicator. Due to Twitter’s massive data generation volume, it is challenging to manually scan the entire collection. Given the massive volume of data supplied by Twitter, it is challenging to manually scan the entire collection. So, a hybrid deep learning algorithm is developed to analyse the sentiment of the user. This research incorporates a variety of techniques like pre-processed using tokenization, stop word removal, stemming, Removal of hyperlinks and numbers, Abbreviation extending and spell correction. After that, use Semantic Lexicons with Puffer Fish Optimized GLOVE (SLPFOG) to extract features and convert words into vectors. The, reduce the dimension of the extracted features by applying the Laplacian Eigen map. To forecast the user sentiment of Twitter Big data, a hybrid Hopfield Neural Network—Bidirectional Gated Recurrent Unit (HNN-BiGRU) technique was created. The proposed hybrid HNN-BiGRU approach has an accuracy of 96%, specificity of 99%, NPV of 99% and MCC of 97%. Thus, the hybrid deep learning algorithm is the best option for sentimental analysis of twitter big data because they achieves relatively high accuracy with respect to basic algorithms without sacrificing the interpretability of the learning results.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 2","pages":"115 - 127"},"PeriodicalIF":0.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161161","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":"Default Brain System in Schizophrenia","authors":"A. Vartanov,&nbsp;M. Krysko,&nbsp;D. Leonovich,&nbsp;O. Shevaldova,&nbsp;S. Mirova,&nbsp;A. Zeltser,&nbsp;V. Zakurazhnaia,&nbsp;A. Ochneva,&nbsp;D. Andreyuk,&nbsp;G. Kostyuk","doi":"10.3103/S1060992X25700067","DOIUrl":"10.3103/S1060992X25700067","url":null,"abstract":"<p>The default mode network (DMN), also referred to as the “Passive Mode Brain Network” (PMBN), serves as a network of active brain regions while restfully stated. An abnormal homogeneity of the DMN network has been implicated in the first episode of schizophrenia, a mental disorder characterized by perceptual disturbances. This study aimed to investigate the activity and functional connectivity of the DMN in female schizophrenia patients using an innovative brain activity localization technique known as the “Virtually implanted electrode”. EEG was registered in 22 female patients diagnosed with schizophrenia, including 17 cases of F20, 3 cases of F23, and 22 healthy controls, being in a state of quiet wakefulness. The results indicated a complex system of changes in schizophrenia patients compared to controls, attributed to weakening connections originating from structures with reduced activity and reinforcing of other connections, including inhibitory ones. These findings underscore the neurobiological basis of schizophrenia, investigating the DMN.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 2","pages":"206 - 216"},"PeriodicalIF":0.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161162","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":"Topological Charge of Co-Axial Superposition of Gaussian Optical Vortices","authors":"V. V. Kotlyar,&nbsp;A. A. Kovalev,&nbsp;A. G. Nalimov","doi":"10.3103/S1060992X25600296","DOIUrl":"10.3103/S1060992X25600296","url":null,"abstract":"<p>In this work, we analyze the topological charge (TC) of finite superposition of optical vortices (OVs) with a Gaussian envelope. In the source plane, the superposition under study is theoretically and numerically shown to have the TC equal to the number of zeros of a complex polynomial of degree <i>n</i>, where <i>n</i> is the largest TC of the constituent OVs found inside and on a unit-radius circle. Meanwhile upon free space propagation, the TC of the superposition always equals <i>n</i>. We reveal that if, in absolute values, the coefficient of a superposition term with TC = <i>k</i> is larger than the sum of all the rest superposition coefficients, then <i>k</i> zeros occur inside the unit-radius circle, with the total TC of the superposition being equal to <i>k</i> (<i>k</i> ≤ <i>n</i>) in the source plane. If all the coefficients are equal to each other in the absolute value, then, in the source plane, TC takes a value of <i>n</i>/2, before returning to the value of <i>n</i> upon propagation. In this case, extra zeros of the superposition of OVs occur almost at once, at a subwavelength distance from the source plane, with the distance from the optical axis being larger than the radius of an aperture limiting the source field.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 2","pages":"169 - 180"},"PeriodicalIF":0.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161091","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":"Intrusion Detection Using SA-BiLSTM and Enhanced Deep RL Routing with Modular Homomorphic Encryption for Secure Data Transmission in VANET","authors":"T. Pavithra,&nbsp;B. S. Nagabhushana","doi":"10.3103/S1060992X24601052","DOIUrl":"10.3103/S1060992X24601052","url":null,"abstract":"<p>Vehicular Ad Hoc Network (VANET) has become a revolutionary and creative technology that serves as an essential part of Intelligent Transportation Systems (ITS). However, due to their wireless nature and complex operating environment, VANETs are vulnerable to a range of malicious user assaults. It is critical to identify intrusions in the VANET system in order to provide reliable and secure communication among all of the system’s vehicles. Traditional methods are no longer effective due to some limitations like lack of data, interpretability and imbalance classes. Therefore, the proposed approach developed an enhanced deep RL routing (EDRL) with SA-BiLSTM for the detection of intrusion and created a secure VANET system employing modular Homomorphic encryption. In this proposed model, consider if any incident happens on the road, vehicles in that sector are grouped by utilizing the Improved K harmonic means clustering algorithm (IKHM), and the CH is determined according to its minimal distance and highest energy using the Greater Cane Rat Algorithm (GCRA) optimization. The EDRL routing technique is then used to exchange the data to RSU for choosing the appropriate route. RSU discovered the different types of attack and non-attack using Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) classifier. Then the non-attack data are encoded using the Modular Homomorphic Encryption (ModHE) and uploaded in the cloud to intimate the warning message to the vehicular networks. The proposed model’s performance parameters are examined, and the results show that, for 500 vehicle nodes, the outcomes are 82.2% PDR, 13.65J energy usage, 20.3% routing overhead, 18.7 mbps throughput, and 11.22 delay. Accuracy, hit rate, and PPV are assessed at 96.3, 96.7, and 95.8%, respectively, for attack detection. Furthermore, the execution time and encryption take 16.63 and 46.03 milliseconds, respectively. The mentioned results demonstrated that the proposed framework outperformed earlier methods in providing a remarkably energy-efficient as well as secure V2X communication network.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 2","pages":"188 - 205"},"PeriodicalIF":0.8,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145161090","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":"Polarization Singularity Index and Orbital Angular Momentum of Vector Light Fields","authors":"V. V. Kotlyar,&nbsp;A. A. Kovalev,&nbsp;S. S. Stafeev","doi":"10.3103/S1060992X24602100","DOIUrl":"10.3103/S1060992X24602100","url":null,"abstract":"<p>Besides scalar optical vortices that have a topological charge (TC), helical wave front, and carry an orbital angular momentum (OAM) that can be transferred to particles and rotate them along circular trajectories, polarization optical vortices are also known, whose polarization state in the beam section changes with the azimuthal angle. Such vortices are polarization singularities that are described by indices, similar to the TC. However, polarization OAM for polarization vortices still has not been considered, although laser beams with inhomogeneous polarization can perform spiral mass transport in polarization-sensitive media. In this work, we consider two possible definitions of the polarization OAM. One OAM is proportional to the azimuthal velocity of changing direction of linear polarization vector, whereas the other (hybrid OAM) is proportional to the azimuthal velocity of changing ellipticity degree of the polarization ellipse. For instance, the normalized polarization OAM is equal to the order of a cylindrical vector beam and also equals the order of Poincaré beam.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 1","pages":"49 - 62"},"PeriodicalIF":1.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840450","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":"WHO Based K-Means Segmentation Algorithm and Hybrid VGG19-SVM Model for Identifying COVID-19 Patients in Chest X-Ray","authors":"Ranjana Kumari,&nbsp;Rajesh Kumar Upadhyay,&nbsp;Javed Wasim","doi":"10.3103/S1060992X24700905","DOIUrl":"10.3103/S1060992X24700905","url":null,"abstract":"<p>COVID-19 was thought to be the most lethal and devastating disease for humans caused by the novel coronavirus currently. Accurate diagnosis may lead to earlier COVID-19 discovery and lower patient mortality, especially in instances without evident symptoms. The majority of the time, chest X-ray (CXR) images are used to diagnose this illness. Patients who are infected with coronavirus exhibit symptoms that were very similar to those of pneumonia, and the virus targets body’s respiratory organs, making breathing difficult. This paper presented a hybrid VGG19-SVM model for identifying COVID-19 patients in CXR based on wild horse optimizer (WHO) based K-means segmentation to address these problems. The proposed segmentation algorithm comprises four phases such as data gathering, pre-processing, segmentation and COVID-19 detection. CXR data were gathered from medical Internet of Things (IoT) devices. Image pre-processing was performed with the assistance of image resizing, Markov random field (MRF) and adaptive gamma correction (AGC). Then, the proposed WHO based K-clustering is used to segment the affected portion of lung CXR effectively. The hybrid classification approach is introduced based on the combination of VGG19 and SVM, which is employed to classify if the patient is in normal condition either COVID-19, pneumonia or tuberculosis. Thus, various existing methods such as VGG19, AlexNet, VGG16 and GoogleNet are taken in this analysis. The proposed VGG19-SVM attained 0.96 of F1_score, 0.97 of NPV, 0.07 FNR and 0.008 of FPR, when compared to the existing methods obtained better findings using DL techniques. This shows the effectiveness of the proposed WHO based K-means clustering algorithm and hybrid VGG19-SVM model which can be useful for segment the CXR images.</p>","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"34 1","pages":"95 - 114"},"PeriodicalIF":1.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143840394","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}