Optical Memory and Neural Networks最新文献_第4页

Reformed SARSA Algorithm Using Osprey Optimization for Effective Task Scheduling and Optimal Load Balancing in Cloud Computing 基于鱼鹰优化的改进SARSA算法在云计算中的有效任务调度和最优负载均衡

IF 0.9

Optical Memory and Neural Networks Pub Date : 2026-06-26 DOI: 10.3103/S1060992X25600405

G. M. Kiran, D. Kavitha, B. V. Satish Babu, K. Bala Brahmeswara, B. Annapurna

{"title":"Reformed SARSA Algorithm Using Osprey Optimization for Effective Task Scheduling and Optimal Load Balancing in Cloud Computing","authors":"G. M. Kiran, D. Kavitha, B. V. Satish Babu, K. Bala Brahmeswara, B. Annapurna","doi":"10.3103/S1060992X25600405","DOIUrl":"10.3103/S1060992X25600405","url":null,"abstract":"Cloud computing provides on-demand services with high performance and scalability over the Internet. The primary goals of task scheduling in cloud environments are to efficiently utilize available resources, minimize execution time, and ensure timely task completion. Load balancing is crucial to ensure that virtual machines (VMs) are evenly utilized. However, the key challenge in cloud computing lies in effectively balancing the load and scheduling tasks. In the proposed model, an optimal task-scheduling mechanism is designed using the State-Action-Reward-State-Action (SARSA) algorithm. The scheduling process is enhanced by the Osprey Optimization Algorithm (OOA) to select the best resources, minimizing execution time, cost, and resource utilization. Once the tasks are schelued in queue, the load balancing is carried out using the Walrus Optimization Algorithm (WaOA), which optimally balances the load across VMs based on task lifetime and response time. The performance of the proposed model is evaluated under various task conditions, and the results are compared to existing models for validation. The proposed approach demonstrates superior performance with a makespan time of 64.81 s, turnaround time of 7.05 s, waiting time of 203.48 s, response time of 70 s, scheduling time of 226 s, a success rate of 95.7%, 6.01% resource utilization, and 5.69 Mbps throughput. This method effectively minimizes resource consumption while ensuring optimal task scheduling and load balancing.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 2","pages":"352 - 371"},"PeriodicalIF":0.9,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323657","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}

引用次数: 0

Deep Learning Based Segmentation and Classification for Effective Iris Liveness Detection Using Various Quality Metrics 基于深度学习的虹膜活性检测分割与分类

IF 0.9

Optical Memory and Neural Networks Pub Date : 2026-06-26 DOI: 10.3103/S1060992X25600351

Vaishali C. Kulloli, Maheshwari S. Biradar

{"title":"Deep Learning Based Segmentation and Classification for Effective Iris Liveness Detection Using Various Quality Metrics","authors":"Vaishali C. Kulloli,  Maheshwari S. Biradar","doi":"10.3103/S1060992X25600351","DOIUrl":"10.3103/S1060992X25600351","url":null,"abstract":"Iris pattern recognition’s stability and uniqueness have significantly improved biometric authentication. The individual’s iris is the most promising biometric authentication method that can reliably identify an individual based on their unique characteristics. Traditional drawback of iris recognition systems is their vulnerability to such spoofing, which can compromise the effectiveness of biometric security, making it essential to enhance the detection of fraudulent attempts by the system. Iris recognition problems still exist even with sophisticated algorithms, especially in real-world settings where consumers might not be aware of any weaknesses. A deep learning-based segmentation and classification method for iris liveness detection utilizing quality metrics has been proposed in order to address this problem. Human eye images are first gathered and used as an input dataset. A Lightweight Attention Guided ConvNext Network (LACN) is used for image improvement and a Pixel Density based Trimmed Median Filter (PDTMF) to reduce noise. A Lightweight Dual Multiscale Residual Block-based Convolutional Neural Network (LDMRes-Net) is used for segmentation in order to recover the iris and pupil regions. These separated regions are used to calculate important iris and pupil region characteristics including as area, radius, and perimeter. Additionally, quality measures including pupil-iris ratio, iris-sclera contrast, iris-pupil contrast, grayscale utilization, and usable iris area are calculated. A deep learning classifier known as the Progressively Growing Adversarial Network with Dropout Layer (PGAN-DL) uses to determine iris liveness. Simulated findings show that the proposed technique achieves 98.1% accuracy, 97.2% PPV, and 6.2% miss rate for Pupil and 95.6% accuracy, 96.8% PPV, and 93.8% hit rate for Iris. Thus, the proposed approach is the most effective strategy for identifying iris liveness using quality criteria.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 2","pages":"256 - 275"},"PeriodicalIF":0.9,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323822","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}

引用次数: 0

Erratum to: Human Action Recognition from Motion Capture Data Using Machine Learning Techniques 使用机器学习技术从动作捕捉数据中识别人类动作的勘误

IF 0.9

Optical Memory and Neural Networks Pub Date : 2026-06-26 DOI: 10.3103/S1060992X26020013

V. Konovalov, A. Borisov, E. Myasnikov

引用次数: 0

Reliable Genomic Data Classification for Monogenetic Disorders Using AEGA-BoostFNN-MonoDx Hybrid Deep Learning Framework 使用aaga - boostfnn - monodx混合深度学习框架进行可靠的单基因疾病基因组数据分类

IF 0.9

Optical Memory and Neural Networks Pub Date : 2026-06-26 DOI: 10.3103/S1060992X25602222

Akila Rajini Selvaraj, Mahalakshmi Poopathy, Saranya Priyadharshini Ravi Shanker, Ram Ganesh G.H., Nivetha Ganesan, Raj Kannan Chandrasekaran, Nandhini Krishna Kumar

{"title":"Reliable Genomic Data Classification for Monogenetic Disorders Using AEGA-BoostFNN-MonoDx Hybrid Deep Learning Framework","authors":"Akila Rajini Selvaraj, Mahalakshmi Poopathy, Saranya Priyadharshini Ravi Shanker,  Ram Ganesh G.H., Nivetha Ganesan, Raj Kannan Chandrasekaran, Nandhini Krishna Kumar","doi":"10.3103/S1060992X25602222","DOIUrl":"10.3103/S1060992X25602222","url":null,"abstract":"Monogenetic disorders result from mutations in a single gene, making early diagnosis essential for preventive healthcare. Timely and accurate identification is crucial for effective clinical intervention, genetic counseling, and personalized treatment. However, the high dimensionality, redundancy, and complexity of genomic data present major challenges for traditional diagnostic models. In this study, a novel hybrid framework of Adaptive Elistic Genetic Algorithm Boosted Feedforward Neural Network for Monogenetic Disorder Diagnosis (AEGA-BoostFNN-MonoDx) Model is proposed for monogenetic disorder prediction. This approach integrates with an Adaptive Elistic Genetic Algorithm (AEGA) with a Boosted Feedforward Neural Network (BoostFNN) to enhance the predictive accuracy. The AEGA module is designed to perform intelligent feature selection using adaptive mutation and elitist strategies. This ensures the identification of highly informative and non-redundant features while minimizing the computational complexity. The optimized features are then used to train the BoostFNN classifier, which captures the complex nonlinear patterns in genomic sequence data. The model is evaluated by using datasets such as the Of Genomes And Genetics Datasets and Genetic Variant Classifications Datasets, encoded using biologically meaningful representations such as k-mer and one-hot encoding. The AEGA-BoostFNN-MonoDx Model outperforms the existing model, achieving 98.75% recall, 98.94% accuracy, 98.69% specificity, 98.77% f1-score, and 98.81% precision.Therefore, the proposed model contributes to advancing the results by providing a reliable and adaptable model for the early monogenetic disorders.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 2","pages":"318 - 337"},"PeriodicalIF":0.9,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323825","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}

引用次数: 0

Review on Crop Yield Prediction by Using Different Machine Learning and Deep Learning Model 基于不同机器学习和深度学习模型的作物产量预测研究综述

IF 0.9

Optical Memory and Neural Networks Pub Date : 2026-06-26 DOI: 10.3103/S1060992X25601071

Madhuri Ghuge, Megharani Patil

{"title":"Review on Crop Yield Prediction by Using Different Machine Learning and Deep Learning Model","authors":"Madhuri Ghuge,  Megharani Patil","doi":"10.3103/S1060992X25601071","DOIUrl":"10.3103/S1060992X25601071","url":null,"abstract":"Crop yield prediction algorithms let farmers decide when and what kinds of crops to cultivate based on environmental factors, which increase yields. Predicting crop yields is difficult because of a number of intricate elements, including genotype, water availability, pest infestations, landscape quality, and harvest scheduling. By addressing these challenges through robust methodologies and innovative approaches, machine learning and deep learning models have the potential to greatly improve the accuracy and dependability of crop production forecast, thereby supporting global efforts to ensure food security and sustainable agriculture. This study utilizes Deep Learning and Machine Learning algorithms to analyse crop cultivation stages, providing solutions based on meteorological conditions, fertilizer requirements, and Growing Degree Days. Production in agriculture efficiently uses machine learning algorithms like RF, KNN, MSER, BMA, and SVM for crop yield prediction. BMA’s machine learning model has a high prediction accuracy rate of 98% for crop yields. For analysing deep learning algorithms procedure, DNN, CNN-RNN, DBN, Adaboost, and SBE algorithms are used. With an accuracy of 99.24%, DNN is the most used deep learning algorithm. Finally for evaluating the hyperparameter optimized network model, SR-BOA, GBXBLG-BOA, MLP-SMO, BDTRFNN, MARS-PCA, and RFOERNN algorithms are employed. MARS-PCA overcomes than all other techniques with accuracy of 98.7%. Experimental results consistently show that the Deep Neural Networks (DNNs) perform exceptionally well for predicting crop yield across multiple agricultural scenarios.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 2","pages":"389 - 401"},"PeriodicalIF":0.9,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323653","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}

引用次数: 0

Erratum to: Reputation-Based Byzantine Fault Tolerance and ElGamal Cryptography with Deep Belief Network on Smart Contract for Secure Blockchain 基于信誉的拜占庭容错和基于深度信念网络的加密技术在安全bbb智能合约中的应用

IF 0.9

Optical Memory and Neural Networks Pub Date : 2026-06-26 DOI: 10.3103/S1060992X26700049

V. Devi, P. Amudha

引用次数: 0

Multimodal Sentiment and Emotion Detection Using Hybrid Fusion of Text, Audio, and Image with Explainable AI Integration 使用文本、音频和图像的混合融合和可解释的人工智能集成的多模态情感和情感检测

IF 0.9

Optical Memory and Neural Networks Pub Date : 2026-06-26 DOI: 10.3103/S1060992X25602817

Anjana Thampy S., Jeyaraj Jane Rubel Angelina

{"title":"Multimodal Sentiment and Emotion Detection Using Hybrid Fusion of Text, Audio, and Image with Explainable AI Integration","authors":"Anjana Thampy S.,  Jeyaraj Jane Rubel Angelina","doi":"10.3103/S1060992X25602817","DOIUrl":"10.3103/S1060992X25602817","url":null,"abstract":"Emotion recognition plays a crucial role in applications such as human-computer interaction, healthcare, adaptive learning, and social media analysis. However, existing multimodal frameworks often struggle to effectively model paralinguistic features and manage the random fusion of different modalities, resulting in misclassifications and limited interpretability. To address these challenges, this paper introduces TriModal-SentiXNet, a novel multimodal framework designed for dual emotion and sentiment recognition using text, speech, and facial expressions. The framework operates in three main stages: pre-processing, feature extraction, and explainable classification. The pre-processing phase employs a Multimodal Adaptive Pre-processing (MAP) method that enhances input quality by denoising speech, standardizing visual frames, and semantically enriching textual data. For feature extraction, the RoBi-FuseNet architecture learns comprehensive contextual representations from each modality while effectively modeling temporal dynamics and cross-modal interactions to improve recognition accuracy. Explainable classification is achieved using SHapley Additive exPlanations (SHAP), which provides transparency by quantifying each modality’s contribution and ensuring balanced influence. The integration of adaptive pre-processing and context-aware fusion strengthens robustness, interpretability, and overall system reliability. Experimental evaluations on benchmark datasets CMU-MOSEI and CMU-MOSI demonstrate notable performance gains, achieving accuracies of 87.92 and 86%, respectively. These results surpass unimodal and existing multimodal baselines, establishing TriModal-SentiXNet as a promising step toward ethical, transparent, and context-aware emotion recognition systems suitable for real-world deployment.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 2","pages":"294 - 317"},"PeriodicalIF":0.9,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323656","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}

引用次数: 0

Approach to Predict Farmland Gross Primary Productivity (GPP) for Sustainable Food Production Using Remote Sensing Data 基于遥感数据的可持续粮食生产农田总初级生产力预测方法

IF 0.9

Optical Memory and Neural Networks Pub Date : 2026-06-26 DOI: 10.3103/S1060992X25600314

K. Choudhary, M. S. Boori, A. Kupriyanov, R. Paringer

{"title":"Approach to Predict Farmland Gross Primary Productivity (GPP) for Sustainable Food Production Using Remote Sensing Data","authors":"K. Choudhary, M. S. Boori, A. Kupriyanov, R. Paringer","doi":"10.3103/S1060992X25600314","DOIUrl":"10.3103/S1060992X25600314","url":null,"abstract":"To address numerous farmland challenges, Russia has been implementing abundant programs that have been demonstrated to significantly reduce the ecological degradation of farming. Food security and sustainable agricultural production constitute significant concerns for society. This research analyzes various data to explore these aspects in Russia, such as: soil moisture, net primary productivity (NPP), gross primary productivity, actual evapotranspiration (ETa), precipitation (mm), land surface temperature (°C), normalized difference vegetation Index, leaf area index, and digital elevation model (DEM), and crop grain production in 2010–2020. The goal of this study is to develop a linear regression model for predicting farmland gross primary productivity (GPP) by integrating remote sensing data with various environmental and agricultural parameters. The annual farmland GPP increased from 2010 to 2020. The results illustrate that the differences in crop cereal production data were not consistent with the annual pattern. Therefore, the recommended linear model for farmland GPP prediction performed significantly better in the regional studies. Our findings demonstrate that linear regression-based farmland GPP products can be utilized in the adequate assessment of agricultural ecosystems at the field and regional scales. It is also beneficial for food safety and security for the growing population.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 2","pages":"338 - 351"},"PeriodicalIF":0.9,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323826","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}

引用次数: 0

Optimized ResNet50-based Model for Solar Panel Fault Detection Using a Modified Light Spectrum Optimizer 基于改进的光谱优化器优化的resnet50太阳能板故障检测模型

IF 0.9

Optical Memory and Neural Networks Pub Date : 2026-06-26 DOI: 10.3103/S1060992X25600399

Gehad Ismail Sayed

{"title":"Optimized ResNet50-based Model for Solar Panel Fault Detection Using a Modified Light Spectrum Optimizer","authors":"Gehad Ismail Sayed","doi":"10.3103/S1060992X25600399","DOIUrl":"10.3103/S1060992X25600399","url":null,"abstract":"The need for automatic solar panel defect detection is becoming more and more urgent due to the rising demand for new solar energy systems worldwide. Convolutional neural networks are very effective at solving the image classification problem across a wide range of domains. A model for detecting faults in solar panel surfaces was presented in this paper. The three primary stages of the suggested model are the data preparation stage, the ResNet50 hyperparameter optimization stage, and the image classification stage. Furthermore, a modified version of the light spectrum optimizer (MLSO) was introduced in this paper. In the optimization process of the original light spectrum optimizer, the proposed MLSO makes use of the outward search method. The ideal batch size and learning rate values are determined using MLSO. A publicly available dataset with six classes is employed. The proposed fault detection model can greatly improve the accuracy of solar panel defect detection, according to the experimental results. The model achieved an accuracy of 96.58%, a sensitivity of 98.36%, a specificity of 98.02%, and an F1-score of 96.72%. Furthermore, the results showed that, when compared to other well-known swarm optimization algorithms, the proposed MLSO produced the most effective results.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 2","pages":"372 - 388"},"PeriodicalIF":0.9,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323655","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}

引用次数: 0

Radiography and Subject Data Integration for Osteoporosis Detection Using Dingo Optimization Based Deep Belief Network Model 基于Dingo优化的深度信念网络模型用于骨质疏松症检测的影像学与受试者数据集成

IF 0.9

Optical Memory and Neural Networks Pub Date : 2026-06-26 DOI: 10.3103/S1060992X25602234

Dhanyavathi A, Veena M B

{"title":"Radiography and Subject Data Integration for Osteoporosis Detection Using Dingo Optimization Based Deep Belief Network Model","authors":"Dhanyavathi A,  Veena M B","doi":"10.3103/S1060992X25602234","DOIUrl":"10.3103/S1060992X25602234","url":null,"abstract":"Osteoporosis (OP) is a prevalent metabolic bone disease characterized by decreased bone density and strength, leading to an increased risk of fractures, especially among the elderly. This condition often remains undiagnosed until a fracture occurs, making early detection crucial to prevent complications, reduce morbidity, and improve the patient’s quality of life. Currently the bone density was to identify the osteoporosis, and Dual Energy X-ray Absorptiometry (DEXA) for figuring out the size of the bone, time consuming, and the manual interpretation having the error. A novel fusion method for radiography images contrast augmentation was developed in order to avoid this issue. Initially, both X-Ray and subject records are considered as the dataset. The images are enhanced for clear bone identification through pre-processing techniques including resizing, anisotropic diffusion, contrast stretching, and high boost filtering. After pre-processing the images are segmented using the fuzzy C-means clustering segmentation. Features extraction is performed on the segmented images using the mean, skewness, kurtosis, gabour, grey level co-occurrence matrix, and discrete wavelet transform. The extracted images are merged with subject records using the canonical correlation analysis. The selected features are fed into the IDBNN to detect the condition of the osteoporosis prediction while the Dingo optimization algorithm by selecting the optimal weight. The proposed model has 94% accuracy and a 10% false positive rate. The suggested approach could detect osteoporosis with less computational time, making it ideal for real-time applications.","PeriodicalId":721,"journal":{"name":"Optical Memory and Neural Networks","volume":"35 2","pages":"276 - 293"},"PeriodicalIF":0.9,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148323823","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}

引用次数: 0