Conditional Random Fields Research Articles

Research on 3D humanoid robot pose estimation based on HRNet-Epipolar and CRF robot model by multiple view

Purpose This paper aims to present an unmarked method including entire two-dimensional (2D) and three-dimensional (3D) methods to recover absolute 3D humanoid robot poses from multiview images. Design/methodology/approach The method consists of two separate steps: estimating the 2D poses in multiview images and recovering the 3D poses from the multiview 2D heatmaps. The 2D one is conducted by High-Resolution Net with Epipolar (HRNet-Epipolar), and the Conditional Random Fields Humanoid Robot Pictorial Structure Model (CRF Robot Model) is proposed to recover 3D poses. Findings The performance of the algorithm is validated by experiments developed on data sets captured by four RGB cameras in Qualisys system. It illustrates that the algorithm has higher Mean Per Joint Position Error than Direct Linear Transformation and Recursive Pictorial Structure Model algorithms when estimating 14 joints of the humanoid robot. Originality/value A new unmarked method is proposed for 3D humanoid robot pose estimation. Experimental results show enhanced absolute accuracy, which holds important theoretical significance and application value for humanoid robot pose estimation and motion performance testing.

A Chinese named entity recognition method for landslide geological disasters based on deep learning

Landslide Named Entity Recognition (LNER) involves extracting specific entities from Chinese unstructured landslide disaster texts, which is crucial for constructing a knowledge graph and supporting landslide prevention efforts. This study proposes a deep learning-based LNER model that utilizes Bidirectional Encoder Representations from Transformer (BERT) for word embeddings and integrates the Conditional Random Fields (CRF) algorithm and projected gradient descent (PGD) adversarial neural networks to enhance sequence labeling accuracy. The practical implications of this research lie in improving the efficiency and precision of disaster information extraction, aiding in real-time decision-making and risk mitigation strategies. Experiments on the constructed dataset show that the model effectively identifies eight types of landslide entities, achieving a highest F1 score of 89.7%.

Simulation of conditional non-Gaussian random fields with directional asymmetry

Simulation of conditional non-Gaussian random fields with directional asymmetry

Dynamic Spatial-Temporal Embedding via Neural Conditional Random Field for Multivariate Time Series Forecasting

How to capture dynamic spatial-temporal dependencies remains an open question in multivariate time series (MTS) forecasting. Although recent advanced spatial-temporal graph neural networks (STGNNs) achieve superior forecasting performance, they either consider pre-defined spatial correlations or simply learn static graphs. Some research has tried to learn many adjacent matrices to reveal time-varying spatial correlations, but they generate discrete graphs which cannot encode evolutionary information and also face computational complexity problem. In this article, we propose two significant plugins to help automatically learn enhanced dynamic spatial-temporal embedding of MTS data: (1) a novel neural conditional random field (CRF) layer. We find that the implicit time-varying spatial dependencies are reflected by the explicit changeable links between edges, and we propose the neural CRF to encode such pairwise changeable evolutionary inter-dependencies; (2) a structure adaptive graph convolution (SAGC) that does not require pre-defined graphs to capture semantically richer spatial correlations. Then, we integrate the neural CRF, SAGC with recurrent neural network to develop a new STGNN paradigm termed Adaptive Spatial-Temporal graph neural network with Conditional Random Field (ASTCRF), which can be trained in an end-to-end fashion. We validate the effectiveness, efficiency, and scalability of ASTCRF on five public benchmark MTS datasets.

ILMCNet: A Deep Neural Network Model That Uses PLM to Process Features and Employs CRF to Predict Protein Secondary Structure.

Protein secondary structure prediction (PSSP) is a critical task in computational biology, pivotal for understanding protein function and advancing medical diagnostics. Recently, approaches that integrate multiple amino acid sequence features have gained significant attention in PSSP research. We aim to automatically extract additional features represented by evolutionary information from a large number of sequences while simultaneously incorporating positional information for more comprehensive sequence features. Additionally, we consider the interdependence between secondary structures during the prediction stage. To this end, we propose a deep neural network model, ILMCNet, which utilizes a language model and Conditional Random Field (CRF). Protein language models (PLMs) pre-trained on sequences from multiple large databases can provide sequence features that incorporate evolutionary information. ILMCNet uses positional encoding to ensure that the input features include positional information. To better utilize these features, we propose a hybrid network architecture that employs a Transformer Encoder to enhance features and integrates a feature extraction module combining a Convolutional Neural Network (CNN) with a Bidirectional Long Short-Term Memory Network (BiLSTM). This design enables deep extraction of localized features while capturing global bidirectional information. In the prediction stage, ILMCNet employs CRF to capture the interdependencies between secondary structures. Experimental results on benchmark datasets such as CB513, TS115, NEW364, CASP11, and CASP12 demonstrate that the prediction performance of our method surpasses that of comparable approaches. This study proposes a new approach to PSSP research and is expected to play an important role in other protein-related research fields, such as protein tertiary structure prediction.

An One-step Triple Enhanced weakly supervised semantic segmentation using image-level labels.

Weakly supervised semantic segmentation, based on image-level labels, abandons the pixel-level labels relied upon by traditional semantic segmentation algorithms. It only utilizes images as supervision information, thereby reducing the time cost and human resources required for marking pixel data. The prevailing approach in weakly supervised segmentation involves two-step method, introducing an additional network and numerous parameters, thereby complicating the model structure. Furthermore, image-level labels typically furnishes only category information for the entire image, lacking specific location details and accurate target boundaries during model training. We propose an innovative One-Step Triple Enhanced weakly supervised semantic segmentation network(OSTE). OSTE streamlines the model structure, which can accomplish both pseudo-labels generation and semantic segmentation tasks in just one step. Furthermore, we augment the weakly supervised semantic segmentation network in three key aspects based on the class activation map construction method, thereby enhancing segmentation accuracy: Firstly, by integrating local information from the activation map with the image, we can enhance the network's localization and expansion capabilities to obtain more accurate and rich location information. Then, we refine the seed areas of the class activation map by exploiting the correlation between multi-level feature. Finally, we incorporate conditional random field theory to generate pseudo-labels with higher confidence and richer boundary information. In comparison to the prevailing two-step weakly supervised semantic segmentation schemes, the segmentation network proposed in this paper achieves a more competitive mean Intersection over Union (mIoU) score of 58.47% on Pascal VOC. Additionally, it enhances the mIoU score by at least 5.03% when compared to existing end-to-end schemes.

AI-Powered Approaches for Hypersurface Reconstruction in Multidimensional Spaces

The present article explores the possibilities of using artificial neural networks to solve problems related to reconstructing complex geometric surfaces in Euclidean and pseudo-Euclidean spaces, examining various approaches and techniques for training the networks. The main focus is on the possibility of training a set of neural networks with information about the available surface points, which can then be used to predict and complete missing parts. A method is proposed for using separate neural networks that reconstruct surfaces in different spatial directions, employing various types of architectures, such as multilayer perceptrons, recursive networks, and feedforward networks. Experimental results show that artificial neural networks can successfully approximate both smooth surfaces and those containing singular points. The article presents the results with the smallest error, showcasing networks of different types, along with a technique for reconstructing geographic relief. A comparison is made between the results achieved by neural networks and those obtained using traditional surface approximation methods such as Bézier curves, k-nearest neighbors, principal component analysis, Markov random fields, conditional random fields, and convolutional neural networks.

Design of agricultural question answering information extraction method based on improved BILSTM algorithm

With the rapid growth of the agricultural information and the need for data analysis, how to accurately extract useful information from massive data has become an urgent first step in agricultural data mining and application. In this study, an agricultural question-answering information extraction method based on the BE-BILSTM (Improved Bidirectional Long Short-Term Memory) algorithm is designed. Firstly, it uses Python’s Scrapy crawler framework to obtain the information of soil types, crop diseases and pests, and agricultural trade information, and remove abnormal values. Secondly, the information extraction converts the semi-structured data by using entity extraction methods. Thirdly, the BERT (Bidirectional Encoder Representations from Transformers) algorithm is introduced to improve the performance of the BILSTM algorithm. After comparing with the BERT-CRF (Conditional Random Field) and BILSTM algorithm, the result shows that the BE-BILSTM algorithm has better information extraction performance than the other two algorithms. This study improves the accuracy of the agricultural information recommendation system from the perspective of information extraction. Compared with other work that is done from the perspective of recommendation algorithm optimization, it is more innovative; it helps to understand the semantics and contextual relationships in agricultural question and answer, which improves the accuracy of agricultural information recommendation systems. By gaining a deeper understanding of farmers’ needs and interests, the system can better recommend relevant and practical information.

Attenuative Unified U-Net with Conditional PatchMatch Algorithm for Edge Preserving Salient Object Detection

The rise of Deep Neural Networks (DNNs) has significantly boosted salient object detection in computer vision tasks. However, downsampling methods like striding and pooling often introduce bluish artifacts near edges, hindering detection accuracy. To address this, the "Attenuative Unified U-Net with Conditional PatchMatch Algorithm" is proposed. The method employs Gaussian smoothing for noise reduction and depth refinement in preprocessing. In existing methods, the Edge-preserving salient object detection struggles with cluttered backgrounds and connected objects, like a bird on a rock, making it difficult to accurately distinguish edges and salient regions due to shared boundaries. Hence, an Attenuative Unified Backpropagated Entropy UNet is proposed, which integrates an Attention Mechanism that enhance feature map spatial weight assignment, emphasizing features or regions that are judged most relevant for edge and salient region detection. Then, the Cascaded Laplace pyramid is incorporated into the Unified U-Net’s design for multi-scale information processing, capturing contextual details effectively. The Backpropagated Entropy Loss Function is then created to ensure accurate and confident fused image output. The depth map, edge map, and salient area map are combined in the U-Net’s final output layer to create an overall fused image. Next, to addresses depth discontinuity problem a noteworthy novel idea the Conditional PatchMatch Random Field Algorithm (CPMRF), which combines PatchMatch efficiency and CRFs’ contextual modeling. PatchMatch iteratively propagates matches from neighboring patches to find similar patches in the remaining portion of the image and Conditional Random Field (CRF) is the next step, which improves the PatchMatch results by taking into account the connections between the matched patches and their neighboring areas. Hence, the experimental outcomes of the proposed model effectively show the improved detection of edge in salient object detection with better accuracy, precision, recall, MaxF, and F1 score with minimized MAE.

Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study.

Named entity recognition (NER) models are essential for extracting structured information from unstructured medical texts by identifying entities such as diseases, treatments, and conditions, enhancing clinical decision-making and research. Innovations in machine learning, particularly those involving Bidirectional Encoder Representations From Transformers (BERT)-based deep learning and large language models, have significantly advanced NER capabilities. However, their performance varies across medical datasets due to the complexity and diversity of medical terminology. Previous studies have often focused on overall performance, neglecting specific challenges in medical contexts and the impact of macrofactors like lexical composition on prediction accuracy. These gaps hinder the development of optimized NER models for medical applications. This study aims to meticulously evaluate the performance of various NER models in the context of medical text analysis, focusing on how complex medical terminology affects entity recognition accuracy. Additionally, we explored the influence of macrofactors on model performance, seeking to provide insights for refining NER models and enhancing their reliability for medical applications. This study comprehensively evaluated 7 NER models-hidden Markov models, conditional random fields, BERT for Biomedical Text Mining, Big Transformer Models for Efficient Long-Sequence Attention, Decoding-enhanced BERT with Disentangled Attention, Robustly Optimized BERT Pretraining Approach, and Gemma-across 3 medical datasets: Revised Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA), BioCreative V CDR, and Anatomical Entity Mention (AnatEM). The evaluation focused on prediction accuracy, resource use (eg, central processing unit and graphics processing unit use), and the impact of fine-tuning hyperparameters. The macrofactors affecting model performance were also screened using the multilevel factor elimination algorithm. The fine-tuned BERT for Biomedical Text Mining, with balanced resource use, generally achieved the highest prediction accuracy across the Revised JNLPBA and AnatEM datasets, with microaverage (AVG_MICRO) scores of 0.932 and 0.8494, respectively, highlighting its superior proficiency in identifying medical entities. Gemma, fine-tuned using the low-rank adaptation technique, achieved the highest accuracy on the BioCreative V CDR dataset with an AVG_MICRO score of 0.9962 but exhibited variability across the other datasets (AVG_MICRO scores of 0.9088 on the Revised JNLPBA and 0.8029 on AnatEM), indicating a need for further optimization. In addition, our analysis revealed that 2 macrofactors, entity phrase length and the number of entity words in each entity phrase, significantly influenced model performance. This study highlights the essential role of NER models in medical informatics, emphasizing the imperative for model optimization via precise data targeting and fine-tuning. The insights from this study will notably improve clinical decision-making and facilitate the creation of more sophisticated and effective medical NER models.

Advances in medical image analysis: A comprehensive survey of lung infection detection

AbstractThis research investigates advanced approaches in medical image analysis, specifically focusing on segmentation and classification techniques, as well as their integration into multi‐task architectures for lung infections. This research begins by explaining key architectural models used in segmentation and classification tasks. The study extends to the enhancement of these architectures through attention modules and conditional random fields. Relevant datasets and evaluation metrics, incorporating discussions on loss functions are also reviewed. This review encompasses recent advancements in single‐task and multi‐task models, highlighting innovations in semi‐supervised, self‐supervised, few‐shot, and zero‐shot learning techniques. Empirical analysis is conducted on both single‐task and multi‐task architectures, predominantly utilizing the U‐Net framework, and is applied across multiple datasets for segmentation and classification tasks. Results demonstrate the effectiveness of these models and provide insights into the strengths and limitations of different approaches. This research contributes to improved detection and diagnosis of lung infections by offering a comprehensive overview of current methodologies and their practical applications.

Deep Learning-Based Segmentation and Prediction of Liver Cancer Using CT Images

Background: Liver cancer presents a significant global health challenge, particularly in Asia, due to high incidence rates and the difficulty of early detection. Despite advances in medical imaging, the accurate segmentation and prediction of liver tumors remain critical for effective treatment planning. Deep-learning is a revolutionary tool in medical imaging, offering promising solutions for automated analysis of liver cancer using CT images. Methods: This study develops a framework based on deep-learning- for the segmentation, and prediction cancer of the liver from Computed Tomography images. The framework leverages advanced convolutional neural network (CNN) architectures, including U-Net and its variants, optimized with class balancing methods. The model is trained and authenticated using a high-quality annotated images, dataset of liver CT images. Key techniques such as transfer learning, data augmentation, and hyper parameter tuning are employed to enhance model performance. Evaluation metrics include accuracy, sensitivity, specificity, Dice-coefficient, and (IoU).Intersection over Union Results: The design of proposed program achieved significant improvements in segmentation, accuracy, with an average Dice, Similarity-Coefficient (DSC) of 0.96 for liver segmentation and 0.84 for the tumor segmentation. The results demonstrate the superior performance of the model's compared to the other traditional methods and other existing deep learning models. Cascaded fully convolutional networks (FCNs) combined with 3D Conditional Random Fields (CRFs) showed robust performance, achieving true value accuracy rates of approximately 99.55% for liver segmentation. Conclusion: This study presents a novel and effective deep learning framework for liver cancer segmentation and prediction, highlighting the clinical applicability and relevance of the developed models in real-world medical settings. The findings highlight the potential in deep-learning for enhancing liver cancer diagnosis, facilitating early detection, timely interventions, and improved patient outcomes. This research contributes in increasing the body of knowledge in medical image analysis, enlighten the way for future advancements in the field. Keywords: Computed Tomography, Deep Learning, Machine Learning, U-Net, 3D, Segmentation, Fully Convolutional Networks, Conditional Random Fields, Dice Similarity Coefficient, Intersection over Union, Conventional Neural Network

WSSS-CRAM: precise segmentation of histopathological images via class region activation mapping.

Fast, accurate, and automatic analysis of histopathological images using digital image processing and deep learning technology is a necessary task. Conventional histopathological image analysis algorithms require the manual design of features, while deep learning methods can achieve fast prediction and accurate analysis, but rely on the drive of a large amount of labeled data. In this work, we introduce WSSS-CRAM a weakly-supervised semantic segmentation that can obtain detailed pixel-level labels from image-level annotated data. Specifically, we use a discriminative activation strategy to generate category-specific image activation maps via class labels. The category-specific activation maps are then post-processed using conditional random fields to obtain reliable regions that are directly used as ground-truth labels for the segmentation branch. Critically, the two steps of the pseudo-label acquisition and training segmentation model are integrated into an end-to-end model for joint training in this method. Through quantitative evaluation and visualization results, we demonstrate that the framework can predict pixel-level labels from image-level labels, and also perform well when testing images without image-level annotations. Future, we consider extending the algorithm to different pathological datasets and types of tissue images to validate its generalization capability.

Relationship detection for manipulation in object stacking scene with fully connected CRF

Relationship detection for manipulation in object stacking scenes is an actively studied problem in recent years. Since the stacked objects may occlude and block each other, it is necessary to model relationships among them for safety. Almost all existing solutions to this problem model object relationships separately and ignore their dependencies on each other. In this paper, we propose a fully connected Conditional Random Field (CRF) to impose global constraints on the object stacking scenes. Since the inference of fully connected CRF is usually intractable, we propose efficient methods for the exact inference and the variational inference to solve the proposed CRFs. Furthermore, the variational inference is implemented as an RNN for end-to-end training. Experimental results on Visual Manipulation Relationship Dataset VMRD and REGRAD suggest that the proposed CRF can, with a clear margin, improve the accuracy of manipulation relationship detection in object stacking scenes. Finally, the proposed models are also deployed on a physical robot to build a relation-aware grasping system. Results demonstrate that our algorithms can be generalized to real-world scenes well and help the robot perform safe grasping in object stacking scenes.

A Novel Optimization Scheme for Named Entity Recognition with Pre-trained Language Models

Named Entity Recognition (NER) is crucial for extracting structured information from text. While traditional methods rely on rules, Conditional Random Fields (CRFs), or deep learning, the advent of large-scale Pre-trained Language Models (PLMs) offers new possibilities. PLMs excel at contextual learning, potentially simplifying many natural language processing tasks. However, their application to NER remains underexplored. This paper investigates leveraging the GPT-3 PLM for NER without fine-tuning. We propose a novel scheme that utilizes carefully crafted templates and context examples selected based on semantic similarity. Our experimental results demonstrate the feasibility of this approach, suggesting a promising direction for harnessing PLMs in NER.

IPerFEX-2023: Indonesian personal financial entity extraction using indoBERT-BiGRU-CRF model

There is minimal research focusing on applications of Indonesian named entity recognition (NER) in a specific domain. This study proposes an Indonesian personal financial entity extraction task that can be utilized in a financial assistant chatbot system to interpret user’s financial situation for personalization. Due to the simplicity that a chatbot has, it can promote financial management practices to youth as early as possible in their career. However, the challenge in financial NER is numerical entity extraction that relies heavily on contextual information and suffers the out-of-vocabulary (OOV) problem. Therefore, to extract 15 personal financial entities in daily Indonesian discussions (expense-type, expense-amount, income-type, income-amount, asset-type, asset-amount, saving-type, saving-amount, liability-type, liability-amount, family, time, financial-goal, age, and occupation), this research proposes a dataset, IPerFEX-2023, trained using the Bidirectional Gated Recurrent Unit BiGRU and Conditional Random Field (CRF) with Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT) pre-trained model for feature embeddings (IndoBERT-BiGRU-CRF). It is compared with the corresponding Bidirectional Long Short-Term Memory (BiLSTM) (IndoBERT-BiLSTM-CRF) as baseline. Not only the IndoBERT-BiGRU-CRF model achieves the best performance with a 0.73 F1-score, but it is also 14% faster on average compared to the corresponding baseline model due to its simpler unit structure. This paper also discusses future directions covering model enhancement strategy based on the error analysis result and complementary tasks needed to complete personalization

An Investigation of AI-Enabled Comprehensive Survey of Phishing Attacks detection techniques

Phishing is an online criminal activity which traps regular online customers to reveal their sensitive information into fake destinations by disguising themselves as original website. Because of the short life expectancy of phishing sites and the fast progression of phishing systems the current anti- phishing solutions are either unfit to manage the rising changes or in fit for assuming a successful part against this attack.Natural language handling and machine learning – these are the centre usage of the multi-stage approach proposed to identify phishing attacks and to perceive the substance/association that has been misused by the attackers to execute the phishing attacks. In this technique the primary stage is the revelation of named elements (names of areas, individuals and associations) and afterward the disclosure of concealed topics and for this the strategies that backings both phishing and non-phishing information therefore, Conditional Random Field (CRF) and Latent Dirichlet Allocation (LDA) is utilized. Next stage is the AdaBoost arrange where the named elements and the concealed subjects are dealt as features and the messages are ordered into phishing or non-phishing. proposed techniques are planned to alleviate the effect of phishing attack. The principal strategy proposes is to identify Wi-Fi phishing utilizing Association Rule mining. A Wi-Fi hotspot which associates such hand held gadgets has turned into a transitory archive of delicate data, in this way giving an open door for programmers to gather money related gain .Consequently data security arrangements tending to Wi-Fi hotspots (Wi-Fi phishing attack) has turned into the need of great importance. The conceivable approaches to attack the security of Wi-Fi hotspots and counter-attack methodologies have been tended to in this strategy. Key works: Phishing, LDA,AI

An analysis of FRE @ BC8 SympTEMIST track: named entity recognition.

This paper is a more in-depth analysis of the approaches used in our submission (Martínez A, García-Santa N. (2023) FRE @ BC8 SympTEMIST track: Named Entity Recognition Zenodo.) to the 'SympTEMIST' Named Entity Recognition (NER) shared subtask at 'BioCreative 2023'. We participated on the challenge submitting two systems based on a RoBERTa architecture LLM trained on Spanish-language clinical data available at 'HuggingFace' model repository. Before choosing the systems that would be submitted, we tried different combinations of the techniques described here: Conditional Random Fields and Byte-Pair Encoding dropout. In the second system we also included Sub-Subword feature based embeddings (SSW). The test set used in the challenge has now been released (López SL, Sánchez LG, Farré E etal. (2024) SympTEMIST Corpus: Gold Standard annotations for clinical symptoms, signs and findings information extraction. Zenodo), allowing us to analyze more in depth our methods, as well as measuring the impact of introducing data from CARMEN-I (Lima-López S, Farré-Maduell E, Krallinger M. (2023) CARMEN-I: Clinical Entities Annotation Guidelines in Spanish. Zenodo) corpus. Our experiments show the moderate effect of using the Sub-Subword feature based embeddings and the impact of including the symptom NER data from the CARMEN-I dataset. Database URL: https://physionet.org/content/carmen-i/1.0/.

LungXpertAI: A deep multi-task learning model for chest CT scan analysis and COVID-19 detection

Addressing the urgent need for accurate COVID-19 identification and lung infection segmentation in CT scans, our study introduces LungXpertAI, a novel Multi-Task Learning architecture. Previous studies on multi-task structures have been limited in providing detailed insights into their architectures. Addressing these details is crucial for enhancing the performance of multi-task structures. Our proposed multi-task structure, based on the U-Net architecture, incorporates a shared encoder, a specialized segmentation decoder, and a multi-layer perceptron for classification. In the proposed approach, image processing algorithms, including histogram equalization, median filtering, and morphological operations have been employed in the pre-processing stage of input images. Also, by combining these algorithms in pairs, an attempt has been made to enhance the performance of tasks. Initial preprocessing and the integration of the Convolutional Block Attention Module (CBAM) contribute to effective feature extraction. Post-processing with Conditional Random Field (CRF) further refines segmentation outputs. CRF considers spatial dependencies within the segmentation results, contributing to more precise delineation of COVID-19-affected areas in CT scans. Evaluation on using dataset demonstrates the effectiveness of pairwise combinations of image processing algorithms, achieving superior results with a segmentation accuracy of 95.66 % and a classification accuracy of 95.62 %. The incorporation of the CBAM module improves classification accuracy to 96.22 %. Moreover, Integrating CBAM and CRF significantly improves segmentation accuracy to 96.51 %. The application of our proposed method steps to U-Net++ and ResUnet showcases their potential for enhancing multi-task structures. This study establishes new COVID-19 detection standards, promising progress in medical image analysis.

Short-term PV energy yield predictions within city neighborhoods for optimum grid management

The global shift towards the exploitation of renewable energy sources is a key aspect in the transition to carbon-free societies. Although harnessing of PV energy disclosed the numerous environmental and economic benefits of PV systems, the ever-increasing share of PV systems in power generation has revealed their adverse effect on the electricity grid due to their dependence on weather conditions. Uncertain environmental parameters, especially cloud cover, can adversely affect the stability of the energy generated by a PV system, causing dynamic changes in PV power generation in a given future time period. Therefore, accurate predictions of PV power output are a major challenge, as they can contribute to the optimal management and flexibility of the power grid, and to the improvement of the operating efficiency of PV power stations, thus enabling the development of flexible green power electricity grids across cities. In addition, precise forecasting can provide system/grid operators with information needed for efficient capacity management and scheduling and ensure grid stability.Most forecasting models provide promising results in terms of error performance; however, they need weather variables or satellite information as inputs that make prognosis significantly challenging. Hence, the current work utilizes a data-driven method for predicting PV power generation from distributed PV systems. The spatio-temporal model Sparse Gaussian Conditional Random Fields was considered. Although this model was used extensively for wind forecasting, in this work, it was used for solar PV power output predictions. Moreover, the effect of varying spatial distribution for aggregated multiple PV systems was investigated on the forecast model performance. The main objective was to provide the solar PV energy sector with important information for building a smart grid for cities or regions.Continuous data from a grid-connected dense network of 21 PV systems was used within a region of 38.485 km2 in Nicosia, Cyprus. It should be noted that the test set includes data only from winter months. The results showed that the predicted power output signal responds to the fluctuation and follows the trend of the actual power signal, even on days with large variations in actual PV power. The average nRMSE of all PV systems in each dataset studied ranges from 0.119 to 0.146, proving that the proposed method delivered significant results that can be compared with existing results from similar studies. Overall, the proposed method can provide important information that can assist in decision-making for the management, optimization, and visualization of grids across cities.