Utilization Of Information Research Articles

A grid self-adaptive exploration-based algorithm for multimodal multiobjective optimization

In multimodal multiobjective optimization, the key is to find as many equivalent Pareto optimal solutions as possible through broad exploration in the decision space. The grid search strategy can achieve quick convergence by guiding the evolution with historical information in each grid while enabling broad exploration. However, inaccurate utilization of information in grids may lead to losing numerous potential solutions, especially on imbalanced problems. In order to resolve this issue, a grid self-adaptive exploration-based algorithm (GSEA) is proposed in this paper. In GSEA, the historical information in the grid is accurately utilized through grid-based self-adaptive exploration and niche clearing methods, which retain a large number of solutions with potential and effectively handle multimodal multiobjective optimization problems (MMOPs). Experimental results show that the proposed algorithm outperforms seven other state-of-the-art multimodal multiobjective evolutionary algorithms (MMEAs) on two types of MMOPs, and the approach can effectively deal with the MMOPs with middle-scale decision variables as memory allows.

Digital Literacy of LAZIS Syuhada Yogyakarta in Building Public Awareness through Philanthropy Product Branding

This research describes the literacy of LAZIS Syuhada Yogyakarta in branding activities for the economic empowerment of the poor through the utilization of Information and Communication Technology online. One of the literacy efforts made by LAZIS Syuhada is branding the economic empowerment products of the poor to promote Islamic philanthropy and attract donors to donate. Branding the product is expected to build public awareness and knowledge (literacy) about LAZIS Syuhada's philanthropic activities, especially through social media. Social media is used by LAZIS Syuhada to increase public literacy of the branded products through information and promotions that are disseminated, so as to attract donations. This study contributes to understanding LAZIS Syuhada's literacy strategy in branding economic empowerment products as a form of philanthropic promotion so that people know and get involved in it. This study uses library research and descriptive analytics. The results of the study are expected to contribute to understanding LAZIS Syuhada's literacy strategy in its philanthropic product branding activities.

Maternal Health Information Access and Utilization among Pregnant Women in Rural Communities of Jalingo, Taraba State, Nigeria

Maternal Health Information Access and Utilization among Pregnant Women in Rural Communities of Jalingo, Taraba State, Nigeria

Hepatitis B vaccination coverage and associated factors among nurses working at health centers in Addis Ababa, Ethiopia: a cross-sectional study

BackgroundHepatitis B virus (HBV) poses one of the most serious workplace health risks facing healthcare workers, especially nurses, due to occupational exposure. The HBV vaccination coverage among healthcare workers in Ethiopia ranged from 5.4 to 21.9%. However, little is known about HBV vaccination coverage and associated factors among nurses in Ethiopia. Therefore, the aim of this study was to assess HBV vaccination coverage and associated factors among nurses working in health centers in Addis Ababa, Ethiopia, in 2023.MethodsAn institutional-based cross-sectional study was conducted from March 2 to March 31, 2023, among 428 nurses working in 32 health centers in Addis Ababa, Ethiopia. Data were entered into Epi-Info version 7.2.5.0 and then exported to SPSS version 27 for analysis. Descriptive statistics (frequencies and proportions) were used to summarize the data on the study variables. Bivariate and multivariate logistic regression analyses were conducted to determine the strength of the association, and the statistical significance of associations between the variables was determined using adjusted odds ratio with a 95% CI and p values < 0.05.ResultsHBV-full vaccination coverage among 428 nurses in health centers in Addis Ababa, Ethiopia, was 36.9%. Male nurses [AOR = 1.78, 95% CI: 1.08, 2.95], taking training on infection prevention [AOR = 1.73, 95% CI: 1.08, 2.78], having good knowledge about HBV vaccine [AOR = 1.98, 95% CI: 1.28, 3.04], and testing for HBV [AOR = 1.98, 95% CI: 1.08, 3.64] were more likely to have HBV full vaccination coverage, whereas monthly salary ≤ 7,071 Ethiopian Birr was 54% less likely to have HBV full vaccination coverage [AOR = 0.46, 95% CI: 0.25, 0.85] at p-value < 0.05.ConclusionThis study revealed that the full HBV vaccination coverage of nurses working in health centers in Addis Ababa, Ethiopia, was low (36.9%), and consequently, the majority of nurses are at a high risk of being infected with HBV. Therefore, the Ministry of Health and health centers should promote awareness, implement prevention programs, provide diagnostic, treatment, and care services, enhance information generation and utilization, and strengthen the health system to increase vaccination coverage among nurses.

Climate Change Adaptation Information Usability Among Maize Farmers in Ido Local Government Area of Oyo State, Nigeria

The availability of climate change information has not always led to more successful adaptation; even when climate change adaptation information is available, there are still obstacles to its efficient use. Furthermore, the tactics required to communicate climate information to end-users for successful adaptation have received less attention. This study focuses on utilization of available climate change adaptation information among maize farmers in the study area. Simple random sampling technique was used to select 203 respondents for data collection. The data were collected using a questionnaire and analysed through the use of the standard statistical package for social sciences. The communication strategies used include inter-personal, audio, visual and social media. The results revealed that the strategies used in communicating climate change adaptation information to maize farmers in the study area yielded great influence in terms of benefits to the farmers such as the increase in income, prevention of yield loss, reduced soil degradation, maintaining good human health status, and improved weed management. The information that is popularly utilised is mixed cropping (61.6%), water management (58.1%), and early warning (58.1%). A significant relationship existed between communication channels (r=0.8, p&amp;lt;.05) and respondents’ climate change adaptation information utilization. Poor understanding of the message and lack of funds to put the information into practice hinders some respondents from utilising adaptation information. Hence, extension agents need to locate these category farmers and link them with service providers that could provide incentives or credit facilities for sustainable utilisation of available information to combat climate change.

CTRNet: An Automatic Modulation Recognition Based on Transformer-CNN Neural Network

Deep learning (DL) has brought new perspectives and methods to automatic modulation recognition (AMR), enabling AMR systems to operate more efficiently and reliably in modern wireless communication environments through its powerful feature learning and complex pattern recognition capabilities. However, convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are used for sequence recognition tasks, face two main challenges, respectively: the ineffective utilization of global information and slow processing speeds due to sequential operations. To address these issues, this paper introduces CTRNet, a novel automatic modulation recognition network that combines a CNN with Transformer. This combination leverages Transformer’s ability to adequately capture the long-distance dependencies between global sequences and its advantages in sequence modeling, along with the CNN’s capability to extract features from local feature regions of signals. During the data preprocessing stage, the original IQ-modulated signals undergo sliding-window processing. By selecting the appropriate window sizes and strides, multiple subsequences are formed, enabling the network to effectively handle complex modulation patterns. In the embedding module, token vectors are designed to integrate information from multiple samples within each window, enhancing the model’s understanding and modeling ability of global information. In the feedforward neural network, a more effective Bilinear layer is employed for processing to capture the higher-order relationship between input features, thereby enhancing the ability of the model to capture complex patterns. Experiments conducted on the RML2016.10A public dataset demonstrate that compared with the existing algorithms, the proposed algorithm not only exhibits significant advantages in terms of parameter efficiency but also achieves higher recognition accuracy under various signal-to-noise ratio (SNR) conditions. In particular, it performs relatively well in terms of accuracy, precision, recall, and F1-score, with clearer classification of higher-order modulations and notable overall accuracy improvement.

A Multi-Baseline Forest Height Estimation Method Combining Analytic and Geometric Expression of the RVoG Model

As an important parameter of forest biomass, forest height is of great significance for the calculation of forest carbon stock and the study of the carbon cycle in large-scale regions. The main idea of the current forest height inversion methods using multi-baseline P-band polarimetric interferometric synthetic aperture radar (PolInSAR) data is to select the best baseline for forest height inversion. However, the approach of selecting the optimal baseline for forest height inversion results in the process of forest height inversion being unable to fully utilize the abundant observation data. In this paper, to solve the problem, we propose a multi-baseline forest height inversion method combining analytic and geometric expression of the random volume over ground (RVoG) model, which takes into account the advantages of the selection of the optimal observation baseline and the utilization of multi-baseline information. In this approach, for any related pixel, an optimal baseline is selected according to the geometric structure of the coherence region shape and the functional model for forest height inversion is established by the RVoG model’s analytic expression. In this way, the other baseline observations are transformed into a constraint condition according to the RVoG model’s geometric expression and are also involved in the forest height inversion. PolInSAR data were used to validate the proposed multi-baseline forest height inversion method. The results show that the accuracy of the forest height inversion with the algorithm proposed in this paper in a coniferous forest area and tropical rainforest area was improved by 17% and 39%, respectively. The method proposed in this paper provides a multi-baseline PolInSAR forest height inversion scheme for exploring regional high-precision forest height distribution. The scheme is an applicable method for large-scale, high-precision forest height inversion tasks.

Additional fractional gradient descent identification algorithm based on multi-innovation principle for autoregressive exogenous models

This paper proposed the additional fractional gradient descent identification algorithm based on the multi-innovation principle for autoregressive exogenous models. This algorithm incorporates an additional fractional order gradient to the integer order gradient. The two gradients are synchronously used to identify model parameters, thereby accelerating the convergence of the algorithm. Furthermore, to address the limitation of conventional gradient descent algorithms, which only use the information of the current moment to estimate the parameters of the next moment, resulting in low information utilisation, the multi-innovation principle is applied. Specifically, the integer-order gradient and additional fractional-order gradient are expanded into multi-innovation forms, and the parameters of the next moment are simultaneously estimated using multi-innovation matrices, thereby further enhancing the parameter estimation speed and identification accuracy. The convergence of the algorithm is demonstrated, and its effectiveness is verified through simulations and experiments involving the identification of a 3-DOF gyroscope system.

Exploration of Gender, Equity, and Information Access and Utilization of Services and Opportunities: Do Ugandan Universities Empower Students and Foster Belonging

Anchored in social justice theory, this study explored gender and equity perspectives on access and utilization of information on services and opportunities by students in Institutions of Higher Learning in Uganda. The goal was to determine whether such information created a sense of belonging. Information on services and opportunities required by students and sources of information provided by Ugandan HIL were examined. The study used a cross-sectional research design. Quantitative and qualitative approaches to collect data were used from six universities. Data was collected from 162 participants (150 students and 12 key informants) using surveys, focus group discussions, key informant interviews and secondary reviews tools. The study identifies student’s representatives and religious leaders as the main sources of information. Although according to social justice theory recommends student’s engagement in activities that affect them universities excluded them from identifying, developing, packaging, disseminating, monitoring and evaluating of their information needs on S&amp;Os. Most critical information required by students includes sexually transmitted infections, reproductive health, HIV/AIDs cooperate governance, career guidance and internship and job placement, scholarships and financial literacy. Generally, information on basic S&amp;O provided by universities did not create a sense of belonging. Information on services and opportunities from university administration was perceived as difficult to comprehend, detached, without speaking to student actualities, yet students were not homogenous nor gendered beings. The insensitivity to gender and equity information needs of students affected more female male students, disabilities and those from disadvantaged communities. This article serves as an opportunity to advance discussions on gender inclusive and equity dimensions towards the creation of a sense of belonging in universities. This would require social justice and gender-inclusive trajectories aimed at dismantling gender-insensitive systems in universities.

Utilization of Information and Communication Technologies (ICTs) Among Women Farmers in North-Eastern Nigeria

This research focused on evaluating the utilization of ICTs by women farmers in North-Eastern Nigeria. A multi-stage sampling method, which included both purposive and simple random sampling techniques, was used to select 359 women farmers for the study. Primary data were gathered and analysed using descriptive and inferential statistics to meet the study's objectives. The majority of respondents (78.0%) were married, with a mean age of 41 years. Most of the respondents (52.5%) were involved in trading, and the average farm size was 3.4 hectares. Additionally, 88.3% had a high social participation score, and 51.7% and 58.9% were aware of old and new ICTs, respectively. Majority of the participants benefited from access to information on fertilizer sources and application (74.9%), credits, grants, and loans (70.8%), market rates and locations (63.1%), and crop pests, diseases, and control (62.1%) through the use of ICTs. Most of the respondents faced several constraints: high cost of gadgets (73.5%), insufficient time allotted to agricultural programmes (73.5%), lack of or inadequate ICT infrastructure (62.3%), expensive power/electricity prices (61.3%), defective equipment (57.8%), and multiple responsibilities of women farmers (54.3%). The regression analysis showed that age, marital status, religion, farm size, household size, and occupations did not significantly affect ICT utilization. However, educational attainment, participation in social organizations, and awareness were significant factors influencing the use of ICTs among women farmers. Based on these findings, it was recommended that the Federal Government of Nigeria should prioritize the establishment of essential infrastructure aimed at promoting and facilitating the utilization of ICTs among women farmers.

Language conditioned multi-scale visual attention networks for visual grounding

Visual grounding (VG) is a task that requires to locate a specific region in an image according to a natural language expression. Existing efforts on the VG task are divided into two-stage, one-stage and Transformer-based methods, which have achieved high performance. However, most of the previous methods extract visual information at a single spatial scale and ignore visual information at other spatial scales, which makes these models unable to fully utilize the visual information. Moreover, the insufficient utilization of linguistic information, especially failure to capture global linguistic information, may lead to failure to fully understand language expressions, thus limiting the performance of these models. To better address the task, we propose a language conditioned multi-scale visual attention network (LMSVA) for visual grounding, which can sufficiently utilize visual and linguistic information to perform multimodal reasoning, thus improving performance of model. Specifically, we design a visual feature extractor containing a multi-scale layer to get the required multi-scale visual features by expanding the original backbone. Moreover, we exploit pooling the output of the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model to extract sentence-level linguistic features, which can enable the model to capture global linguistic information. Inspired by the Transformer architecture, we present the Visual Attention Layer guided by Language and Multi-Scale Visual Features (VALMS), which is able to better learn the visual context guided by multi-scale visual and linguistic features, and facilitates further multimodal reasoning. Extensive experiments on four large benchmark datasets, including ReferItGame, RefCOCO, RefCOCO+ and RefCOCOg, demonstrate that our proposed model achieves the state-of-the-art performance.

Utilization of Information and Communication Technology and 21st Century Skills

Utilization of Information and Communication Technology and 21st Century Skills

Quantitative fecal pollution assessment with bacterial, viral, and molecular methods in small stream tributaries

Stream water quality can be impacted by a myriad of fecal pollution sources and waste management practices. Identifying origins of fecal contamination can be challenging, especially in high order streams where water samples are influenced by pollution from large drainage areas. Strategic monitoring of tributaries can be an effective strategy to identify conditions that influence local water quality. Water quality is assessed using fecal indicator bacteria (FIB); however, FIB cannot differentiate sources of fecal contamination nor indicate the presence of disease-causing viruses. Under different land use scenarios, three small stream catchments were investigated under ‘wet’ and ‘dry’ conditions (Scenario 1: heavy residential; Scenario 2: rural residential; and Scenario 3: undeveloped/agricultural). To identify fecal pollution trends, host-associated genetic targets HF183/BacR287 (human), Rum2Bac (ruminant), GFD (avian), and DG3 (canine) were analyzed along with FIB (Escherichia coli and enterococci), viral indicators (somatic and F+ coliphage), six general water quality parameters, and local rainfall. Levels of E. coli exceeded single sample maximum limits (235 CFU/100 mL) in 70.7 % of samples, enterococci (70 CFU/100 mL) in 100 % of samples, and somatic coliphage exceeded advisory thresholds (600 PFU/L) in 34.1 % of samples. The detection frequency for the human-associated genetic marker was highest in Scenario 3 (50 % of samples) followed by Scenario 2 (46 %), while the ruminant-associated marker was most prevalent in Scenario 1 (64 %). Due to the high proportion of qPCR-based measurements below the limit of quantification, a Bayesian data analysis approach was applied to investigate links between host-associated genetic marker occurrence with that of rainfall and fecal indicator levels. Multiple trends associated with small stream monitoring were revealed, emphasizing the role of rainfall, the utility of fecal source information to improve water quality management. And furthermore, water quality monitoring with bacterial or viral methodologies can alter the interpretation of fecal pollution sources in impaired waters.

Dynamic monitoring and counting for lotus flowers and seedpods with UAV based on improved YOLOv7-tiny

The numbers of lotus flowers and seedpods are the important agronomic traits for lotus breeding and field management. However, the traditional measurement method is manual, subjective, inefficient and labor-intensive. Therefore, this study proposed a novel approach for dynamic monitoring and counting lotus flowers and seedpods with UAV based on improved YOLOv7-tiny. To improve YOLOv7-tiny, the fusion mechanism of SPD layer and convolutions was studied to construct novel SPD-Conv blocks which outperformed the traditional SPD-Conv. Besides, a small target detection layer was adopted to enhance the performance for the lotus flower and seedpod detection. Subsequently, a group of Convolutional Block Attention Modules were embedded in the neck of YOLOv7-tiny to optimize the utilization of channel-spatial information. The P, R, mAP@.5 and mAP@.5:.95 of the improved YOLOv7-tiny model achieved an increase of 0.6%, 4.0%, 3.5% and 3.3% comparing with YOLOv7-tiny. In addition, the MAE, RMSE, and R2 for flowers measurement were 1.90, 2.72, and 0.96 respectively, while 1.96, 2.78, and 0.97 for seedpods respectively. The results demonstrated that the improved YOLOv7-tiny model had satisfactory performance for lotus flowers and seedpods detection and counting. In addition, the specialized software base on the improved YOLOv7-tiny was developed, and a sliding window method was proposed to monitor the large-scale lotus field. This study provides an efficient and convenient measurement method for flowers and seedpods in lotus breeding and field management.

Advanced Global Prototypical Segmentation Framework for Few-Shot Hyperspectral Image Classification.

With the advancement of deep learning, related networks have shown strong performance for Hyperspectral Image (HSI) classification. However, these methods face two main challenges in HSI classification: (1) the inability to capture global information of HSI due to the restriction of patch input and (2) insufficient utilization of information from limited labeled samples. To overcome these challenges, we propose an Advanced Global Prototypical Segmentation (AGPS) framework. Within the AGPS framework, we design a patch-free feature extractor segmentation network (SegNet) based on a fully convolutional network (FCN), which processes the entire HSI to capture global information. To enrich the global information extracted by SegNet, we propose a Fusion of Lateral Connection (FLC) structure that fuses the low-level detailed features of the encoder output with the high-level features of the decoder output. Additionally, we propose an Atrous Spatial Pyramid Pooling-Position Attention (ASPP-PA) module to capture multi-scale spatial positional information. Finally, to explore more valuable information from limited labeled samples, we propose an advanced global prototypical representation learning strategy. Building upon the dual constraints of the global prototypical representation learning strategy, we introduce supervised contrastive learning (CL), which optimizes our network with three different constraints. The experimental results of three public datasets demonstrate that our method outperforms the existing state-of-the-art methods.

A Multi-Scale Mask Convolution-Based Blind-Spot Network for Hyperspectral Anomaly Detection

Existing methods of hyperspectral anomaly detection still face several challenges: (1) Due to the limitations of self-supervision, avoiding the identity mapping of anomalies remains difficult; (2) the ineffective interaction between spatial and spectral features leads to the insufficient utilization of spatial information; and (3) current methods are not adaptable to the detection of multi-scale anomaly targets. To address the aforementioned challenges, we proposed a blind-spot network based on multi-scale blind-spot convolution for HAD. The multi-scale mask convolution module is employed to adapt to diverse scales of anomaly targets, while the dynamic fusion module is introduced to integrate the advantages of mask convolutions at different scales. The proposed approach includes a spatial–spectral joint module and a background feature attention mechanism to enhance the interaction between spatial–spectral features, with a specific emphasis on highlighting the significance of background features within the network. Furthermore, we propose a preprocessing technique that combines pixel shuffle down-sampling (PD) with spatial spectral joint screening. This approach addresses anomalous identity mapping and enables finite-scale mask convolution for better detection of targets at various scales. The proposed approach was assessed on four real hyperspectral datasets comprising anomaly targets of different scales. The experimental results demonstrate the effectiveness and superior performance of the proposed methodology compared with nine state-of-the-art methods.

Multi-level interactive fusion network based on adversarial learning for fusion classification of hyperspectral and LiDAR data

With the growing awareness of the limitations of unimodal data, research on joint classification using multimodal remote sensing data has garnered increasing attention. However, existing methods still have certain deficiencies in dealing with feature extraction and feature fusion of multimodal remote sensing data. In order to obtain more accurate classification results, it is crucial to fully exploit and utilize the complementary information in multimodal remote sensing data. In this paper, a multi-level interactive fusion network based on adversarial learning (MLIF-AL) is proposed for hyperspectral image (HSI) and light detection and ranging (LiDAR) data fusion classification. First, the feature extraction part based on generative adversarial network (GAN) utilizes adversarial training between the generator and the discriminator to guide the network to learn more discriminative and distinguished feature representations. Meanwhile, a cross-modal interactive information extraction (CMIIE) module is also designed for the generator encoding part to promote the interaction between multimodal data from a global perspective and realize the full utilization of multimodal complementary information. In addition, in the multi-level feature fusion classification (MLFFC) part, the complementarities between the features learned by the neural networks at each layer are comprehensively utilized to obtain higher-level multimodal fusion semantic information. Finally, the adversarial loss and classification loss are combined for network training. Extensive experiments on three commonly used HSI and LiDAR datasets and comparisons with state-of-the-art methods demonstrate the effectiveness and superiority of the model.

Multi-scale large kernel convolution and hybrid attention network for remote sensing image dehazing

Remote sensing (RS) image dehazing holds significant importance in enhancing the quality and information extraction capability of RS imagery. The enhancement in image dehazing quality has progressively advanced alongside the evolution of convolutional neural network (CNN). Due to the fixed receptive field of CNN, there is insufficient utilization of contextual information on haze features in multi-scale RS images. Additionally, the network fails to adequately extract both local and global information of haze features. In addressing the above problems, in this paper, we propose an RS image dehazing network based on multi-scale large kernel convolution and hybrid attention (MKHANet). The network is mainly composed of multi-scale large kernel convolution (MSLKC) module, hybrid attention (HA) module and feature fusion attention (FFA) module. The MSLKC module fully fuses the multi-scale information of features while enhancing the effective receptive field of the network by parallel multiple large kernel convolutions. To alleviate the problem of uneven distribution of haze and effectively extract the global and local information of haze features, the HA module is introduced by focusing on the importance of haze pixels at the channel level. The FFA module aims to boost the interaction of feature information between the network's deep and shallow layers. The subjective and objective experimental results on on multiple RS hazy image datasets illustrates that MKHANet surpasses existing state-of-the-art (SOTA) approaches. The source code is available at https://github.com/tohang98/MKHA_Net.

The Measuring the success of the implementation of an information and communication technology (ICT)-based management system using the Technology Acceptance Model (TAM) and the Information Security Index (KAMI) at SMK TELKOM Lampung.

The utilization of information and communication technology (ICT) in education, particularly through the implementation of ICT-based management systems in schools, has great potential to improve the quality of learning and school management. However, the success of this system's implementation is also influenced by factors such as ease of use, perceived benefits, and information security levels. This study aimed to measure the success level of the implementation of ICT-based management systems at SMK Telkom Lampung, specifically the My Learning System (MyLMS) learning platform and the Igracias school application. The study employs the Technology Acceptance Model (TAM) to measure user acceptance of the system and the Information Security Index (KAMI) to assess the maturity level of information security. The method was a survey with a quantitative approach, with a sample of 180 respondents, consisting of students, teachers, and administrative staff who actively use the system. The results show that ease of use (average score 10.74) and perceived benefits (average score 13.91) of the system positively affect user acceptance (average score 13.30), which ultimately enhances the perceived quality of the system (average score 143.67). However, the evaluation using the KAMI Index reveals that SMK Telkom Lampung needs to improve its information security management. The total score is 403 out of 645, indicating that SMK Telkom Lampung is at the "Needs Improvement" level. These results indicate that while the ICT-based management system has been well received by users, the aspect of information security still requires improvement to ensure optimal data and information protection

KMPR-AEP: Knowledge-Enhanced Multi-task Parallelized Recommendation Algorithm Incorporating Attention-Embedded Propagation

Existing knowledge graph-based recommendation algorithms either use Knowledge Graphs (KGs) as auxiliary information or use KG prediction tasks as regular terms to constrain recommendation with multi-task learning. However, the first method, which introduces multi-hop neighbors to enhance item representations, also weakens the relationship information within individual triples to some extent. The second method ignores neighboring information and thus fails to capture the long-range connectivity between items, which leads to a lack of utilization of the structural information in the KG. In response to the limitations of existing recommendation algorithms that do not fully leverage the relational and structural information within KGs, we propose a novel method named Knowledge-Enhanced Multi-Task Parallelized Recommendation Algorithm Incorporating Attention-Embedded Propagation (KMPR-AEP). It employs a parallel approach to simultaneously incorporate KG as auxiliary information and a regular term. We take into account the multi-hop neighbor information of users and items as well as enhance the relations among individual triples, which considers both the structural and relational information of KG. Extensive experiments conducted on three real-world datasets for CTR prediction and Top-K recommendation scenarios demonstrate the superiority of our proposed method over the state-of-the-art. On average, our KMPR-AEP shows improvements of 2.44% in AUC, 3.49% in ACC, and 2.64% in F1.