Precision Ratio Research Articles

Natural Language Processing for Sentiment Analysis in Social Media Posts to Identify Suspicious Behaviour

This article reports a study on the use of the natural language processing approach in the sentiment analysis of social media posts for detecting suspicious activities involving deep learning methods. The primary objective is to design an automated system capable of identifying unfavourable sentiments and patterns that imply suspicious activity such as cyberbullying, hate speech and disinformation. The methodology involves collecting a diverse data set of 2,000 posts from platforms such as X and Facebook, pre-processed by tokenisation and normalisation. The sentiment analysis model uses a long short-term memory (LSTM) network because it finds that the LSTM network is good for recognising long-term dependencies between texts. Other heuristics are used to give prominence to posts with malicious behavioural profiles to flag them accordingly. The model is designed and tuned to pre-labelled data sets. This work shows considerable improvement over previous techniques at categorising the posts as suspicious by showing higher accuracy, precision, recall and F1 score ratios. In doing so, this research positively impacts the enhancement of paranoid security or the totality of social media safety by offering a strong early detection mechanism for reducing risky behaviours on social networks.

Revolutionizing Early Detection: The Influence of Machine Learning in Esophageal Adenocarcinoma Diagnosis

Machine learning is an analysis technique of big data that uses computers and mathematical models to develop algorithms. These algorithms are applied in many situations and one of the most common uses is in developing prediction models. Machine learning is the activity of making prognosis of other data points which are related to the self using key factors from a dataset. It is designed to effectively predict the dysplasia within a given setting using machine learning algorithms on a dataset with primary focus on Barrett’s Esophagus. The study investigates the use of five classification methods: As per the classification, the techniques identified include Naïve Bayes, Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors, and Logistic Regression. Barrett’s Esophagus Below are the methods applied using and without Principal Component Analysis (PCA) on the special dataset of Barrett’s esophagus. The primary objective is the identification of the effectiveness of each algorithm by comparing their functional characteristics such as F1 score, recall value, accuracy percentage, precision ratio, and specificity coefficient. From the results obtained from the experiments, two algorithms that yield good accuracy for predicting Barrett’s esophagus in this dataset are the SVM algorithm with PCA and Logistic Regression algorithm with PCA and both algorithms returned review ratings of 1. undefined This career has earned a maximum Score of 0.1000, Naïve Bayes perform well in the assessment with 0.964 of accuracy, it followed by K-Nearest Neighbors with 0.9349 and Logistic Regression with 0.923.

UAV forest fire detection based on CNN and InceptionV3

Abstract Forest fires have created a severe hazard in our ecological environment with abrasive climate change all over the world. What is even more concerning, the frequency of forest fires is increasing year by year. By far, a great number of methodologies, such as satellite systems, IR, and WSN, have been created to resolve this rigorous problem. However, most of these detection methods are costly and structurally complex, making them difficult to quickly learn and use, thus not effectively reducing personnel and property injuries. Our research proposed using UAVs (Unmanned Aerial Vehicles) as visual detection carriers and using InceptionV3 for visual processing of aerial images. The flame dataset from IEEE Dataport, containing 63178 JPEG pictures, is used in our research. Our proposed forest fire detection methodology using Convolutional Neural Network (CNN) and InceptionV3 achieved a remarkable 89.90% validation accuracy. Moreover, our methodology gained an ideal precision and recall ratio.

Ultrafast Laser Processing for High-Aspect-Ratio Structures.

Over the past few decades, remarkable breakthroughs and progress have been achieved in ultrafast laser processing technology. Notably, the remarkable high-aspect-ratio processing capabilities of ultrafast lasers have garnered significant attention to meet the stringent performance and structural requirements of materials in specific applications. Consequently, high-aspect-ratio microstructure processing relying on nonlinear effects constitutes an indispensable aspect of this field. In the paper, we review the new features and physical mechanisms underlying ultrafast laser processing technology. It delves into the principles and research achievements of ultrafast laser-based high-aspect-ratio microstructure processing, with a particular emphasis on two pivotal technologies: filamentation processing and Bessel-like beam processing. Furthermore, the current challenges and future prospects for achieving both high precision and high aspect ratios simultaneously are discussed, aiming to provide insights and directions for the further advancement of high-aspect-ratio processing.

Gradient Boosting Prediction of Overlapping Genes From Weighted Co-expression and Differential Gene Expression Analysis of Wnt Pathway: An Artificial Intelligence-Based Bioinformatics Study.

Introduction The Wnt (wingless-related integration site) signalling pathway is crucial for bone formation and remodelling, regulating the commitment of mesenchymal stem cells (MSCs) to the osteoblastic lineage. It triggers the transcriptional activation of Wnt target genes and promotes osteoblast proliferation and survival. Weighted co-expression network analysis (WGCNA) and differential gene expression analysis help researchers understand gene roles. Gradient boosting, a machine learning technique, enhances understanding of genetic and molecular mechanisms contributing to overlap genes, improving gene regulation and functional genomics. The aim is to predict overlapping genes in the Wnt signalling pathway. Methods Differential gene expression analysis was performed using the National Center for Biotechnology Information (NCBI) geo dataset-GSE251951, focusing on the effect of Wnt signaling on treatment. The WGCNA module was analyzed using the iDEP tool to identify interconnected gene clusters. Hub genes were identified by calculating module eigengenes, correlated with external traits, and ranked based on module membership values. The study utilized gradient boosting, an ensemble learning method, to predict models, evaluate their performance using metrics like accuracy, precision, recall, and F1 score, and adjust predictions based on gradient and learning rate. Results The dendrogram uses the "Dynamic TreeCut" algorithm to analyze gene clusters, aiding researchers in understanding gene modules and biological processes, identifying co-expressed genes, and discovering new pathways. The confusion matrix displays 88 actual and predicted cases. The gradient boosting model achieves 78.9% accuracy in predicting Wnt pathway overlapping genes, with a respectable area under the curve (AUC) and classification accuracy values. It accurately predicts 73.9% of samples, with a high precision ratio and low recall. Conclusion Future research should enhance differential expression analysis and WGCNA to identify key Wnt pathway genes, improve sensitivity, specificity, hyperparameter tuning, and validation experiments, and use larger datasets.

Mapping tidal restrictions to support blue carbon restoration

Blue carbon ecosystems (BCEs), encompassing mangroves, saltmarshes, and seagrasses, are vital ecosystems that deliver valuable services such as carbon sequestration, biodiversity support, and coastal protection. However, these ecosystems are threatened by various anthropogenic factors, including tidal restrictions like levees, barriers, and embankments. These structures alter the natural seawater flow, often converting coastal ecosystems into freshwater environments. Identifying tidal restrictions and assessing their suitability for tidal restoration in areas amenable for coastal management is a crucial first step to successfully restore BCEs and the associated ecosystem services they provide, i.e., managed realignment. This study presents a novel approach for detecting tidal restrictions in the state of Victoria, Australia, using high-resolution LiDAR data, geospatial analysis techniques, and a multi-criteria scoring system. Our model successfully identified 90 % of known tidal restrictions from an existing dataset, while also detecting an additional 118 potential tidal restrictions, representing a 35 % increase. The model performance analysis revealed trade-offs between precision, recall, and noise ratio when using different noise reduction thresholds, highlighting the importance of selecting an appropriate threshold based on project objectives. The multi-criteria scoring system, which considered factors such as proximity to BCEs and current land use, enabled the selection of tidal restrictions based on their hydrological suitability for restoration. The results of this study have significant implications for BCE restoration efforts not only in Victoria, but more broadly across Australia and globally, providing a systematic approach to identifying and targeting areas with the greatest potential for successful restoration projects. While the approach is low-cost and user-friendly, it is dependent on the availability of LiDAR data for the study area. This can make it accessible to researchers and practitioners worldwide, allowing for its adaptation and application in diverse regions to support global efforts in restoring BCEs through tidal restoration.

Football teaching and training based on video surveillance using deep learning.

The objective performance evaluation of an athlete is essential to allow detailed research into elite sports. The automatic identification and classification of football teaching and training exercises overcome the shortcomings of manual analytical approaches. Video monitoring is vital in detecting human conduct acts and preventing or reducing inappropriate actions in time. The video's digital material is classified by relevance depending on those individual actions. The research goal is to systematically use the data from an inertial measurement unit (IMU) and data from computer vision analysis for the deep Learning of football teaching motion recognition (DL-FTMR). There has been a search for many libraries. The studies included have examined and analyzed training through profound model construction learning methods. Investigations show the ability to distinguish the efficiency of qualified and less qualified officers for sport-specific video-based decision-making assessments. Video-based research is an effective way of assessing decision-making due to the potential to present changing in-game decision-making scenarios more environmentally friendly than static picture printing. The data showed that the filtering accuracy of responses is improved without losing response time. This observation indicates that practicing with a video monitoring system offers a play view close to that seen in a game scenario. It can be an essential way to improve the perception of selection precision. This study discusses publicly accessible training datasets for Human Activity Recognition (HAR) and presents a dataset that combines various components. The study also used the UT-Interaction dataset to identify complex events. Thus, the experimental results of DL-FTMR give a performance ratio of 94.5%, behavior processing ratio of 92.4%, athletes energy level ratio of 92.5%, interaction ratio of 91.8%, prediction ratio of 92.5%, sensitivity ratio of 93.7%, and the precision ratio of 94.86% compared to the optimized convolutional neural network (OCNN), Gaussian Mixture Model (GMM), you only look once (YOLO), Human Activity Recognition- state-of-the-art methodologies (HAR-SAM). This finding proves that exercising a video monitoring system that provides a play view similar to that seen in a game scenario can be a valuable technique to increase selection accuracy perception.

Hyper β-glucosidase producer Beauveria bassiana SAN01—optimization of fermentation conditions and evaluation of saccharification potential

AbstractThe hyper-production of β-glucosidase by a local strain of Beauveria bassiana under submerged conditions is reported in this study. The initial screening of seven agricultural residues showed that the haulm of Bambara—an underutilized African legume—supported the highest β-glucosidase production; hence, statistical optimization of enzyme production was done using this biomass as the sole carbon source. Plackett–Burman design identified the concentrations of Bambara haulm, KCl, and NaCl as well as agitation speed and incubation time as the most significant factors affecting enzyme production. Subsequently, the central composite design predicted the optimal conditions (Bambara 57 g/L, KCl 302 mg/L, NaCl 154 mg/L, agitation speed 150 rpm, and incubation 223 h) for B. bassiana β-glucosidase production, which were further validated. The generated quadratic model was deemed significant judging from its F-value (201.63), adequate precision ratio (45.74), as well as the R2 (0.9988), adjusted R2 (0.9938), and predicted R2 (0.9195) values. The optimization resulted in a ~5.36-fold increase in enzyme levels from the unoptimized production of ~133 to 711 U/mL. The enzyme was also demonstrated to efficiently hydrolyze cellobiose, converting more than 90% of the substrate to glucose. These results further establish the resourcefulness of the B. bassiana strain for the production of β-glucosidase enzyme, having immense potential, especially in the food and energy industries.

Embedding Enhancement Method for LightGCN in Recommendation Information Systems

In the modern digital age, users are exposed to a vast amount of content and information, and the importance of recommendation systems is increasing accordingly. Traditional recommendation systems mainly use matrix factorization and collaborative filtering methods, but problems with scalability due to an increase in the amount of data and slow learning and inference speeds occur due to an increase in the amount of computation. To overcome these problems, this study focused on optimizing LightGCN, the basic structure of the graph-convolution-network-based recommendation system. To improve this, techniques and structures were proposed. We propose an embedding enhancement method to strengthen the robustness of embedding and a non-combination structure to overcome LightGCN’s weight sum structure through this method. To verify the proposed method, we have demonstrated its effectiveness through experiments using the SELFRec library on various datasets, such as Yelp2018, MovieLens-1M, FilmTrust, and Douban-book. Mainly, significant performance improvements were observed in key indicators, such as Precision, Recall, NDCG, and Hit Ratio in Yelp2018 and Douban-book datasets. These results suggest that the proposed methods effectively improved the recommendation performance and learning efficiency of the LightGCN model, and the improvement of LightGCN, which is most widely used as a backbone network, makes an important contribution to the entire field of GCN-based recommendation systems. Therefore, in this study, we improved the learning method of the existing LightGCN and changed the weight sum structure to surpass the existing accuracy.

Analysis of Employment Competitiveness of College Students Based on Binary Association Rule Extraction Algorithm

Today, assessing competition among college students in the job search is extremely important. However, various methods available are often inaccurate or inefficient when it comes to determining the level of their readiness for work. Conventional techniques usually depend on simplistic measures or miss out on crucial factors responsible for employability. The challenging characteristics of such competitive employment of college students are the lower levels of perceived stress, financing my education, and crucial professional skills. Hence, in this research, the Internet of Things Based on Binary Association Rule Extraction Algorithm (IoT-BAREA) technologies have improved college students' employment competitiveness. IoT-BAREA addresses this situation using a binary association rule extraction algorithm that helps detect significant patterns and relationships in large amounts of data involving student attributes and employment outcomes. IoT-BAREA positions itself as capable of providing insights into features that highly mediate the employability levels among students. This paper closes this gap and recommends a new IoT-BAREA method to help increase accuracy and efficiency in evaluating student employment competitiveness. Specifically, this study uses rigorous evaluation methods such as precision, recall and interaction ratio to determine how well IoT-BAREA predicts students' employability.

Application of recommendation system based on fuzzy logic in music culture communication and music teaching management

Music has been utilized throughout history as a medium for cultural communication and artistic expression, embodying various nations’ and societies’ ideologies and experiences. Music culture communication is crucial for encouraging cultural diversity and understanding and developing social cohesion and community building among people. Music teaching management is the process of setting up, arranging, and executing music education programs in a manner that successfully teaches students the essential skills and information necessary for becoming proficient musicians. Users’ exact preferences for various areas of attraction cannot be determined, nor are users’ choices for traditional music recommendations sufficiently accurate. A recommender system estimates or anticipates people’s preferences and offers appropriate recommendations. First, the sparsity problem emerges when insufficient data is accessible for the recommendation, and the coverage is one of the key drawbacks of social labeling. Cold start issues might be difficult since new music learners might not have given sufficient details about their musical tastes. Hence, the Hybridized Fuzzy logic-based Content and Collaborative Music Recommendation (HFC2MR) system is proposed to create personalized music teaching plans that are effective and engaging for each student based on their music preferences and learning outcomes. Enhanced Fuzzy C-Means clustering is used in collaborative recommendations to group users based on their shared musical tastes and to provide each user with more individualized, accurate music recommendations based on other users’ listening habits and preferences in the same cluster. Subsequently, an assessment of the recommender system using parameters like accuracy, precision, f1-score, and recall ratio is shown with optimal cluster selection. The coverage ratio is used to compare experimental data based on skill capacity covered through the assessment of music teaching. RMSE metric is used to evaluate the accuracy of students’ performance based on music attributes related to teaching goals.

Efficient Sampling of Two-Stage Multi-Person Pose Estimation and Tracking from Spatiotemporal

Tracking the articulated poses of multiple individuals in complex videos is a highly challenging task due to a variety of factors that compromise the accuracy of estimation and tracking. Existing frameworks often rely on intricate propagation strategies and extensive exchange of flow data between video frames. In this context, we propose a spatiotemporal sampling framework that addresses the degradation of frames at the feature level, offering a simple yet effective network block. Our spatiotemporal sampling mechanism empowers the framework to extract meaningful features from neighboring video frames, thereby optimizing the accuracy of pose detection in the current frame. This approach results in significant improvements in running latency. When evaluated on the COCO dataset and the mixed dataset, our approach outperforms other methods in terms of average precision (AP), recall rate (AR), and acceleration ratio. Specifically, we achieve a 3.7% increase in AP, a 1.77% increase in AR, and a speedup of 1.51 times compared to mainstream state-of-the-art (SOTA) methods. Furthermore, when evaluated on the PoseTrack2018 dataset, our approach demonstrates superior accuracy in multi-object tracking, as measured by the multi-object tracking accuracy (MOTA) metric. Our method achieves an impressive 11.7% increase in MOTA compared to the prevailing SOTA methods.

Research on neural networks in computer network security evaluation and prediction methods

Anomaly detection in networks to identify intrusions is a common and successful security measure used in many different types of network infrastructure. Network data traffic has increased due to the proliferation of viruses and other forms of cyber-attacks as network technology and applications have developed quickly. The limitations of classical intrusion detection, such as poor detection accuracy, high false negatives, and dependence on dimensionality reduction methods, become more apparent in the face of massive traffic volumes and characteristic information. That’s why IoT infrastructures often use Software-Defined Networking (SDN), allowing for better network adaptability and control. Hence, this paper’s convolutional neural network-based Security Evaluation Model (CNN-SEM) is proposed to secure the source SDN controller from traffic degradation and protect the source network from DDoS assaults. The proposed CNN-SEM system might defend against DDoS assaults once discovered by applying and testing a Convolutional Neural Network (CNN). The model can automatically extract the useful aspects of incursion samples, allowing for precise classification of such data. The detection and mitigation modules evaluate the proposed SDN security system’s performance, and the findings showed promise against next-generation DDoS assaults. The experimental results show the CNN-SEM achieves a high accuracy ratio of 96.6%, a detection ratio of 97.1%, precision ratio of 97.2%, a performance ratio of 95.1% and an enhanced security rate of 98.1% compared to other methods.

Detecting attacks on e-mail

E-mail has become a popular communication tool widely used by universities, enterprises and governments. Despite the convenience it brought to people, attacks on e-mail happen very frequently in the range of the world, causing large economic loss and occupying a mass of network bandwidth every year. The hazards from e-mail attacks underline the importance of detecting and resisting spam in an efficient and timely way. Using Python, we built Na¨ıve Bayes (NB) and support vector machine (SVM) filters for emails. The filtering performance of NB and SVM email filters applying different kernel functions was compared and evaluated based on several evaluation indices including accuracy, precision, and total cost ratio (TCR). Also, in order to optimize the filters, the influences of stop words removal, feature numbers and other parameters in the filtering algorithms were monitored.

Velocity Augmentation Model for an Empty Concentrator-Diffuser-Augmented Wind Turbine and Optimisation of Geometrical Parameters Using Surface Response Methodology

Wind energy, renowned for cost-effectiveness and eco-friendliness, addresses global energy needs amid fossil fuel scarcity and environmental concerns. In low-wind speed regions, optimising wind turbine performance becomes vital and achievable by augmenting wind velocity at the turbine rotor using augmentation systems such as concentrators and diffusers. This study focuses on developing a velocity augmentation model that correctly predicts the throat velocity in an empty concentrator-diffuser-augmented wind turbine (CDaugWT) design and determines optimal geometrical parameters. Utilising response surface methodology (RSM) in Design Expert 13 and computational fluid dynamics (CFD) in ANSYS Fluent, 86 runs were analysed, optimising parameters such as diffuser and concentrator angles and lengths, throat length, and flange height. The ANOVA analysis confirmed the model’s significance (p < 0.05). Notably, the interaction between the concentrator’s length and the diffuser’s length had the highest impact on the throat velocity. The model showed a strong correlation (R2 = 0.9581) and adequate precision (ratio value of 49.655). A low coefficient of variation (C.V.% = 0.1149) highlighted the model’s reliability. The findings revealed a 1.953-fold increase in inlet wind speed at the throat position. Optimal geometrical parameters for the CDaugWT included a diffuser angle of 10°, concentrator angle of 20°, concentrator length of 375 mm (0.62Rth), diffuser length of 975 mm (1.61Rth), throat length of 70 mm (0.12Rth), and flange height of 100 mm (0.17Rth) where Rth is the throat radius. A desirability value of 0.9, close to 1, showed a successful optimisation. CFD simulations and RSM reduced calculation cost and time when determining optimal geometrical parameters for the CDaugWT design.

Theoretical, numerical, and experimental investigation on second-order Bezier curve flexure hinges

This paper proposes the second-order Bezier curve flexure hinge. The flexure hinge can obtain different notch profiles by changing the position of the control points. Firstly, the compliance and precision of the flexure hinge are modeled based on the force deformation formula of the cantilever beam in material mechanics. Compared with the simulation data of ANSYS Workbench, the modeling error is less than 7%. Secondly, the influence of geometric parameters on compliance and precision is analyzed. The flexure hinge is compared with circular, elliptical, and parabolic hinges. The results show that the second-order Bezier curve flexure hinges have a higher compliance precision ratio. In addition, the use of second-order Bezier curve flexure hinges provides better performance than the use of traditional circular and elliptical flexure hinges in bridge amplification mechanisms. Finally, a flexure hinge was manufactured and measured. The experimental results show that the error between the experimental and modeling values is 8.76%. Both the simulation and experimental results verified the effectiveness of the model.

Distributed private preserving learning based chaotic encryption framework for cognitive healthcare IoT systems

In the field of cognitive healthcare Internet of Things (CH-IoT), there is a strong demand for reliable and minimally intrusive smart gadgets that consistently acquire, analyse, and obtain the confidential health details of the individual. In fact, CH-IoT is empowered with artificial intelligence (AI) to transmute a fewer operational inputs into actionable, intelligent actions through the digitization of medical healthcare data. However, these systems consume more network complexity, interaction, and overhead costs, while inducing a blend of susceptibility and confidentiality issues. In support of this complexity, these cognitive systems need centralised data collection and to be gathered and analysed, which affects scalability issues and adds fuel to privacy and security breaches. Even though it possesses greater intricacy in its potential application, a substantial factor is maintaining the private preservation of healthcare data against the growing attacks. Thus, this paper presents a distributed privacy-preserving, chaotic encryption-based framework that can be deployed for CH-IoT systems to safeguard sensitive data against message modification, denial of service (DoS), and man-in-the-middle attacks (MIM), guaranteeing privacy and data integrity. The proposed framework integrated the federated learning layered hybrid chaotic encryption strategies by investigating through examination the learning infrastructure of convolutional neural networks (CNN). In the examination, the complete framework was carried out in the Tensorflow Federated Learning Libraries (FLL), and numerous performance metrics such as accuracy, precision, recall, f1-score, transmission efficiency, and overhead ratio were measured and contrasted with the various existing frameworks. For the intensive analysis, formal and informal security experiments were also conducted by NIST (National Institute of Science and Technology). The analytical results illustrate the importance of the proposed framework by achieving better security performance and outperforming the other existing models. Lastly, the proposed framework has more potential than the other existing frameworks and finds its place in real-time healthcare systems, but it needs to be improvised for real-time datasets.

Recognition and statistical method of cows rumination and eating behaviors based on Tensorflow.js

Information about dairy cow ruminating is closely associated with the health status of dairy cows. Therefore, it is of great significance to recognize and make statistics of dairy cows’ ruminating and feeding behavior. Concerning conventional recognition methods which are dependent on contact type devices, they have some defects of poor instantaneity and strong stress responses. As for recognition based on machine vision, it needs to transmit masses of data and raises high requirements for the cloud server and network performance. According to principles of edge computing, the model is deployed via Tensorflow.js in an edge device in the present study, constructing a recognition and statistical system for ruminating and feeding behavior of dairy cows. Through the application programming interface (API) of the browser, an edge device is able to invoke a camera and acquire dairy cow images. Then, the images can be inputted in the SSD MobileNet V2 model, which is followed by inference based on browser hashrate. Moreover, the edge device merely uploads recognition results to the cloud server for statistics, which features high instantaneity and compatibility. In terms of recognizing ruminating and feeding behavior of dairy cows, the proposed system has a precision ratio of 96.50%, a recall rate of 91.77%, an F1-score of 94.08%, specificity of 91.36%, and accuracy of 91.66%. This suggests that the proposed method is effective in recognizing dairy cow behavior.

Lightweight and smart data fusion approaches for wearable devices of the Internet of Medical Things

Internet of Medical Things (IoMT) refers to a group of dedicated devices, i.e., wearable sensors attached to the human body for capturing the real-time data, specifically physiological signals, to develop an automated and technology-assisted smart healthcare framework. However, due to the miniaturized nature and their limited capabilities, data captured by these wearable devices need to be consistent, especially in terms of accuracy and precision. Therefore, data must be refined prior to the actual processing, which is a challenging task in a smart healthcare infrastructure. In the literature, various approaches have been reported to address this issue. However, most of these approaches are either domain-specific or extremely difficult to implement, especially in the resource-constrained environments of IoMT. In this paper, we present two-tier lightweight device and server-enabled data fusion approaches for wearable devices and their server modules, respectively. In these approaches, each device is bound to perform an in-node processing, i.e., local data fusion, on the sensed data values prior to initialization of the transmission process. For this purpose, each wearable device is required to use knowledge of the previously transmitted refined data values. Additionally, the proposed approach is pruned against abrupt changing scenarios, e.g. heart attack, where the frequency of sensed data values drastically fluctuates. Moreover, a non-metric-based data fusion, i.e., global data fusion, is presented to enhance the accuracy of sensed data by removing redundant data values, especially those collected by neighboring devices. Simulation results reflect the exceptional performance of these smart data fusion approaches, i.e., both local and global, in the context of numerous performance evaluation metrics, such as accuracy and precision ratio.

A Hybrid POI Recommendation System Combining Link Analysis and Collaborative Filtering Based on Various Visiting Behaviors

Location-based recommender systems (LBRSs) have exhibited significant potential in providing personalized recommendations based on the user’s geographic location and contextual factors such as time, personal preference, and location categories. However, several challenges (such as data sparsity, the cold-start problem, and tedium problem) need to be addressed to develop more effective LBRSs. In this paper, we propose a novel POI recommendation system, called LACF-Rec3, which employs a hybrid approach of link analysis (HITS-3) and collaborative filtering (CF-3) based on three visiting behaviors: frequency, variety, and repetition. HITS-3 identifies distinctive POIs based on user- and POI-visit patterns, ranks them accordingly, and recommends them to cold-start users. For existing users, CF-3 utilizes collaborative filtering based on their previous check-in history and POI distinctive aspects. Our experimental results conducted on a Foursquare dataset demonstrate that LACF-Rec3 outperforms prior methods in terms of recommendation accuracy, ranking precision, and matching ratio. In addition, LACF-Rec3 effectively solves the challenges of data sparsity, the cold-start issue, and tedium problems for cold-start and existing users. These findings highlight the potential of LACF-Rec3 as a promising solution to the challenges encountered by LBRS.