Fuzzy Hypergraph Modeling, Analysis and Prediction of
Crimes

P.M., Dhanya; P.B., Ramkumar; Cletus, Nirmaljith; Joy, Paul

doi:10.12785/ijcds/110152

Cited by 5 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two types of MAEs are used: The MAE RS (i) calculate MAE for a given tourist i according to equation (9):…”

Section: ) Mae (Mean Absolute Error)mentioning

confidence: 99%

“…These systems can recommend POIs such as hotels [3], restaurants [4], e-commerce products [5], tourist destinations [6], movies [7], and scientific articles [8]. In addition, these systems can also predict actions such as criminal behaviour associated with a place [9], the intellectual performance of learners [10], etc. However, in the field of smart tourism, RSs can recommend POIs without taking into account the actual context of the tourist (location, weather, company, means of travel, etc.)…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Toward a Systematic Evaluation Approach of Point Of Interest Recommendation Algorithms of a Novel Smart Tourism Tool

HADJHENNI,

DENNOUNI,

SLAMA

2024

IJCDS

View full text Add to dashboard Cite

Intelligent tourism can increase the interest of nomadic tourists in discovering new cities. However, many Points of Interest (POIs) are available, creating an information overload for tourists when choosing POIs to visit. For this reason, CARS (Context-Aware Recommendation Systems) can play an important role by exploiting the experiences of previous tourists and their contexts to recommend attractive POIs. Consequently, choosing the right POI recommendation algorithm (RA) for CARS is crucial because it involves the costly intervention of real tourists during the test phase. In order to make this phase more cost-effective, we can test several RAs simultaneously in order to assess their limitations in terms of cold start and tourist satisfaction. To compare these RAs, we propose in this article an approach called SEPRA (Systematic Evaluation for POI Recommendation Algorithms), which allows us to carry out an initial online evaluation of each tourist during their visit and a second offline evaluation of each CARS after the end of each POI path. To achieve this objective, we designed and implemented a new smart tourism tool that makes POI recommendations using two algorithms: the first is based on tourist/tourist similarity, and the second uses POI/POI similarity. These algorithms use memory-based collaborative filtering and are executed in parallel by our tool in the form of CARSs, incorporating time or weather as context variables. To evaluate these systems during their test phases, Our approach enables: (1) the calculation of prediction accuracy; (2) the examination of the relevance of the recommended POIs; and (3) the estimation of the acceptance rate of the recommendation process. Finally, the experimental results obtained with our approach show that the algorithm based on tourist similarity is more resistant to the cold start problem during the test phase and has a better satisfaction rate than the algorithm based on POI similarity.

show abstract

“…Two types of MAEs are used: The MAE RS (i) calculate MAE for a given tourist i according to equation (9):…”

Section: ) Mae (Mean Absolute Error)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward a Systematic Evaluation Approach of Point Of Interest Recommendation Algorithms of a Novel Smart Tourism Tool

HADJHENNI,

DENNOUNI,

SLAMA

2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…Later, they analysed some significant characteristics of fuzzy F * -homotopy and also proved that each fuzzy F * -loop based at any fuzzy point in fuzzy F * -contractible space is equivalent to the constant fuzzy F * -loop. Dhanya et al [25] proposed a fuzzy hypergraph-based model to predict crimes in various locations. The crime fuzzy hypergraph contains two layers: an outer level and an interior level.…”

Section: Literature Surveymentioning

confidence: 99%

An Effective Fuzzy Clustering of Crime Reports Embedded by a Universal Sentence Encoder Model

et al. 2023

View full text Add to dashboard Cite

Crime reports clustering is crucial for identifying and preventing criminal activities that frequently happened in society. In the proposed work, named entities in a report are recognized to extract the crime-related phrases and subsequently, the phrases are preprocessed by applying stopword removal and lemmatization operations. Next, the module of the universal encoder model, called the transformer, is applied to extract phrases of the report to get a sentence embedding for each associated sentence, aggregation of which finally provides the vector representation of that report. An innovative and efficient graph-based clustering algorithm consisting of splitting and merging operations has been proposed to get the cluster of crime reports. The proposed clustering algorithm generates overlapping clusters, which indicates the existence of reports of multiple crime types. The fuzzy theory has been used to provide a score to the report for expressing its membership into different clusters, and accordingly, the reports are labelled by multiple categories. The efficiency of the proposed method has been assessed by taking into account different datasets and comparing them with other state-of-the-art approaches with the help of various performance measure metrics.

show abstract

“…In addition, the book also sheds light on real-world applications of these hypergraphs, making it a valuable resource for students and researchers in the field of mathematics, as well as computer and social scientists [17]. There is also some research about fuzzy (hyper) graphs and their applications in complex hypernetworks, such as the implementation of single-valued neutrosophic soft hypergraphs on the human nervous system [18], decision-making methods based on fuzzy soft competition hypergraphs [19], hypergraph and network flow-based quality function deployment [20], global domination in fuzzy graphs using strong arcs [21], fuzzy hypergraph modeling, analysis and prediction of crimes [22], single-valued neutrosophic directed (hyper) graphs and applications in networks [23], achievable single-valued neutrosophic graphs in wireless sensor networks [24], fuzzy hypergraph network for recommending top-k profitable stocks [25], an algorithm to compute the strength of competing interactions in the bearing sea based on Pythagorean fuzzy hypergraphs [26] and centrality measures in fuzzy social networks [27]. Recently, Smarandache extended hypergraphs to a new concept as nsuperhypergraph and Plithogenic n-superhypergraph which have several properties and are connected with the real-world [28].…”

Section: Introductionmentioning

confidence: 99%

Decision Making Based on Valued Fuzzy Superhypergraphs

Hamidi,

Smarandache,

Taghinezhad

2024

CMES

View full text Add to dashboard Cite

This paper explores the defects in fuzzy (hyper) graphs (as complex (hyper) networks) and extends the fuzzy (hyper) graphs to fuzzy (quasi) superhypergraphs as a new concept. We have modeled the fuzzy superhypergraphs as complex superhypernetworks in order to make a relation between labeled objects in the form of details and generalities. Indeed, the structure of fuzzy (quasi) superhypergraphs collects groups of labeled objects and analyzes them in the form of the part to part of objects, the part of objects to the whole group of objects, and the whole to the whole group of objects at the same time. We have investigated the properties of fuzzy (quasi) superhypergraphs based on any positive real number as valued fuzzy (quasi) superhypergraphs, considering the complement of valued fuzzy (quasi) superhypergraphs, the notation of isomorphism of valued fuzzy (quasi) superhypergraphs based on the permutations, and we have presented the isomorphic conditions of (self complemented) valued fuzzy (quasi) superhypergraphs. The concept of impact membership value of fuzzy (quasi) superhypergraphs is introduced in this study and it is applied in designing the real problem in the real world. Finally, the problem of business superhypernetworks is presented as an application of fuzzy valued quasi superhypergraphs in the real world.

show abstract

Fuzzy Hypergraph Modeling, Analysis and Prediction of Crimes

Cited by 5 publications

References 5 publications

Toward a Systematic Evaluation Approach of Point Of Interest Recommendation Algorithms of a Novel Smart Tourism Tool

Toward a Systematic Evaluation Approach of Point Of Interest Recommendation Algorithms of a Novel Smart Tourism Tool

An Effective Fuzzy Clustering of Crime Reports Embedded by a Universal Sentence Encoder Model

Decision Making Based on Valued Fuzzy Superhypergraphs

Contact Info

Product

Resources

About