Suç Kategorisi Tespiti için Yığınlama Topluluk Öğrenimi Modeli Kullanan Çatı Tasarımı

Author:

ARSLAN Recep Sinan1ORCID,DÜLGEROĞLU Burak2ORCID

Affiliation:

1. KAYSERİ ÜNİVERSİTESİ, MÜHENDİSLİK, MİMARLIK VE TASARIM FAKÜLTESİ, BİLGİSAYAR MÜHENDİSLİĞİ BÖLÜMÜ

2. KAYSERİ ÜNİVERSİTESİ, LİSANSÜSTÜ EĞİTİM ENSTİTÜSÜ

Abstract

Crime refers to an action legally defined as harmful to society, and it is important to understand the type of crime to prevent these actions. However, crime can occur at any time and place, making it difficult to predict. Data generated based on previously committed crimes contributes to overcoming this difficulty. This study proposes a novel model for classifying criminal activities using a Doc2Vec that can cause a numerical representation of texts regardless of length and a stacking ensemble model that includes 8 different machine-learning models. Unlike the literature, the model processes the features as text and converts them into vectors rather than categorically. In this way, it enables using features that cannot be used in the literature. The proposed model is tested using a distributed online competition database, Francisco Crime Classification, which contains crimes committed over 12 years. An accuracy value of 99.28% was obtained for the 15 crime categories with the highest crime records, while precision, recall, and f-score values were 99.18%, 99.38%, and 99.20%, respectively. With cross-validation (k=10), 99.80% performance was achieved with a std. value of 0.001. These performance values are higher than those of all the studies in the literature using categorical feature structures. The results show that converting criminal activity reports, which contain text-based features, into vectors that can be processed with natural language processing techniques such as Doc2vec instead of using them categorically in model training can directly contribute to the classification performance and provide a more efficient model with less preprocessing.

Publisher

Cukurova Universitesi Muhendislik-Mimarlik Fakultesi Dergisi

Subject

General Medicine

Reference31 articles.

1. 1. İçli, T.G., 1993. Türkiye’de Suçlular (Sosyal Kültürel ve Ekonomik Özellikleri. Atatürk Kültür, Dil ve Tarih Kurumu Atatürk Kültür Merkezi Yayını, Ankara, 71.

2. 2. Hochstetler, J., Hochstetler, L., Fu, S., 2016. An Optimal Police Patrol Planning Strategy for Smart City Safety. IEEE 18th International Conference on High Performance Computing and Communications, Sydney, Australia, 1256-1263.

3. 3. Open Government, https://www.data.gov/open -gov/, Access date: Haziran 2023.

4. 4. Data.world Crime Datasets, https://data.world/ datasets/crime, Access date: Temmuz 2023.

5. 5. All Data Related to Crime And Justice, https://www.ons.gov.uk/peoplepopulationandcommunity/crimeandjustice/datalist?filter=datasets, Access date: Ağustos 2023.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3