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Deciphering Property Crime Through OLS Regression: A Demographic Study

International Social science Journal 2025年第256期75卷 395-412页

作者： Masron, Tarmiji Ahmad, Azizul Abdillah, Kiky Kirina Ali, Asykal Syakinah Mohd Junaini, Syahrul Nizam Kimura, Yoshinari Centre for Spatially Integrated Digital Humanities (CSIDH) Faculty of Social Sciences & Humanities Universiti Malaysia Sarawak Sarawak Kota Samarahan Malaysia Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Sarawak Kota Samarahan Malaysia Graduate School of Literature and Human Sciences Osaka Metropolitan University Osaka Sumiyoshi-Ku Japan

This study explores the correlation between property crime and demographic factors in Kuala Lumpur and Putrajaya using spatial autocorrelation (SA) and ordinary least squares (OLS) regression from 2015 to 2020. The 2016 SA analysis shows a significant increase in Moran's I index (0.012905), with a positive z-score of 2.020088 and a p value of 0.043374, indicating spatial clustering of crime. The study examines how factors like total population, household areas, residential areas, male populations and female populations influence number of property crime cases, revealing varying relationships year by year. By highlighting fluctuations in R-squared and F-statistic values, this research challenges static crime models, advocating for adaptable, data-driven strategies in crime prevention. These findings emphasize the importance of continuous policy adjustments to address the evolving socio-economic dynamics of urban areas. © 2025 John Wiley & Sons Ltd.

关键词： Kuala Lumpur ordinary least squares (OLS) regression analysis population study property crime Putrajaya

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An automated approach for difference detection in cultural heritage applications

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Multimedia Tools and Applications 2025年 1-21页

作者： Saiti, Evdokia Saha, Sunita Bunsch, Eryk Sitnik, Robert Theoharis, Theoharis Department of Computer and Information Science Norwegian University of Science and Technology Trondheim7030 Norway Faculty of Mechatronics Warsaw University of Technology św. Andrzeja Boboli 8 Warsaw02-525 Poland Saarland Saarbrücken Germany Museum of King Jan III’s Palace at Wilanów ul. Stanislawa Kostki Potockiego 10/16 Warsaw02-958 Poland

This paper presents the application of two key stages in Cultural Heritage (CH) analysis: cross-time registration and change detection, aimed at automatically identifying subtle geometric variations in CH objects. The proposed method addresses the challenge of manually aligning and detecting differences among large collections of 3D-digitized objects, which is both time-consuming and error-prone. The method combines CrossTimeReg deep learning technique for automatic registration and the Change-Based-Segmentation method for identifying differences and segmented changes. We applied this method to two ceramic sculptures, titled Zephyr and Flora, from the Museum of King Jan III’s Palace at Wilanów, Poland, and validated the results with the CH scientific experts of the museum. The results demonstrated that our method effectively detected geometric variations resulting from potential alterations or restorations, which are crucial for verifying the authenticity of artifacts. Additionally, the method facilitated comparative analysis, enabling researchers to examine similar objects and establish connections, origins, and historical significance. The technique also proved useful in analyzing changes in the geometry of the same object over time due to destructive factors. © The Author(s) 2025.

关键词： Change detection

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Path Planning Method for Mobile Robots in Pedestrian-Friendly Environments Using Drone Bird's-Eye View

Path Planning Method for Mobile Robots in Pedestrian-Friendl...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Ye, Linghao Chugo, Daisuke Muramatsu, Satoshi Yokota, Sho Hashimoto, Hiroshi Kwansei Gakuin University Graduate School of Science and Technology Hyogo Sanda6691330 Japan Tokai University School of Information Science and Technology Kanagawa Hiratsuka2591292 Japan Toyo University Faculty of Science and Engineering Saitama Kawagoe3508585 Japan Advanced Institute of Industrial Technology Master Program of Innovation for Design and Engineering Shinagawa Tokyo1400011 Japan

ISBN: (纸本)9798331531614

The objective of this paper is to develop a robot system that can transmit the video taken by the drone to the computer in real time, use YOLO to recognize the image of pedestrians, and enable the ground robot to plan the path to avoid pedestrians. External sensors, such as laser range finders (LRF) and CCD cameras, are often used on mobile robots. As we all know, these sensors are susceptible to occlusion. Therefore, even if the mobile robot avoids the pedestrian in front of it, it is likely to collide with other pedestrians that cannot be detected due to occlusion. In this study, we used a drone to shoot videos at high altitudes, transferred the videos to a computer, used YOLO on the computer to perform image recognition of pedestrians around the robot, and passed the recognition results to ROS, which enabled it to plan a path to avoid pedestrians around it. As a result, showed that the videos shot at high altitudes could effectively identify pedestrians around the robot after being recognized by YOLO. © 2025 IEEE.

关键词： Drones

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A Simple Deep Learning Model for Classifying Oranges in Embedded Manufacturing Settings

SSRN

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SSRN 2023年

作者： Salaiwarakul, Anongporn Mungklachaiya, Sujitranan Program in Computer and Information Technology Faculty of Science and Technology Loei Rajabaht University Loei42000 Thailand Department of Computer Science and Information Technology Faculty of Science Naresuan University Phitsanulok65000 Thailand

Recently, deep learning has emerged as a powerful tool for automating various tasks in manufacturing industry. Deep learning has shown promise particularly in classifying food products, e.g., identifying sweet oranges in a factory setting. Traditionally, classification of oranges based on sweetness is a labor-intensive and time-consuming process, requiring manual inspection and tasting by trained personnel. However, by employing deep learning techniques, this process can be automated, giving rise to accurate and consistent results at a much faster rate. In this study, we propose a deep learning model, combined with computer vision, for classifying the sweetness of oranges in a factory setting. By training a deep neural network on a dataset of labeled images, we developed a simple Convolutional Neural Network (sCNN) model with few layers that can accurately predict the sweetness of new oranges based on their images. We evaluated the performance of the model and compare it with four CNN architecture-transfer learning models of orange classification. Upon comparison with four different CNN architecture-transfer learning models, namely, MobileNet-V2, Inception-V3, VGG-16, and ResNet-50, we found that while each of these models yields a satisfactory result, learning rates of 0.001 or 0.0001 yielded superior results in every model. Without the application of dropout and image augmentation, ResNet-50 yielded the best classification accuracy (81.43%) of the five models, and the model performance improved upon employment of these methods. However, the provided images were not suitable for Inception-V3 models. Nevertheless, Simple CNN shows that the augmentation method enables the model to learn, achieving an accuracy of 81.43% with a dropout of 0.3, which is close to the VGG-16's highest accuracy of 83.57% (dropout = 0.0) and MobileNet-V2’s 74.29% (dropout = 0.3). © 2023, The Authors. All rights reserved.

关键词： Convolutional neural networks

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TELEX: Two-Level Learned Index for Rich Queries on Enclave-based Blockchain Systems

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IEEE Transactions on Knowledge and Data Engineering 2025年第7期37卷 4299-4313页

作者： Wu, Haotian Tang, Yuzhe Shen, Zhaoyan Tao, Jun Lin, Chenhao Peng, Zhe The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Syracuse University Department of Electrical Engineering and Computer Science United States Shandong University School of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Xi'an Jiaotong University Faculty of Electronic and Information Engineering China The Hong Kong Polytechnic University Shenzhen Research Institute Hong Kong

Blockchain has become a popular paradigm for secure and immutable data storage. Despite its numerous applications across various fields, concerns regarding the user privacy and result integrity during data queries persist. Additionally, the need for rich query functionalities to harness the full potential of blockchain data remains an area ripe for exploration. In order to address these challenges, our paper first utilizes a framework based on the Trusted Execution Environment (TEE) and oblivious RAM technique to achieve both privacy and data integrity. To enhance the query efficiency over the entire blockchain, we then devise a two-level learned indexing methodology named TELEX within the TEE for both integer and string keys. We also propose different query processing algorithms for versatile query types, including exact queries, aggregate queries, Boolean queries, and range queries. By implementing the prototype and conducting extensive evaluation, we demonstrate the feasibility and remarkable improvement in efficiency compared to existing solutions. © 1989-2012 IEEE.

关键词： Query languages

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Spatio-temporal Network Analysis for Detecting Traffic Accident Clusters 14

Spatio-temporal Network Analysis for Detecting Traffic Accid...

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14th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2023

作者： Ando, Keisuke Kuniyoshi, Yusuke Shimizu, Kazuhiko Uchitane, Takeshi Mukai, Naoto Iwata, Kazunori Ito, Nobuhiro Jiang, Yong Graduate School of Business Administration and Computer Science Aichi Institute of Technology Toyota Japan Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan Faculty of Information Science Aichi Institute of Technology Toyota Japan Sugiyama Jogakuen University Faculty of Culture-Information Studies Nagoya Japan Aichi University Faculty of Business Administration Nagoya Japan Aichi University Faculty of Regional Policy Toyohashi Japan

ISBN: (纸本)9798350324228

Various methods have been proposed for analyzing traffic accident hotspots. One of these methods is to detect traffic accident hotspots on the road network using a hypothesis testing method. However, this method does not consider the time of occurrence of a traffic accident. In other words, this method provides information on high-risk locations for traffic accidents but not on the corresponding time. This paper proposes a new method for detecting traffic accident hotspots considering not only location but also time. We therefore extended the previous hypothesis testing method to consider the time of occurrence of a traffic accident. First, we check for changes in spatial properties over time in the target area using local indicators of spatial autocorrelation (LISA) cluster maps. Next, we estimate the probability density function of traffic accidents in the spatio-temporal network using the spatio-temporal network kernel density estimation (STNKDE) method. Finally, we detect clusters where the probability density of traffic accidents is significantly higher through hypothesis testing based on the estimation results. We detect clusters under four conditions while testing different hypotheses and parameters. The results show that the proposed method can detect traffic accident clusters. Therefore, when considering traffic safety measures, the method can be adopted to detect the location and time that require attention and allocate the necessary resources. © 2023 IEEE.

关键词： Roads and streets

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Dagang Yuk Yuk As Smartphone Application for MSME Product Sales 2

Dagang Yuk Yuk As Smartphone Application for MSME Product Sa...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Erinda, Muhammad Fandi Warnars, Harco Leslie Hendric Spits Sumardin, Winters Arianto, Jimmy Vinarycha, Devi Wahyu Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara Univesity Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

Many MSMEs manually close their businesses in today's competition, especially those caused by the COVID19 pandemic, and besides that, the lack of technology implementation is even more aggravating. Like it or not, Micro, Small, and Medium Enterprises (MSMEs) must equip their businesses with the application of technology, and unfortunately, there are still many MSMEs that have not been able to take advantage of the benefits of technological developments. Many companies see this condition as an opportunity;for example, Gojek and Grab partner with MSME players to make it easier to trade their products or services. In this paper, a smartphone application for MSMEs called 'Dagang Yuk Yuk, ' which consists of activities such as Login, Create Order, Input Delivery Location, Select Courier, and Select Payment Method has been proposed. Dagang Yuk Yuk tries to give different ideas for implementing MSME actors where this application is only for MSME players as part of an MSME business unit. This paper discusses the application's design using use case diagrams to describe the business process, while class diagrams model the database design. This application was developed using Arduino studio and MySQL database for database storage. © 2022 IEEE.

关键词： Competition

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Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

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Intelligent Automation & Soft Computing 2023年第8期37卷 2117-2132页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Amel Ali Alhussan Marwa M.Eid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansouraEgypt

The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and ***,the accurate prediction can be realized using the recent advances in machine learning and predictive *** research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTM)*** proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting *** optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic *** prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTM-based model,and the results are compared with that of the proposed *** results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013,which is the best among the recorded results of the other *** addition,statistical experiments are conducted to prove the statistical difference of the proposed *** results of these tests confirmed the expected outcomes.

关键词： Stochastic fractal search dipper throated optimization energy consumption long short-term memory prediction models

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An Efficient Technique to Prevent Data Misuse with Matrix Cipher Encryption Algorithms

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computers, Materials & Continua 2023年第2期74卷 4059-4079页

作者： Muhammad Nadeem Ali Arshad Saman Riaz Syeda Wajiha Zahra Ashit Kumar Dutta Moteeb Al Moteri Sultan Almotairi Department of Computing Abasyn UniversityIslamabadPakistan Department of Computer Science National University of TechnologyIslamabadPakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Department of Management Information Systems Business Administration CollegeKing Saud UniversityRiyadh11451Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Many symmetric and asymmetric encryption algorithms have been developed in cloud computing to transmit data in a secure *** cryptography is a data encryption mechanism that consists of different steps and prevents the attacker from misusing the *** paper has developed an efficient algorithm to protect the data from invaders and secure the data from *** this algorithm is applied to the cloud network,the attacker will not be able to access the *** encrypt the data,the values of the bytes have been obtained by converting the plain text to ASCII.A key has been generated using the Non-Deterministic Bit Generator(NRBG)mechanism,and the key is XNORed with plain text bits,and then Bit toggling has been *** that,an efficient matrix cipher encryption algorithm has been developed,and this algorithm has been applied to this *** capability of this algorithm is that with its help,a key has been obtained from the plain text,and only by using this key can the data be decrypted in the first steps.A plain text key will never be used for another plain *** data has been secured by implementing different mechanisms in both stages,and after that,a ciphertext has been *** the end of the article,the latest technique will be compared with different *** will be a discussion on how the present technique is better than all the other techniques;then,the conclusion will be drawn based on comparative analysis.

关键词： Symmetric cryptography ciphertext encryption decryption cloud security matrix cipher

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A radius-incorporated localized multiple kernel learning algorithm for detecting depression in speech

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International Journal of Speech technology 2023年第2期26卷 371-378页

作者： Jiang, Haihua Hu, Bin Liu, Zhenyu Wang, Gang Zhang, Lan School of Computer and Information Engineering Zhejiang Gongshang University Hangzhou China Faculty of Information Technology Beijing University of Technology Beijing China School of Information Science and Engineering Lanzhou University Lanzhou China Beijing Anding Hospital of Capital Medical University Beijing China Lanzhou University Second Hospital Lanzhou China

Early intervention for depression could provide a means to reducing the disease burden, but there is a lack of objective diagnostic methods. This study investigated automatic depression classification on a speech dataset of 85 healthy controls (51 females and 34 males) and 85 depressed patients (53 females and 32 males). Considering that there are obvious differences in the performance of different types of speech features, we propose a radius-incorporated localized multiple kernel learning (trLMKL) algorithm for detecting depression in speech to make the best use of speech features. To improve the classification accuracy, we combine the information of both the margin and the radius of the MEB to learn the gating model parameters in our algorithm. Furthermore, we do not directly incorporate the radius of the MEB, but incorporate the trace of the total scattering matrix of training data. This method can avoid the time cost of calculating the radius at each iteration and decrease the computational complexity. Comprehensive experiments were carried out on our depressed speech dataset and 10 UCI datasets. Our algorithm achieved better classification performance overall than SimpleMKL and LMKL, and it was efficient at detecting depression, indicating its potential for use as a diagnostic method for depression. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Iterative methods

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