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An improved methodology for locating and ranking potential hazardous landfill sites at a regional scale using night-time imagery

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GeoJournal 2025年第3期90卷 1-31页

作者： Aladejana, Olabanji Odunayo Jolaiya, Emmanuel Department of Geomatics Engineering and Land Management The University of the West Indies St Augustine Trinidad and Tobago Erasmus GeoTech Masters Department of Computer Languages and Systems Universitat Jaume I (UJI) Castellon Spain

Optimal landfill site selection is critical for sustainable waste management, especially in rapidly urbanizing regions where hazardous waste disposal poses significant environmental and public health risks. This study develops an integrated remote sensing (RS) and GIS-based approach to identify and prioritize regional-scale hazardous landfill sites using multi-criteria decision analysis (MCDA). Combining Landsat 8 OLI, SRTM data, and Thiessen polygons derived from Suomi NPP/VIIRS nighttime light imagery, we apply the fuzzy analytical hierarchy process to evaluate eight key factors: land cover, lineament density, slope, lithology, soil type, road networks, and land surface temperature—spanning environmental, socioeconomic, and infrastructural dimensions. The analysis, covering 25,000 km2 of southwestern Nigeria, reveals 17 highly suitable sites (suitability index: 3.50–3.82) characterized by favorable conditions such as gentle slopes, low lineament density, and robust road networks, which minimize leaching risks and operational costs. Conversely, 7 unsuitable zones (index < 2.50) were flagged due to steep slopes, urban proximity, and poor soil stability, aligning with global evidence on contamination risks. Thiessen polygons delineate service areas for major settlements, with northern regions (e.g., Ijero, Efon, Ado Ekiti) emerging as optimal due to superior infrastructure, while southern areas face constraints from fragmented reserves and weaker accessibility. This study advances landfill site selection by replacing traditional pixel-based outputs with Thiessen polygon-based suitability maps, thereby improving spatial clarity and interpretability for stakeholders. Integrating RS with MCDA, it offers a data-driven framework that supports sustainable waste management and reduces administrative bias.

关键词： Health risks

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Enhancing Retrieval Augmented Generation systems Using AI Models and Graph Databases

Enhancing Retrieval Augmented Generation Systems Using AI Mo...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Souvik Mukherjee Soosan Shabnam Sara Hasan D. Ajitha Department of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Software Systems School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamil Nadu India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

This paper outlines the process of generating a Neo4j graph database powered by Language Models (LLMs). The primary goal is to extract structured information from unstructured data, including user profiles, paper briefs, and Slack messages, and convert them into Cypher queries. The data is then ingested into Neo4j to build a graph database that captures relationships between users, paper, technologies, and messages. A pipeline was developed to automate the process, ensuring accurate entity and relationship extraction using predefined templates. This approach allows for efficient data representation and supports consultancy in managing large datasets by generating insightful visualizations and querying capabilities.

关键词： Ciphers Accuracy Databases Pipelines Retrieval augmented generation Loading Data visualization Transforms Paper technology Data mining

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Security Aspects of Container Orchestration in Kubernetes Environments

Security Aspects of Container Orchestration in Kubernetes En...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Sava Stanišić Milan Vesković Olga Ristić Borislav Đorđević Department of Computer and Software Engineering Faculty of Technical Sciences Čačak Republic of Serbia IMP Computer Systems Mihajlo Pupin Institute Belgrade Republic of Serbia

ISBN: (数字)9798331515799

ISBN: (纸本)9798331515805

Security is one of the key challenges in container orchestration, especially in complex environments. This paper explores the security aspects of implementing containerized applications using Docker within a Kubernetes cluster. The first part of the paper describes Docker, Kubernetes, and various ways of applying them within DevOps methodology. It then presents potential vulnerabilities during the implementation of these technologies, as well as vulnerabilities specific to Docker and Kubernetes. Subsequently, some solutions for securing a Kubernetes environment are described.

关键词： DevOps Scalability Organizations Containers Stability analysis Malware Complexity theory Security Object recognition Resilience

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Variational quantum classifier-based early identification and classification of chronic kidney disease using sparse autoencoder and LASSO shrinkage

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PeerJ computer Science 2025年 11卷

作者： Parthasarathi, P. Alshahrani, Haya Mesfer Venkatachalam, K. Cho, Jaehyuk Department of Computer Science and Engineering Bannari Amman Institute of Technology Erode India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Saudi Arabia Department of Software Engineering Jeonbuk National University Jeonju Korea Republic of Department of Software Engineering Division of Electronics Information Engineering Jeonbuk National University Jeonju Korea Republic of

The two leading causes of chronic kidney disease (CKD) are excessive blood pressure and diabetes. Researchers worldwide utilize the rate of globular filtration and kidney inflammation biomarkers to identify chronic kidney disease that gradually reduces renal function. The mortality rate for CKD is high, and thus, a person with this illness is more likely to pass away at a younger age. Healthcare professionals must diagnose the various illnesses connected to this deadly disease as promptly as possible to lighten the impact of CKD. A quantum machine learning (QML) based technique is presented in this research to help with the early diagnosis and prognosis of CKD. The proposed research comprises four phases: data pre-processing, data augmentation, feature selection, and classification. In the first phase, Kalman filter and data normalization techniques are applied to handle the missing and noisy data. In the second phase, data augmentation uses sparse autoencoders to balance the data for smaller classes. In the third phase, LASSO shrinkage is used to select the significant features in the dataset. Variational Quantum classifiers, a supervised QML technique, are employed in the classification phase to classify chronic kidney diseases. The proposed system has been evaluated on the UCI dataset, which comprises 400 CKD patients in the early stages with 25 attributes. The suggested system was assessed using F1-score, precision, recall, and accuracy as evaluation metrics. With a 99.2% classification accuracy, it was found that this model performed better than the other traditional classifiers used for chronic kidney disease classification. Copyright 2025 Parthasarathi et al. Distributed under Creative Commons CC-BY 4.0

关键词： Blood pressure

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Bluetooth Vulnerabilities and Security

Bluetooth Vulnerabilities and Security

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Mareeswari V Vijayan R Ashish Shukla Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Department of Information Technology School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India Master of Computer Applications School of Computer Science Engineering and Information Systems (SCORE) Vellore Institute of Technology (VIT) Vellore India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Bluetooth technology, which facilitates wireless communication between Billions of devices including smartphones, tablets, laptops, and Internet of Thing (IoT) devices, is a cornerstone of modern connectivity. Its importance lies in its ability to enable seamless data exchange and interaction across a wide range of applications, from personal gadgets to complex industrial systems. Despite its widespread adoption, Bluetooth is not immune to critical security vulnerabilities. Issues like Bluetooth Low Energy (BLE) vulnerabilities and denial-of-service (DoS) attacks can overwhelm devices with excessive traffic, while BLE Forced Connection can lead to unauthorized access, and eavesdropping exposes sensitive data being exchanged. We have discussed these vulnerabilities in detail, examining their mechanisms, potential impacts, and various implementation aspects. Additionally, we have added demonstrations to illustrate how these attacks can be executed and the potential consequences. To prevent these threats, we explored essential measures, including regular firmware updates, secure pairing protocols, and careful management of Bluetooth settings. Adopting best practices like disabling Bluetooth when not in use and monitoring connected devices can further enhance security, ensuring the reliable and safe operation of the vast and growing ecosystem of Bluetooth-enabled devices.

关键词： Wireless communication Ethics Protocols Ecosystems Denial-of-service attack Security Internet of Things Bluetooth Low Energy Microprogramming Guidelines

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Design, Modeling and Simulation of Cybercriminal Personality-Based Cyberattack Campaigns 24

Design, Modeling and Simulation of Cybercriminal Personality...

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Proceedings of the Winter Simulation Conference

作者： Jeongkeun Shin Geoffrey B. Dobson L. Richard Carley Kathleen M. Carley Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Software and Societal Systems Department Carnegie Mellon University Pittsburgh PA USA Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA and Software and Societal Systems Department Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)9798331534202

Cybersecurity challenges are inherently complex, characterized by both advanced technical elements and complex aspects of human cognition. Although extensive research has explored how victims' human factors affect their susceptibility to cyberattacks, the influence of cybercriminals' personalities on the pattern of cyberattack campaigns and the resulting damage to organizations has not received equivalent attention. To bridge this research gap through computer simulation, we introduce a cyberattack campaign designer that enables modelers to construct cyberattack campaigns. This tool allows modelers to define how the process or pattern of the cyberattack campaign can change based on the cybercriminal's personality and simulates how these personality differences influence the magnitude of cyberattack damage on the target organization. In this paper, through computer simulation, we demonstrate how cautious and reckless personalities result in variations in the cyberattack pattern and, consequently, affect the magnitude of cyberattack damage, despite identical cyberattack objectives and techniques used at each step.

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YOLO-RAW: Advancing UAV Detection With Robustness to Adverse Weather Conditions

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IEEE Transactions on Intelligent Transportation systems 2025年第6期26卷 7857-7873页

作者： Munir, Adnan Siddiqui, Abdul Jabbar Hossain, M. Shamim El-Maleh, Aiman Computer Engineering Department Dhahran31261 Saudi Arabia KFUPM SDAIA-KFUPM Joint Research Center for Artificial Intelligence IRC for Intelligent Secure Systems and Computer Engineering Department Dhahran31261 Saudi Arabia King Saud University College of Computer and Information Sciences Research Chair of Pervasive and Mobile Computing Department of Software Engineering Riyadh12372 Saudi Arabia KFUPM Computer Engineering Department Information and Computer Science Department IRC for Intelligent Secure Systems Dhahran31261 Saudi Arabia

With the widespread adoption of unmanned aerial vehicles (UAVs) in various applications (e.g., aerial transportation, traffic monitoring), there have been apprehensions regarding the associated risks of employing UAVs in both civilian and military contexts, including concerns about privacy infringement, safety issues, and security threats. Although several methods have been proposed to detect UAVs, a pressing open challenge is posed by varying adverse weather conditions that could degrade the performance of many existing methods. To address these limitations, this work proposes a YOLO-based (You Only Look Once) novel model, YOLO-RAW that exhibits improved performance in adverse weather conditions and considers different scales of UAVs. Furthermore, to facilitate a more comprehensive evaluation of the proposed model’s effectiveness in UAV detection, we have curated a complex background dataset and introduced three distinct test sets affected by adverse weather conditions. These three test sets comprise the Rainy Test Set (RTS), the AWGN (Additive White Gaussian Noise) Test Set (ATS), and the Motion Blurred Test Set (MBTS). The comprehensive experiments demonstrate the effectiveness of the proposed YOLO-RAW model over its counterparts in detecting UAVs under adverse conditions. © 2000-2011 IEEE.

关键词： Drones

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Predicting Concentration Levels of Air Pollutants by Transfer Learning and Recurrent Neural Network

arXiv

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arXiv 2025年

作者： Fong, Iat Hang Li, Tengyue Fong, Simon Wong, Raymond K. Tallón-Ballesteros, Antonio J. Department of Computer and Information Science University of Macau Taipa China Faculty of Engineering University of New South Wales Sydney Australia Department of Languages and Computer Systems University of Seville Seville Spain

Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short term memory (LSTM) recurrent neural networks (RNNs) have been used to predict the future concentration of air pollutants (APS) in Macau. Additionally, meteorological data and data on the concentration of APS have been utilized. Moreover, in Macau, some air quality monitoring stations (AQMSs) have less observed data in quantity, and, at the same time, some AQMSs recorded less observed data of certain types of APS. Therefore, the transfer learning and pre-trained neural networks have been employed to assist AQMSs with less observed data to build a neural network with high prediction accuracy. The experimental sample covers a period longer than 12-year and includes daily measurements from several APS as well as other more classical meteorological values. Records from five stations, four out of them are AQMSs and the remaining one is an automatic weather station, have been prepared from the aforesaid period and eventually underwent to computational intelligence techniques to build and extract a prediction knowledge-based system. As shown by experimentation, LSTM RNNs initialized with transfer learning methods have higher prediction accuracy;it incurred shorter training time than randomly initialized recurrent neural networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Long short-term memory

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VisioSLR: A Vision Data-Driven Framework for Sign Language Video Recognition and Performance Evaluation on Fine-Tuned YOLO Models

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Procedia computer Science 2025年 257卷 85-92页

作者： Leila Ismail Nada Shahin Henok Tesfaye Alain Hennebelle Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Independent Researcher

With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.

关键词： Artificial intelligence computer vision Deep learning Fine-tuning Machine translation Neural machine translation Sign language Sign language recognition Transfer learning YOLO

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Refractive Index Biosensor–Based Detection of Mycobacterium Tuberculosis Using Sea Lion Political Optimizer and Deep Learning

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Plasmonics 2025年 1-19页

作者： Ragavan, V. K. Senthil Nithya, N.S. Vengala, Anantharamaiah Subramanian, Balambigai Ambhika, C. Sri Shanmugha College of Engineering and Technology Tamil Nadu Sankari Salem (D.T.) 637 304 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of C S E K L E F Andhra Pradesh Vaddeswaram Guntur 522502 India Dept of ECE Karpagam College of Engineering Tamil Nadu Coimbatore India Department of AIML R.M.D Engineering College RSM Nagar Tamil Nadu Kavarapetai India

Mycobacterium tuberculosis (MTB) detection is a major global health concern, requiring accurate and reliable diagnostic tools. According to the World Health Organization (WHO), the global burden of TB is staggering, with approximately 10 million new cases reported annually, leading to a devastating 1.5 million fatalities worldwide every year. The timely detection and treatment of TB are critical components in the fight against this disease, as they play a vital role in preventing its transmission, minimizing the risk of complications and deaths, and enhancing the overall quality of life for affected individuals. Hence, this study presents a novel Sea Lion Political Optimizer_Deep Maxout Network (SLnPO_DMN) for detecting MTB, leveraging a robust processing pipeline to achieve exceptional accuracy. The proposed pipeline involves a series of meticulous steps, including data pre-processing by Min–Max normalization to scale the data to a common range. To enhance model generalizability, data augmentation is carried out by Bootstrap, generating additional training samples by resampling the original data with replacement. The augmented data is then fed into a DMN, optimized by the SLnPO, a hybrid algorithm combining the strengths of sea lion optimization (SLnO) and political optimizer (PO). The SLnPO_DMN model is trained to identify and learn intricate patterns within the data, ultimately attaining optimal performance and demonstrating exceptional capability in detecting Mycobacterium tuberculosis with high accuracy and reliability. The SLnPO_DMN is evaluated using a comprehensive set of metrics, including accuracy, sensitivity, and specificity. The results demonstrate the effectiveness of the SLnPO_DMN model achieving an accuracy (99.12%), sensitivity of (98.74%), and specificity of (98%). © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep maxout network Min–Max normalization Mycobacterium tuberculosis Political optimizer Sea lion optimization

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