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Wastewater treatment plant site selection using advanced decision tree machine learning and remote sensing techniques

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Environmental science and Pollution Research 2025年第1期32卷 234-247页

作者： Thangarasu, Thenmozhi Aldehim, Ghadah Alruwais, Nuha Kandasamy, Anguraj Department of Electronics and Communication Engineering Government College of Engineering Tamil Nadu Salem India Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Applied Studies and Community Services King Saud University Saudi Arabia P.O.Box 22459 Riyadh11495 Saudi Arabia Department of Electronics & Communication Engineering Sona College of Technology Tamil Nadu Salem India

Wastewater treatment plants in Coimbatore South are under pressure from rapid urbanization, inadequate infrastructure, and industrial pollution, leading to environmental and public health concerns. This study aimed to identify suitable locations for wastewater treatment plants using a combination of machine learning, remote sensing, and GIS-based multicriteria decision analysis (MCDA). Several datasets were analysed, with the analytical hierarchy process (AHP) assigning weights to factors such as slope (18.51) and elevation (31.39), which were found to be crucial in site selection. The study classified the suitability of sites into five categories, with the western region being the most favourable due to its low elevation (147 to 200 m), gentle slopes (0–3%), and substantial land availability (approximately 309.00 sq. km). Overall, the site suitability analysis revealed that 14.48% (110.2 sq. km) of the area falls within "Very High Favourable Zones," while 11.21% (85.3 sq. km) is categorized as "High Favourable Zones." Moderate and low favourable zones account for 9.63% (73.3 sq. km) and 24.04% (182.9 sq. km), respectively. The remaining 40.57% (308.7 sq. km) is considered "Very Low Favourable Zones." These results can guide urban planning decisions, highlighting the importance of factors such as land availability, population growth, wastewater volume, and flood vulnerability. Integrating GIS technology with decision-making processes enhances the strategic placement of urban utilities, ensuring long-term sustainability for Coimbatore South's wastewater management. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Site selection

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Optimized deep learning models based-sentiment analysis approach for identifying healthcare rumors over social media 2nd

Optimized deep learning models based-sentiment analysis appr...

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2nd International Conference on Next-Generation Computing and information systems, ICNGCIS 2023

作者： Kumar, Pramod Vashishtha, Ela Alghayadh, Faisal Yousef Wasim Bhatt, Mohammed Bala, Suchitra Sreejitha, T.S. Computer Science and Technology Ganga Institute of Technology and Management Haryana Kablana Jhajjar India Lead Analytics Texas Health Resources TX United States Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Model Institute of Engineering and Technology J&K Jammu India ICT & Cognitive Systems Sri Krishna Arts and Science College India St. Marys College Thrissur India

ISBN: (纸本)9781032738659

Research the mechanism of fake news refuting rumors in the real social network environment. Propose methods to evaluate the effect of rumor refuting and explore the factors that affect the impact of rumor refuting. Based on the current research results and assumptions, eight factors that affect the effect of rumor refuting are summed up, such as the proportion of the original fake news content. Experiments were conducted based on the rumor-refuting Weibo data. The experimental analysis showed that the proportion of the original fake news in the rumor-refuting information was negatively correlated with the rumor-refuting effect, and the explanation was positively correlated with the rumor-refuting effect suggestions for refuting rumors, including explaining why the original fake news is wrong to guiding social network fake news denying rumors. © 2025 The Author(s).

关键词： Sentiment analysis

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AI-Based Algorithm for Zero-Day Attack Detection Using Reinforcement Learning

AI-Based Algorithm for Zero-Day Attack Detection Using Reinf...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Ola Nasir Waleed Amer Ala Hamarsheh Ali Mahmoud Ali Amani Abu Zaid Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Secyrity Faculty of Information Technology Zarqa University Zarqa Jordan Department of computer science Faculty of Information Technology Arab American University Department of Computer Network Faculty of Engineering Technology Al-Balqa Applied University Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Zero-Day attacks represent one of the most challenging threats in modern cybersecurity, as they exploit unknown vulnerabilities and bypass traditional detection systems. In this paper, we propose a hybrid threat detection system that integrates three key components: a Random Forest-based supervised classification model for detecting known attack patterns, an autoencoder-based anomaly detection module to uncover novel threats, and a Reinforcement Learning (RL) agent to dynamically optimize response strategies in real-time. The proposed system achieves a detection Accuracy of 97.8% for known attacks. The autoencoder, trained exclusively on normal traffic, employs a dynamically determined threshold (set at 0.8409) to flag anomalous behavior indicative of Zero-Day attacks. Furthermore, the RL agent adapts quickly to evolving threats, effectively filtering out 53.2% of benign traffic while blocking 39.7% of attack traffic and quarantining 7.1% of ambiguous cases for further inspection. Experimental results demonstrate that this integrated approach not only enhances top-level threat prevention but also significantly reduces false alerts.

关键词： Heuristic algorithms Prevention and mitigation Autoencoders Reinforcement learning Threat assessment Real-time systems computer security Anomaly detection Random forests Testing

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Predictions of Gender from the Analysis of the Images of Iris and Ocular Regions

Predictions of Gender from the Analysis of the Images of Iri...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Masoud Husayn Mohamed A.E. Abdalla Dept. of Computer Science Faculty of Information Technology University of Benghazi Benghazi Libya Dept. of Artificial Intelligence and Computer Systems Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

the investigation of biological measurements has become a powerful for classifications. The analysis of eye images for gender classification is proven that it is one of the reliable form identification. This paper examines use of support vector machines and CNN for gender predictions based on analyzing images of the ocular and iris regions for determining which one provides more accurate results. Feature extracted by HOG and LBP methods are examined by the SVM classifier. Whilst, pertained CNN is examined with considering the architecture of AlexNet and ResNet18. The proposed models are then evaluated on the MMU2 dataset which contains 995 images. The results of CNN with AlexNet has shown that the eye region yields an accuracy rate of 94.19% higher than the iris with an accuracy of 90.46%. This indicates the feasibility for applications in security, forensics, marketing, customer service improvement.

关键词： Support vector machines Biometrics Iris Accuracy Image analysis Forensics Machine learning Feature extraction Security Reliability

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Systematic Literature Review of Topic Labeling

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IEEE Access 2025年 13卷 93124-93147页

作者： Salma Mekaoui Ilham Chaker Arsalane Zarghili Nikola S. Nikolov Department of Computer Science and Information Systems University of Limerick Limerick Ireland Laboratory of Intelligent Systems and Applications Faculty of Sciences and Technology Sidi Mohamed Ben Abdellah University Fes Morocco

The rapid growth of textual data on the web has led researchers to develop methods in Natural Language Processing (NLP) to process, understand, and identify topics. Among these methods, Topic Modeling helps extract relevant topics, represented as clusters of words. However, interpreting these clusters into meaningful topics remains a challenge. This limitation has led to further research into topic labeling, an approach for assigning comprehensive and semantically meaningful labels to topic modeling results, ensuring that they are interpretable and understandable from a human perspective. In this paper, we present a Systematic Literature Review (SLR) on topic labeling. This review explores its definition, geographical and time distribution, methodologies, datasets, evaluation methods, successes, and challenges. This paper presents an SLR on topic labeling, synthesizing insights from 41 high-quality studies. It serves as a rich source of information for researchers interested in investigating different approaches for discovering topics within textual data. It addresses the various aspects of topic labeling and includes discussions that highlight the challenges of this approach, encouraging further research in this field.

关键词： Labeling Systematic literature review Search problems Probabilistic logic Computational modeling Databases Data models Context modeling Usability Semantics

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Accurate, Secure, and Efficient Semi-Constrained Navigation Over Encrypted City Maps

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2642-2658页

作者： Li, Meng Chen, Yifei Gao, Jianbo Wu, Jingyu Zhang, Zijian He, Jialing Zhu, Liehuang Conti, Mauro Lin, Xiaodong Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei230002 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230002 China Hefei University of Technology Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei230002 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100811 China University of Science and Technology of China School of Computer Science and Technology Hefei230026 China Beijing Institute of Technology Southeast Institute of Information Technology Fujian Putian351100 China Chongqing University College of Computer Science Chongqing400044 China University of Padua Department of Mathematics HIT Center Padua35131 Italy Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft2628 CD Netherlands University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Navigation services enable users to find the shortest path from a starting point S to a destination D, reducing time, gas, and traffic congestion. Still, navigation users risk the exposure of their sensitive location data. Our motivation arises from how users can accurately, securely, and efficiently navigate from S to Dwhile passing through k unordered stops, i.e., midway locations with a non-fixed visiting order. In this work, we formally define Semi-Constrained Navigation (SCN) and present a novel scheme Hermestoachieveaccurate,secure, andefficient SCN. Specifically, weproposeadivide-and-conquerapproachtostrikeagoodbalance betweenaccuracyandefficiency. It recursively depth-first-searches the whole area (a navigation tree) and invokes five carefully-crafted strategies stop-by-stop to compute three subpaths in three se quential subareas. We construct a path-distance oracle to encrypt the road graph and securely implement the strategies by using homomorphic encryption and garble circuits. We formally prove the security in the random oracle model and analyze the search complexity to be less than O(k2). We experiment over a real-world city map and compare with six baselines. Results show that path search with k = 4 among N = 1000 intersections requires 5.58 seconds with a 3.2% distance deviation rate and an 82.5% path similarity. © 2004-2012 IEEE.

关键词： Traffic congestion

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Detection of Upper Limbs Fractures from X-Ray Images Using Machine Learning Techniques

Detection of Upper Limbs Fractures from X-Ray Images Using M...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Abdalkreem Masaod Mohamed A. E. Abdalla Dept. of Computer Science Faculty of Information Technology University of Benghazi Benghazi Libya Dept. of Artificial Intelligence and Computer Systems Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Bone fractures are common injuries which might affect individuals at any age. The diagnosis should be accurate for proper treatment. Although conventional X-ray imaging is generally practiced, physicians might not be able to diagnose the fractures due several challenges such as infinitesimal fractures that could lead to the misdiagnosis. The misdiagnosis is probably happened especially in the case of the small size of bones. This research is carried out to improve detection of upper limbs fractures. In each upper limbs there are 32 bones. 27 bones are in balms which are quite small pieces. Therefore, an accurate diagnosis is really requires. Automatic diagnosis of bone fractures has already been exploited. However, there are several issues due to the accuracy rate. The proposed framework in this study is an experiment to obtain high level of results. It involves: data gathering, preparation, feature extraction, selection of the best features, and classification. It employs advanced methods such as Gray Level Co-occurrence Matrix, Local Binary Patterns, and deep learning models like VGG16 and DenseNet to improve the diagnosis accuracy. The findings have shown that the SVM classifier yields a rate accuracy of 96%, which outperforms other methods. These findings have been yielded from analyzing the dataset of 8942 samples. This result shows that the proposed framework has outperform other studies.

关键词： Support vector machines Deep learning Limbs Accuracy Medical services Bones Feature extraction X-ray imaging Injuries Computational intelligence

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Leveraging the Machine Learning Techniques for Diagnosing Liver Diseases

Leveraging the Machine Learning Techniques for Diagnosing Li...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Warda A. A. Altawati Lubna A. B. Mohammed Mohamed A. E. Abdalla Dept. of Computer Science Faculty of Information Technology University of Benghazi Benghazi Libya Dept. of Artificial Intelligence and Computer Systems Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Liver diseases are known as one of the deadly diseases around the world. The diagnosis is crucial due to the fact of that the high incidence and death rates. Although, there are commonly used methods for diagnosing liver disorders, they are too expensive. People who are at risk of liver health issues would benefit from the early and low-cost diagnosis for effective treatment. Recently, machine learning plays obvious roles on healthcare to help medical experts to detect illnesses at early onset of the disease. The diagnosis of the liver diseases has been considered in several studies. This paper develops the accuracy rate of the diagnosis of liver diseases with less size of features. Decreasing the size of feature vectors minimizes the time cost of the machine learning model. The paper puts into practice feature selection techniques: Relief, PCA, PSO, and OS to gain its aim. The proposed method of this paper of the diagnosis is applied on two different datasets: ILPD and BUPA. Four different experiments have been carried out for acquiring high level of outcomes. Despite the promised results obtained from all feature selection techniques considered in this study, the selected features using Relief with the random forest classifier (RF) has yielded the best accuracy rates of 91.3% and 98.55% from ILPD and BUPA respectively.

关键词： Accuracy Liver diseases Mortality Feature extraction Vectors Particle swarm optimization Random forests Principal component analysis Diseases Classification tree analysis

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Classification Models and Variable Selection for SME Credit Risk Assessment in Balance the Data Set 10

Classification Models and Variable Selection for SME Credit ...

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10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Intharasompong, Sornthun Jitpattanakul, Anuchit Mekruksavanich, Sakorn Wongwiwat, Piyada Phaphan, Wikanda King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand University of Phayao School of Information and Communication Department of Computer Engineering 56000 Thailand Suansunandha Rajabhat University Faculty of Science and Technology Bangkok10800 Thailand Research Group in Statistical Learning and Inference Thailand

ISBN: (纸本)9798331543273

Small and medium-sized enterprises (SMEs) are frequently considered high credit risk, making it difficult to obtain loans from banks. A viable substitute for assessing borrower creditworthiness more impartially is using machine learning-based credit risk assessment (CRA) algorithms. This study evaluated the performance of five machine learning techniques: decision tree, support vector machine, gradient boosting, K-nearest neighbor, and Naïve Bayes. We evaluated each model with K-fold cross-validation and utilized the Stepwise selection method for the variable selection. To address the underlying data imbalance, we balanced the dataset with the SMOTE technique, which showed that normal loans were more widespread than Non-Performing Loans (NPLs). After balancing the data, the Gradient Boosting model displayed the best accuracy, F-measure, and robustness without variable selection, making it the most effective model for predicting SME loans. © 2025 IEEE.

关键词： Risk assessment

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From User Preferences to Accurate Predictions: Enhancing Movie Recommendation systems with Neural Collaborative Filtering and Sentiment Analysis

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SN computer science 2025年第3期6卷 1-26页

作者： Bsoul, Qusay Zawaideh, Firas Alqadi, Basma S. Almusfar, Latifa Abdullah Khalaf, Osamah Ibrahim Alattas, Ahmed Saleh Alali, Muath AbdElminaam, Diaa Salama Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia College of Business Administration Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam 31441 Saudi Arabia Department of Solar Al-Nahrain Research Center for Renewable Energy Al-Nahrain University Jadriya Baghdad Iraq Information Science Department Faculty of Arts and Humanities King Abdulaziz University Jeddah Saudi Arabia Data Science and Artificial Intelligence Faculty of Information Technology Applied Science Private University Amman Jordan Jadara Research Center Jadara University Irbid 21110 Jordan MEU Research Unit Middle East University Amman 11831 Jordan

This paper introduces an advanced movie recommendation system that combines neural collaborative filtering (NCF) with sentiment analysis to improve personalization, accuracy, and robustness in recommending movies. By integrating user preferences with the sentiment polarity of reviews, the system refines recommendations, effectively addressing challenges such as the cold start problem, data sparsity, and limited semantic understanding. Unlike traditional collaborative and content-based filtering methods, which often struggle with diversity and accuracy, our approach utilizes deep learning within NCF to reveal hidden patterns in user behavior, enhancing recommendations’ relevance and precision. The proposed system is evaluated using standard performance metrics, including RMSE, MSE, and MAE, demonstrating its superior performance over conventional filtering techniques. Its enhanced scalability and adaptability position it as a promising tool for personalized content delivery in digital entertainment, with considerable potential for large-scale, dynamic recommendation environments. This research contributes to the existing knowledge on recommendation systems and offers new insights into improving content personalization and user satisfaction. The novelty and scientific value of this work lie in applying deep learning to tackle the challenges of accurate content recommendation in the rapidly evolving digital media landscape. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative filtering Content-based filtering Cosine and person similarities Deep learning Evaluation metrics MAE Movie recommendation system NCF RMSE Surprise library

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