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IoT-based Efficient Waste Management on University Campus : Enhancing Monitoring and Optimization through Machine Learning

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Procedia Computer Science 2025年 258卷 4157-4168页

作者： Sumathi M. Hanumanthappa J. K.S. Santhosh Kumar S.P. Shiva Prakash Meghana J. Department of Studies in Computer Science Manasagangothri Campus University of Mysore Mysuru Karnataka 570006 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering Manasagangothri Campus University of Mysore Mysuru Karnataka 570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru Karnataka 570006 India Department of Computer Science Mahajana Post Graduate Center Mysuru Karnataka 570016 India

The introduction of an IoT-based smart waste management system is proposed to optimize various aspects of university campus waste recycle management processes, such as garbage collection scheduling, waste type identification, and collection routes. This system utilizes advanced data analytics algorithms, wireless communication protocols, and sensors to measure and document variations in trash levels, enabling real-time monitoring of waste accumulation. Through this IoT-based infrastructure, university administrators and waste management staff gain fine-grained visibility and control over garbage collection processes. The implications for sustainability advocacy and environmental stewardship in academic environments are significant. By fostering a culture of environmental awareness and accountability, universities can instill ideals of conservation and resourcefulness in their student populations and staff. The proposed IoT-enabled waste bin system exemplifies the synergy between technological innovation and environmental responsibility, serving as a model for institutions worldwide. Collaboration among universities can leverage IoT technology to drive positive change, mitigate environmental degradation, and promote sustainable practices beyond campus boundaries. Efficient waste management is crucial for nurturing sustainability on college and university campuses, as evidenced by the urgent need for the collection and recycling of compost, general waste, and recyclables. Our models consistently achieve high performance, with perfect scores (1.000) across all split ratios, indicating flawless classification of waste types. Although the Artificial Neural Network (ANN) demonstrates slightly lower accuracy, ranging from 0.973 to 0.997 depending on data splits, it still performs well. As a result, Naive Bayes produces less accurate results, typically surpassing 0.93 but falling short of Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)’s flawless

关键词： Artificial Intelligence Smart Parking: Smart city Machine Learning Smart Applications

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Antioxidant system alterations and oxidative stress caused by polyfluoroalkyl substances (PFAS) in exposed biota: a review

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Science of the Total Environment 2025年 977卷 179395页

作者： Soares, Lorena Oliveira Souza de Araujo, Gabriel Farias Gomes, Thais Braga Júnior, Sidney Fernandes Sales Cuprys, Agnieszka Katarzyna Soares, Raquel Moraes Saggioro, Enrico Mendes Laboratory of Environmental Health Assessment and Promotion Oswaldo Cruz Institute Oswaldo Cruz Foundation Av. Brasil 4365 RJ Rio de Janeiro21045-900 Brazil Post-Graduate Program in Public Health and Environment Sergio Arouca National School of Public Health Oswaldo Cruz Foundation Av. Leopoldo Bulhões 1480 RJ Rio de Janeiro21041-210 Brazil Faculty of Science and Technology Norwegian University of Life Sciences P.O. Box 5003 Ås1432 Norway Post-Graduate Program in Environmental Technology and Water Resources Department of Civil and Environmental Engineering – FT University of Brasília Darcy Ribeiro Campus Via L3 Norte DF Brasília70910-900 Brazil

Contamination of aquatic and terrestrial organisms by Perfluoroalkyl substances (PFAS), emerging contaminants, is widespread, as these compounds are present in water, soil, air, and food, owing to their environmental persistence. PFAS exposure induces biochemical process alterations associated with the disruption of the antioxidant defense system in several species. This review aims to discuss how PFAS-induced antioxidant system alterations lead to changes in biochemical processes in different organisms exposed to these pollutants. This disruption, then leads to an imbalance in antioxidant defense systems, contributing to the formation of reactive oxidative species (ROS), which, in turn, can be exacerbate oxidative stress, induce cellular damage, enhance lipid peroxidation, destabilize lysosomal membranes, and cause genotoxic effects, ultimately compromising DNA integrity. In acute tests, PFAS have led to mortality, growth inhibition, diminished behavioral and locomotor abilities, and reproductive impairment. PFAS-induced effects differ with varying species or types of substances, and further bioaccumulation through food chains exacerbates environmental contamination, carrying considerable risks. These findings demonstrate the complex and enduring impact of PFAS on environmental health, emphasizing the importance of this review in corroborating studies on sub-lethal toxicity in exposed organisms and how these effects reflect on the environment. © 2025 Elsevier B.V.

关键词： Biotic

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

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Procedia Computer Science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer Science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Transfer Learning for Keypoint Detection in Low-Resolution Thermal TUG Test Images

arXiv

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arXiv 2025年

作者： Chen, Wei-Lun Hsieh, Chia-Yeh Kao, Yu-Hsiang Liu, Kai-Chun Peng, Sheng-Yu Tsao, Yu Research Center for Information Technology Innovation Academic Sinica Taiwan Graduate Institute of Electrical Engineering National Taiwan University Taiwan Bachelor’s Program in Medical Informatics and Innovative Applications Fu Jen Catholic University Taiwan Department of Electrical Engineering National Taiwan University Taiwan College of Information and Computer Sciences University of Massachusetts AmherstMA01003 United States Department of Electrical Engineering National Taiwan University of Science of Technology Taiwan

This study presents a novel approach to human keypoint detection in low-resolution thermal images using transfer learning techniques. We introduce the first application of the Timed Up and Go (TUG) test in thermal image computer vision, establishing a new paradigm for mobility assessment. Our method leverages a MobileNetV3-Small encoder and a ViTPose decoder, trained using a composite loss function that balances latent representation alignment and heatmap accuracy. The model was evaluated using the Object Keypoint Similarity (OKS) metric from the COCO Keypoint Detection Challenge. The proposed model achieves better performance with AP, AP50, and AP75 scores of 0.861, 0.942, and 0.887 respectively, outperforming traditional supervised learning approaches like Mask R-CNN and ViTPose-Base. Moreover, our model demonstrates superior computational efficiency in terms of parameter count and FLOPS. This research lays a solid foundation for future clinical applications of thermal imaging in mobility assessment and rehabilitation monitoring. Copyright © 2025, The Authors. All rights reserved.

关键词： Thermography (imaging)

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ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicular CAN Bus Networks

ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicula...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Jonathan Rendel William Balte Harrison Kurunathan Hazem Ismail Ali Alexandre Dos Santos Roque Wagner Ourique De Morais Mahdi Fazeli Department for Computing and Electronics for Real-time and Embedded Systems (CERES) School of Information Technology (ITE) Halmstad University Halmstad Sweden CISTER/ISEP Polytechnic Institute of Porto Portugal Graduate Program of Electrical Engineering Federal University of Rio Grande do Sul - UFRGS Porto Alegre Brazil

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Zero-day attacks present a significant security threat to vehicular networks, exploiting vulnerabilities at both software and hardware levels within such systems that remain undiscovered. Mitigating these threats is essential to ensuring the safety and security of vehicular systems. Support Vector Machine (SVM) is a good candidate for anomaly detection of zero-day attacks within vehicular networks because it can handle highdimensional data and effectively distinguish between normal and abnormal patterns in complex and dynamic environments. A trained SVM on the normal operation data of in-vehicular network can identify flag deviations, thus making it effective in the detection of any previously unknown attack patterns, which is a common behaviour of zero-day attacks. In this paper, we introduce an anomaly detection method called “ZeroCAN” which models the behaviour of every single electronic control unit on the network with a separate SVM and a set of high-level features that capture the timing and data payload aspects of CANbus traffic. This approach achieves an anomaly detection rate of over $\mathbf{9 9 \%}$ and a false positive rate below $\mathbf{0. 0 1 \%}$ during normal operation in most cases.

关键词： Support vector machines Telecommunication traffic Feature extraction Data models Timing Security Vehicle dynamics Anomaly detection Standards Payloads

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Adaptive Contextual Caching for Mobile Edge Large Language Model Service

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Liu, Yinqiu Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55%, enabling scalable, low-latency LLM services in resource-constrained edge environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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Reproducing ATT&CK Techniques and Lifecycles to Train Machine Learning Classifier

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IEEE Network 2025年

作者： Yudha, Fietyata Lin, Ying-Dar Lai, Yuan-Cheng Sudyana, Didik Hwang, Ren-Hung EECS International Graduate Program National Yang Ming Chiao Tung University Hsinchu City300 Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu City300 Taiwan The Department of Information Management National Taiwan University of Science and Technology Tapei106 Taiwan The Computer and Network Center National Cheng Kung University Tainan701 Taiwan The College of Artificial Intelligence National Yang Ming Chiao Tung University Tainan711 Taiwan

he MITRE ATT&CK framework categorizes threat actor behaviors into a sequence of techniques called the attack lifecycle. Based on this, Our work introduces a dual-labeled dataset that is accurately labeled with distinct techniques and lifecycles defined by ATT&CK. The dataset offered a more thorough perspective than previous datasets that employed binary or attack classification. It encompassed 17 distinct techniques throughout five lifecycles and was generated through an automated method that guarantees reproducibility and learnability. Reproducibility guarantees the dataset’s consistency, whereas learnability signifies its use in training machine learning models. Our analysis produced a positive result. The dataset achieved a Pearson correlation coefficient of 0.7 for reproducibility. Regarding distinguishing classes, it exhibits an average AUC-ROC score of 0.92 for techniques and 0.82 for lifecycles. The model training yielded an average F1 score of 0.95 for technique classification and 0.9 for lifecycle classification, but only for the traffic dataset. © 1986-2012 IEEE.

关键词： Adversarial machine learning

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IoT based detection of bore-well unclosed holes using automated drone operated cameras in a remote area

IoT based detection of bore-well unclosed holes using automa...

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2020 International E- Conference on Data Analytics, Intelligent Systems and information Security, ICDIIS 2020

作者： Renugadevi, S. Dharshini, M. Gayathri, K. Gopalakrishnan, B. Post Graduate in Software Engineering Department of Information Technology Department of Information Technology Bannari Amman Institute of Technology Sathyamangalam India

In the current era, most of the accidents occur due to the borewells that are left unclosed and many children got trapped in them. Those borewells remain as a hell to many children. These borewells have played endlessly in most of the innocent lives. This process is very difficult to rescue the children away from the borewells. To avoid this critical situation, we must prevent the children from falling in borewells. Here, we came up with an idea to take precaution in order to save many infant lives. The main objective of the system is to fix a Drone camera in a remote area to find the borewells that remain uncovered. After finding the borewells, we have to find whether it is a normal hole or a depth pothole. So to detect the size of a hole, we attached an ultrasonic sensor on the bottom of the drone for identifying the potholes and also to measure the height and depth of a bore well respectively. If the depth and height of the bore well is large, the information will be send to the respected officials to properly close the bore well accordingly. These details will be updated to the cloud for maintaining the information in timeline. Hence, we can save number of lives of children from falling in borewells. © Published under licence by IOP Publishing Ltd.

关键词： Drones

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Pani/MoS₂ composite for an efficient catalyst for photocatalytic reduction and adsorption of chromium Cr (VI)

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Inorganic Chemistry Communications 2025年 178卷

作者： Sharma, Krishna Prasad Shin, Miyeon Awasthi, Ganesh Prasad Gwak, Nayeong Kim, Sung Hoon Yu, Changho Division of Convergence Technology Engineering Jeonbuk National University Jeonbuk State Jeonju 54896 South Korea Department of Energy Storage/Conversion Engineering (BK21 FOUR) Jeonbuk National University Jeonbuk State Jeonju 54896 South Korea Department of Electronics and Information Engineering Korea University Sejong 30019 South Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart City Convergence Technology Korea University Sejong 30019 South Korea

The development and use of a cost-effective, eco-friendly catalyst for the efficient removal of Cr (VI) from wastewater are critical. In response, we have created a Pani/MoS2 composite for adsorption, utilizing a bina... 详细信息

The development and use of a cost-effective, eco-friendly catalyst for the efficient removal of Cr (VI) from wastewater are critical. In response, we have created a Pani/MoS₂ composite for adsorption, utilizing a binary organic–inorganic material produced through a combination of in-situ oxidative polymerization and hydrothermal reactions. This study highlights the significance of using a bifunctional organic–inorganic composite for Cr (VI) removal. The hydrothermally prepared Pani/MoS₂ material serves as a bifunctional catalyst, facilitating both adsorption and photocatalytic reduction. Comprehensive characterization, photocatalysis, and adsorption analyses of the Pani/MoS₂ composite provide valuable insights into its adsorption and reduction mechanisms. The results showed a reduction efficiency of 67.03% after 80 min and a removal efficiency of 73.60% at pH 3 after 24 h. Additionally, Pani/MoS₂ exhibited an adsorption capacity of 29.44 mg/g after 24 h. The hybrid organic–inorganic composite contains multiple adsorption sites, resulting in enhanced adsorption capacity. The adsorption process involves a variety of reactions, including electrostatic interactions, complexation reactions, physical and chemical adsorption, reduction reactions, and photocatalytic reduction, which incorporates simultaneous oxidation and reduction through electron-hole pairs. A study on the stability and the effect of radical scavengers demonstrated the comparable stability of Pani/MoS₂ and the role of radical interference during Cr (VI) adsorption. © 2025 Elsevier B.V.

关键词： Adsorption Composite Molybdenum disulfide Photocatalytic reduction Polyaniline

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An approach to parameter optimizer for music mood classification in machine learning

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AIP Conference Proceedings 2025年第1期3223卷

作者： Nunik Destria Arianti T. Thoyyibah Achmad Udin Zailani Neny Rosmawarni Rinna Rachmatika Achmad Lutfi Fuadi Information System University of Nusaputra Sukabumi Indonesia Information Systems Management Department BINUS Graduate Program-Master of Information System Management Bina Nusantara University Jakarta Indonesia 11480 Information Technology University of Pamulang Banten Indonesia Faculty of Information Technology Veterans National Development University Indonesia

Music is a blend of the human voice and instruments that bring beauty to the listener. Many people like music to create an atmosphere or atmosphere. In the field of computer science, with a lot of data, music is an object that can be studied both through lyrics, audio, biographies and others. This research focuses on the mood of Indonesian music in the 70s and 80s. The data set used consists of text data and audio data. The mood tested consisted of sad, happy and neutral. This area of interest was investigated using the Crisp-DM method derived from business understanding, data understanding, data preparation, modelling, evaluation, deployment. In building the Confusion Matrix, this study uses several parameters, namely epoch, dimensions, set size, learning rate and others. The value generated by the confusion matrix contains a good 85 % value.

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