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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Sheikh, Nishad Kumar, Praveen Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Sawangi Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of computer science and medical engineering Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331505745

Traditional waste management techniques frequently be afflicted by inefficiencies, high operational fees, and environmental drawbacks. However, latest improvements in Artificial Intelligence, such as computer vision and Internet of Things, are remodeling waste management right into an automated and optimized procedure. This research study explores how AI-pushed technology enhance waste series, sorting, and recycling methods. Machine learning algorithms enhance waste category accuracy, computer vision permits unique fabric separation, and IoT-powered smart containers optimize collection schedules, decreasing needless trips and gasoline consumption. Additionally, AI-pushed direction optimization algorithms Significantly reduce emissions by means of designing efficient series pathways. These advancements Contribute. Not only to improve operational efficiency but also to the broader dreams of decreasing environmental impact and promoting a Circular economy. Despite its transformative potential, AI integration in waste control affords monetary, ethical, and technological challenges that require cautious attention. This examine provides a comprehensive evaluation of AI packages, their blessings, boundaries, and destiny capacity in revolutionizing waste management. With AI solutions, the enterprise can transition towards a more adaptive, efficient, and sustainable waste management ecosystem, addressing both urban and environmental demanding situations in the process. © 2025 IEEE.

关键词： Waste treatment

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Blockchain-Based Trust Model for Inter-Domain Routing

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computers, Materials & Continua 2025年第3期82卷 4821-4839页

作者： Qiong Yang Li Ma Sami Ullah Shanshan Tu Hisham Alasmary Muhammad Waqas Faculty of Information Technology Beijing University of TechnologyBeijing100124China School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Computer Science Shaheed Benazir Bhutto UniversitySheringal18050Pakistan Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia School of Computing andMathematical Sciences Faculty of Engineering and ScienceUniversity of GreenwichLondonSE109LSUK School of Engineering Edith Cowan UniversityPerthWA 6027Australia

Border Gateway Protocol(BGP),as the standard inter-domain routing protocol,is a distance-vector dynamic routing protocol used for exchanging routing information between distributed Autonomous Systems(AS).BGP nodes,communicating in a distributed dynamic environment,face several security challenges,with trust being one of the most important issues in inter-domain *** research,which performs trust evaluation when exchanging routing information to suppress malicious routing behavior,cannot meet the scalability requirements of BGP *** this paper,we propose a blockchain-based trust model for inter-domain *** model achieves scalability by allowing the master node of an AS alliance to transmit the trust evaluation data of its member nodes to the *** BGP nodes can expedite the trust evaluation process by accessing a global view of other BGP nodes through the master node of their respective *** incorporate security service evaluation before direct evaluation and indirect recommendations to assess the security services that BGP nodes provide for themselves and prioritize to guarantee their security of routing *** forward the trust evaluation for neighbor discovery and prioritize the nodes with high trust as neighbor nodes to reduce the malicious exchange routing *** use simulation software to simulate a real BGP environments and employ a comparative experimental research approach to demonstrate the performance evaluation of our trust *** with the classical trust model,our trust model not only saves more storage overhead,but also provides higher security,especially reducing the impact of collusion attacks.

关键词： Inter-domain routing BGP security blockchain trust model trust mechanisms trust evaluation

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Empowering SMEs in the Fourth Industrial Revolution: A Framework for Maintenance 4.0 Adoption 6

Empowering SMEs in the Fourth Industrial Revolution: A Frame...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Nasirinejad, Majid Afshari, Hamid Sampalli, Srinivas Faculty of Computer Science Dalhousie University Halifax Canada Department of Industrial Engineering Dalhousie University Halifax Canada

SMEs play a vital role in driving economic growth, creating jobs, and fostering innovation. However, unlike larger businesses, SMEs often struggle to adopt Industry 4.0 technologies due to limited resources. Addressing these challenges is essential to help SMEs leverage advanced technologies, enhance competitiveness, and support economic development. This paper presents a framework for SMEs to adopt Industry 4.0 technologies in maintenance operations. The framework leverages Reliability, Availability, Maintainability, Safety, and Sustainability (RAMS2) benefits and offers optimization opportunities to enhance production efficiency, reduce costs, and improve product quality. Based on the comprehensive literature review the gaps are identified and technical components of the proposed framework are matched with RAMS2 objectives. A case study illustrates its practical application, including a mathematical model to balance cost and reliability in maintenance. The proposed framework, compared to traditional systems, provides SMEs with a competitive edge by achieving operational and financial objectives. © 2025 Elsevier B.V.. All rights reserved.

关键词： Scheduled maintenance

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MetaCon: Revitalizing Internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Bai, He Li, Hui Que, Jianming Zhang, Minglong Chong, Peter Han Joo Sudheera, Kalupahana Liyanage Kushan Pei, Xinyuan School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville United States School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (纸本)9798350368741

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of Internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive Internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions. © 2025 IEEE.

关键词： communication network congestion control meta-learning reinforcement learning transport protocol

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USING BLACK-SUN EFFECT TO IMPROVE UNSCENTED KALMAN FILTERING OF TRISAT-R SATELLITE ATTITUDE PREDICITON

USING BLACK-SUN EFFECT TO IMPROVE UNSCENTED KALMAN FILTERING...

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2024 Small Satellites Systems and Services Symposium, 4S 2024

作者： Kosanič, Nejc Kramberger, Iztok University of Maribor Faculty of Electrical Engineering and Computer Science Koroška cesta 46 Maribor2000 Slovenia

ISBN: (纸本)9781510688940

Due to the nature of monetary and spatial constrictions, larger systems on small satellites are getting replaced by smaller but more inaccurate sensors. To improve the satellite orientation estimate, multiple different optical sensors may be used. In the case of TRISAT-R, the satellite is equipped with photodiodes and small cameras (CMOS sensors) in parallel, to establish the position of the Sun and calculate the satellite attitude. Due to the intensity of solar radiation, the "black-Sun" effect can be clearly distinguished: a black circle in a photo of a CMOS sensor. The black circle is smaller than the whole Sun on the photo, which provides a more accurate estimate of Suns position. In this paper we will discuss the use of an Unscented Kalman Filtering, which uses magnetometer, gyro meter and optical sensor data, and then calculates the satellites attitude and error estimation. The accuracy of attitude estimation between photodiodes and CMOS sensor images will be compared and conclusion that CMOS sensors can be used to calibrate photodiodes in orbit will be presented. © 2025 The Authors.

关键词： CMOS integrated circuits

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End-to-end optimized ensemble model for Alzheimer’s disease detection 19

End-to-end optimized ensemble model for Alzheimer’s disease...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Hassan, Abdelrahman Abuhmed, Tamer El-Sappagh, Shaker Faculty of Computer Science and Engineering Galala University Egypt College of Computing and Informatics Sungkyunkwan University Korea Republic of

ISBN: (纸本)9798331507817

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an increasing prevalence among the elderly, making early and accurate diagnosis critical for effective intervention and management. This paper introduces an end-to-end machine learning pipeline optimized for detecting AD using multimodal data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The study proposes an ensemble model that integrates key modalities—demographics, cognitive scores, neuropsychological assessments, and MRI data—via early fusion to enhance AD detection. Various machine learning models, including Logistic Regression, Gradient Boosting, AdaBoost, XGBoost, Support Vector Machine, and Neural Networks, are compared, and feature selection is optimized through Recursive Feature Elimination, ANOVA F-value, and mutual information methods, with mutual information showing the most efficacy. With further hyperparameter tuning, the highest accuracy is achieved at 95.85%, with Gradient Boosting as the top-performing model. Additional experiments assess and evaluate individual modality contributions, underscoring the predictive value of cognitive scores and neuropsychological features. To enhance clinical interpretability, we incorporate model explainability using LIME and SHAP, which identify key features influencing model predictions across different AD stages. Explainable AI (XAI) is essential in healthcare for providing transparency and supporting clinician confidence by highlighting which biomarkers, such as MMSE, CDRSB, and PTAU, contribute most to diagnostic outcomes. This study demonstrates classical machine learning's potential in AD diagnostics, offering a pathway to cost-effective and clinically viable models for neurodegenerative disease management. © 2025 IEEE.

关键词： Adversarial machine learning

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Multi-Stage-Based Siamese Neural Network for Seal Image Recognition

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computer Modeling in engineering & sciences 2025年第1期142卷 405-423页

作者： Jianfeng Lu Xiangye Huang Caijin Li Renlin Xin Shanqing Zhang Mahmoud Emam School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou310018China Shangyu Institute of Science and Engineering Hangzhou Dianzi UniversityShaoxing312300China Faculty of Artificial Intelligence Menoufia UniversityShebin El-Koom32511Egypt

Seal authentication is an important task for verifying the authenticity of stamped seals used in various domains to protect legal documents from tampering and *** seal inspection is commonly audited manually to ensure document ***,manual assessment of seal images is tedious and laborintensive due to human errors,inconsistent placement,and completeness of the *** image recognition systems are inadequate enough to identify seal types accurately,necessitating a neural network-based method for seal image ***,neural network-based classification algorithms,such as Residual Networks(ResNet)andVisualGeometryGroup with 16 layers(VGG16)yield suboptimal recognition rates on stamp ***,the fixed training data categories make handling new categories to be a challenging *** paper proposes amulti-stage seal recognition algorithmbased on Siamese network to overcome these ***,the seal image is pre-processed by applying an image rotation correction module based on Histogram of Oriented Gradients(HOG).Secondly,the similarity between input seal image pairs is measured by utilizing a similarity comparison module based on the Siamese ***,we compare the results with the pre-stored standard seal template images in the database to obtain the seal *** evaluate the performance of the proposed method,we further create a new seal image dataset that contains two subsets with 210,000 valid labeled pairs in *** proposed work has a practical significance in industries where automatic seal authentication is essential as in legal,financial,and governmental sectors,where automatic seal recognition can enhance document security and streamline validation ***,the experimental results show that the proposed multi-stage method for seal image recognition outperforms state-of-the-art methods on the two established datasets.

关键词： Seal recognition seal authentication document tampering siamese network spatial transformer network similarity comparison network

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Enhanced Emotion Recognition Using a Hybrid Autoencoder-LSTM Model Optimized with a Hybrid ACO-WOA Algorithm for Hyperparameter Tuning

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International Journal of Advanced computer science and Applications 2025年第4期16卷 861-876页

作者： Waiker, Vinod Ramesh, Janjhyam Venkata Naga Bala, Ms Kiran Krishnaiah, V.V. Jaya Rama Jackulin, T. Muniyandy, Elangovan Shahin, Osama R. Datta Meghe Institute of Management Studies Maharashtra Nagpur India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of CSE Panimalar Engineering College Tamil Nadu Chennai India Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science College of Computer and Information Sciences Jouf University Saudi Arabia Physics and Mathematics Department Faculty of Engineering Helwan University Helwan Egypt

Emotion recognition is vital in the human computer interaction because it improves interaction. Therefore, this paper proposes an improved method for emotion recognition regarding the Hybrid Autoencoder-Long Short-Term Memory (LSTM) model and the newly developed hybrid approach of the Ant Colony Optimization (ACO) and Whale Optimization Algorithm (WOA) for hyperparameters tuning. In this case, Autoencoder can reduce input data dimensionality for input data and find the features relevant for the model’s work. In addition, LSTM is able to work with temporal structures of sequential inputs like speech and videos. The contribution of this research lies in the novel combination method of ACO-WOA which aims at tweaking hyperparameters of Autoencoder-LSTM model. Global aspect of ACO and WOA thereby improve the search efficiency and the accuracy of the proposed emotion recognition system and its generalization capacity. In context with the benchmark dataset for the experimentations of emotion recognition, it has established the efficiency of the proposed model in terms of the conventional methods. Recall rates in recognitive intended various emotions and different modalities were also higher in the hybrid Autoencoder-LSTM model. The optimization algorithms like the ACO-WOA also supported in reducing the computational cost which arose due to hyperparameters tuning. The implementation of this paper is done through Python Software. This implementation shows a high accuracy of 94.12% and 95.94% for audio datasets and image datasets respectively when compared with other deep learning models of Conv LSTM and VGG16. Therefore, the research shows that the presented hybrid approach can be a useful solution for successfully employing emotion recognition for enhancing the creation of the empathetic AI systems and for improving user interactions within various fields including healthcare, entertainment, and customer support. © (2025), (science and Information Organization). All rights

关键词： Ant colony optimization

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A Prediction Model for Tumbler Strength Based on Deep Temporal Clustering Across Multiple Operating Conditions 26

A Prediction Model for Tumbler Strength Based on Deep Tempor...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Hu, Yifeng Chen, Ao Chen, Xiaoxia Wang, Pengqi Cui, Bingbing Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo China

ISBN: (纸本)9798331521950

Iron ore sintering is a critical step in the blast furnace ironmaking process and tumbler strength is a key physical parameter for evaluating sintered ore quality. Accurate prediction of tumbler strength aids in adjusting process parameters to ensure high strength and stability, fulfilling the blast furnace's needs. This paper proposes a tumbler strength prediction model based on deep temporal clustering in multiple work modes. First, work modes are identified using feature space mapping and deep clustering techniques, addressing the limitations of traditional clustering methods in condition identification. Next, a spatio-temporal modeling approach is applied to independently model each work mode, considering the spatio-temporal characteristics of the sintering process. To mitigate the impact of sample size imbalances across work modes, a weighted strategy is introduced, assigning different weights to data from each condition. Experimental results show that the proposed method achieves high prediction accuracy for tumbler strength. © 2025 IEEE.

关键词： Ore sintering

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TinyML Model for Solar Cell Defect Classification Based on Electroluminescence Images 4th

TinyML Model for Solar Cell Defect Classification Based on E...

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4th International Conference on Electronic engineering and Renewable Energy Systems, ICEERE 2024

作者： Boubaker, S. Moussaoui, C. Mellit, Adel Benghanem, M. Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah21959 Saudi Arabia Faculty of Sciences and Technology University of Jijel Jijel18000 Algeria Department of Physics Faculty of Science Islamic University of Madinah Madinah42351 Saudi Arabia

ISBN: (纸本)9789819606436

Being largely utilized as alternative sources of energy, photovoltaic (PV) panels are being deployed in many locations. However, those panels are extensively being prone to faults. A careful detection and diagnosis of those faults becomes a must for solar energy engineers, practitioners and decision-makers in order to extract the maximum of energy from them. The integration of artificial intelligence (AI) in the domain of solar energy management has become a trend in the few previous years because of the high potential provided by machine learning (ML) and deep learning (DL) algorithms in handling issues related to PV systems such as control and fault detection and isolation (FDI). In this paper, TinyML models for defect classification of photovoltaic modules based on electroluminescence (EL) images were developed. An open-source platform, Edge Impulse, was used to design and deploy the models. Two convolutional neural network models (mobileNetV1 and mobileNetV2) have been used and compared. A dataset of EL with three anomalies was used to develop the models. The validation test shows an accuracy of 88.44 and 76.38% for respectively MobileNet-V1 and MobileNet-V2. The quantized (Int8) model has been integrated into a microcontroller (Arduino Portenta H7) for real-time application. The obtained results can be considered as promising and can be extended to other case studies with more defects and larger datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decision making

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