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Next-Gen Energy Management: IoT Gateway for Smart Monitoring and Appliance Control 6

Next-Gen Energy Management: IoT Gateway for Smart Monitoring...

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6th international conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Kabra, Sohum Nadagouda, Prithvi Nikam, Yash Kavedia, Manoj S. Thadomal Shahani Engineering College Mumbai India Thadomal Shahani Engineering College Mumbai India

ISBN: (纸本)9798331522667

this research study proposes an IoT-based solution for intelligent energy management, designed to monitor and optimize power consumption in both residential and industrial environments. the system will integrate an AI meter for real-time tracking of voltage, current, and power usage, alongside environmental sensors that measure parameters like temperature, humidity, light intensity, and motion. these data inputs are processed by an IoT gateway based on an ESP32 microcontroller and Raspberry pi, which will handle the data locally or transmits it to a cloud-based or edge machine learning model. the machine learning model analyzes real-time sensor data combined with historical consumption patterns to predict and implement optimal appliance control strategies. the system aims to reduce power consumption by up to 20-25%, contributing to energy efficiency without compromising operational performance and delays. Appliances are controlled remotely using IR Blasters and relays, allowing for dynamic, real-time adjustments based on environmental factors. the solution is scalable, making it applicable for both household energy management and large-scale warehouse automation. By utilizing predictive analytics and intelligent control, the system optimizes energy usage, offering a sustainable, automated solution for modern smart homes and industries. this integration of IoT and machine learning provides a significant step toward more efficient and sustainable energy management practices. © 2025 IEEE.

关键词： Gateways (computer networks)

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intelligent Nanorobots for Precision Cancer Diagnosis and Treatment using Deep Reinforcement learning

Intelligent Nanorobots for Precision Cancer Diagnosis and Tr...

引用

11th international conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Sharma, Kapil Ranjan, Ravi Delhi Technological university Dept. Information Technology Delhi India

ISBN: (纸本)9798331509293

Early detection of cancer is essential as it significantly improves treatment outcomes and survival rates by addressing the disease before it spreads to other organs. Many cancers, such as breast, cervical, and colorectal, are highly treatable in their initial stages. With the development of Artificial intelligence, nano technology, the engineers, doctors, and biologist are working on nanorobots for diagnosis and treatment of cancer. It can help detect cancer at an early stage. Early diagnosis often reduces the complexity and cost of treatment, alleviating the physical and financial burdens on patients. Nanorobots are at the forefront of advanced biomedical applications, offering potential breakthroughs in targeted drug delivery and precision diagnostics, particularly for cancer treatment. By integrating artificial intelligence and Deep Reinforcement learning, these devices can autonomously navigate complex biological environments to detect cancer cells via biomarker gradients. this work explores the deployment of Deep Q-learning to improve nanorobot decision-making, enabling efficient path optimization and obstacle navigation. © 2025 IEEE.

关键词： Controlled drug delivery

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Using Contrastive learning to Map Stylistic Similarities in Narrative Writers 25th

Using Contrastive Learning to Map Stylistic Similarities in ...

引用

25th international conference on intelligent data engineering and automated learning

作者： Valero-Redondo, Maria Huertas-Tato, Javier D'Antonio Maceiras, Sergio Martin, Alejandro Camacho, David Univ Cordoba Cordoba Spain Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9783031777301;9783031777318

Exploring how literary authors influence each other and how intertextual connections can be found among them is a complex problem that is handled manually by experts. this research field, intertextuality, seeks to understand how writers relate to each other and how the works of one writer have reflected on the work of another. this analysis usually provides insight into the historical, cultural, biographical, and literary contexts that shape literary works. From a computational point of view, the approaches related to this problem have mainly dealt with problems of style classification, known as authorship attribution, but none have so far dealt with the problem of intertextuality. this paper proposes a novel approach based on contrastive learning to generate authorship embeddings that encapsulate the stylistic signatures of narrative writers and that can be projected into graphical representations to discern similarities between a dataset of literary writers. the embeddings generated with this approach aim to represent authorship styles are created and evaluated using a dataset of books from Project Gutemberg. During the evaluation, we perform a book and chunk-level evaluation, showing good performance in both cases. Finally, we present different graphical representations and provide a deep analysis of the relations that arise between the writers.

关键词： Contrastive learning Intertextual analysis Language Models Transformer

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Time-Aware Contrastive Representation learning for Road Network and Trajectory 11

Time-Aware Contrastive Representation Learning for Road Netw...

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11th international conference on Vehicle Technology and intelligent Transport Systems, VEHITS 2025

作者： Sikder, Ashraful Islam Shanto Nower, Naushin Institute of Information Technology University of Dhaka Dhaka Bangladesh

ISBN: (纸本)9789897587450

Modeling and learning representations for road networks and vehicle trajectories is essential for improving various intelligent Transportation System (ITS) applications. Existing methods often treat road network and trajectory data separately, focus only on one, employ two-step processes that result in information loss and error propagation, or ignore temporal dynamics. To address these limitations, we propose a framework called Time-Aware Contrastive Representation learning for Road Network and Trajectory (TCRLRT). Our approach introduces an end-to-end model that simultaneously learns road network and trajectory representations, enhanced by a temporal encoding module that captures temporal information and a synthesized hard negative sampling module to enhance the discriminative power of the learned representations. We validate the effectiveness of TCRLRT through extensive experiments conducted on two real-world datasets, demonstrating improved performance over baseline methods across multiple downstream tasks. the results highlight the advantages of joint representation learning with temporal modeling and hard negative sampling, leading to robust and versatile representations. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Contrastive learning

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Pseudo Labeling based Consistency learning for Semi-supervised Medical Image Segmentation 24

Pseudo Labeling based Consistency Learning for Semi-supervis...

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11th international conference on Biomedical and Bioinformatics engineering, ICBBE 2024

作者： Cheng, Yintao Zhang, Manli Yang, Rui Kang, Guixia Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798400718274

Semi-supervised medical image segmentation has garnered much attention in recent years due to its potential to significantly alleviate the dependence on expert annotations. In this paper, we present a novel Pseudo Labeling based Consistency learning (PLCL) method, designed to effectively exploit unlabeled data by encouraging low entropy outputs while maintaining the consistency of results in ambiguous regions like border area. In practice, the framework consists of a shared encoder and two different decoders, and we generate soft pseudo labels from the outputs. On this foundation, we adopt a rectified intra entropy loss for the dual decoder branch and design a novel uncertainty-aware scheme to derive the mutual consistency loss in the fuzzy region. the proposed method is evaluated on the left atrium (LA) and the BraTS datasets, with experimental results demonstrating superior performance compared to state-of-the-art semi-supervised methods. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Self-supervised learning

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New Approach to Support the Breast Cancer Diagnosis Process Using Frequent Pattern Growth and Stacking Based on Machine learning Techniques 25th

New Approach to Support the Breast Cancer Diagnosis Process ...

引用

25th international conference on intelligent data engineering and automated learning

作者： Sanmartin, John Azuero, Paulina Hurtado, Remigio Univ Politecn Salesiana Cuenca Ecuador

ISBN: (纸本)9783031777370;9783031777387

Breast cancer is one of the most common types of cancer in women, and its early detection significantly improves the survival rate. Although mammography is one of the least invasive and most widely used methods in the diagnostic process, its complexity and subjectivity in medical interpretation present significant challenges. In this article, we propose a new approach that supports the breast cancer diagnosis process by assisting in the classification of mammography images as malignant or benign, or through the BIRADS system. Our proposal consists of two phases. Initially, we implemented the FP-Growth algorithm on patients' clinical data, analyzing variables such as age and sex to identify frequent patterns. this allows us to explore, group, and visually characterize shared findings and trends among clinical data, which is useful for doctors when creating risk groups or establishing a pre-diagnosis based on the patient's profile. In this phase, we also prepared the images for training the different models. Subsequently, we combined the strengths of two models through stacking: the Random Forest (RF) model and Convolutional Neural Networks (CNN) with knowledge transfer, to improve image classification and diagnosis. We also explored other methods such as CNN and Support Vector Machine (SVM) to compare the accuracy of the proposed methodology against conventional techniques. the developed models were trained using public datasets: "the Chinese Mammography database" [2] and "the INbreast database" [3]. the accuracy of the method is evaluated using various classification-related metrics, such as Accuracy, Precision, F1 Score, and Recall. the results show that combining base models using a stacking strategy achieves significantly superior performance compared to individual models, with ideal scores in accuracy, recall, and F1 score using k-fold cross-validation in the meta-model. these excellent results suggest that combining multiple base models more effectively capture

关键词： Machine learning data Science FP-Growth Random Forest Convolutional Neural Networks Transfer learning Support Vector Machine Medical Image Analysis Breast Cancer Diagnosis

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Multi-modal Machine learning in Gastrointestinal Endoscopy: A Review 24

Multi-modal Machine Learning in Gastrointestinal Endoscopy: ...

引用

11th international conference on Biomedical and Bioinformatics engineering, ICBBE 2024

作者： Chan, In Neng Wong, Pak Kin Yan, Tao Hu, Yanyan Chan, Chon In Department of Electromechanical Engineering University of Macau China Xiangyang Central Hospital Affiliated Hospital of Hubei University of Arts and Science Hubei Xiangyang China Department of Gastroenterology Kiang Wu Hospital China School of Mechanical Engineering Hubei University of Arts and Science. China

ISBN: (纸本)9798400718274

the gastrointestinal (GI) tract plays a crucial role in the human body but is frequently affected by diseases that pose a significant global health issue. GI endoscopy is the primary diagnostic tool for early disease detection. However, its efficacy is limited by human factors such as fatigue and diagnostic variability. the integration of artificial intelligence (AI) in GI endoscopy, particularly through machine learning (ML) and deep learning (DL), offers a promising solution to enhance diagnostic accuracy and efficiency. Many studies have primarily focused on the sole use of endoscopic images, which limits the potential benefits of more comprehensive analysis with unimodal data. With advancements in DL algorithms that can handle complex data modalities, multi-modal machine learning (MMML) has emerged as an innovative approach that leverages multiple data modalities to enhance model performance by combining diverse information sources. Despite its potential, there is no specific review on the application of MMML in GI endoscopy. this paper reviews the applications of MMML in GI endoscopy and highlights its benefits in addressing the complexities of GI endoscopy tasks. Besides, it also explores the potential clinical applications and analyzes the challenges and provides recommendations for further advancements in this emerging field. Copyright © 2024 held by the owner/author(s).

关键词： Adversarial machine learning

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A Comprehensive Survey on intelligent Reflecting Surfaces and Unmanned Aerial Vehicles Integration for Enhanced Communication in Massive Networks 11

A Comprehensive Survey on Intelligent Reflecting Surfaces an...

引用

11th international conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Attar, Hani Ababneh, Jafar Hafez, Mohamed Rachini, Ali Solyman, Ahmed Bahy, Ramy M. Zarqa University Faculty of Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Zarqa University Faculty of Information Technology Cyber Security Department Zarqa Jordan INTI-IU-University Nilai Faculty of Engineering Feqs Malaysia Department of Computer Science and Information Technology Jounieh Lebanon School of Computing Engineering and Built Environment Glasgow Caledonian University Glasgow United Kingdom Technical Research Center Cairo Egypt

ISBN: (纸本)9798331509293

intelligent Reflecting Surfaces (IRSs) offer a revolutionary approach to wireless communication by dynamically controlling the properties of electromagnetic waves. By manipulating wavefronts without requiring radio frequency links, IRSs can enhance spectrum and energy efficiency, reduce costs, and improve overall system performance. Unmanned Aerial Vehicles (UAVs) have garnered significant attention due to their mobility and adaptability. However, real-time data transmission between UAVs can be hindered by obstacles and security risks. By combining IRSs and UAVs, we can create unparalleled opportunities in demanding environments. through coordinated movements and intelligent wave reflection, both systems can optimize wireless signal propagation. this paper provides a comprehensive review of IRS-assisted UAV communications. We explore existing research, introduce novel technologies and applications, and highlight potential research directions for developing low-cost and energy-efficient wireless systems. Additionally, we address current challenges and outline promising areas for future study. © 2025 IEEE.

关键词： Drones

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Detection of Track Geometry Fault Using Car-Body Vibration data and Deep learning Technique 11th

Detection of Track Geometry Fault Using Car-Body Vibration D...

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11th international conference on Traffic and Transportation Studies, ICTTS 2024

作者： Li, Chang Wang, Futian Tang, Yuanjie School of Traffic and Transportation Beijing Jiaotong University Beijing100044 China

ISBN: (纸本)9789819796434

With the rapid growth of railway passenger volume and heightened safety concerns from both operators and passengers, stricter demands have been placed on railway track maintenance and repair work. Current techniques for detecting track geometry faults largely rely on manual inspections or track inspection cars, which are often time-consuming and encounter limited occurrences. To address these shortcomings, this study integrates car-body vibration data with deep learning technology, aiming at enhancing the efficiency and accuracy of railway track maintenance work. By incorporating techniques such as sliding window sampling, data augmentation and attention mechanism, this method effectively resolves issues with imbalanced sample distribution and further enhances the model's classification performance. Experimental results demonstrate that this approach significantly improves the ability of deep learning models to detect and classify track geometry faults using car-body vibration data. the comprehensive application of car-body vibration data and deep learning technology provides an efficient, automated detection method for railway track maintenance, laying a data foundation for implementing preventive maintenance strategies. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Preventive maintenance

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Optimizing Pedestrian Paths to Minimize Exposure to Urban Pollution through Traffic data Analysis 25th

Optimizing Pedestrian Paths to Minimize Exposure to Urban Po...

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25th international conference on intelligent data engineering and automated learning

作者： Nadal, Silvia Jordan, Jaume Sanchez-Anguix, Victor Alberola, Juan M. Julian, Vicente Botti, Vicent Univ Politecn Valencia Valencian Res Inst Artificial Intelligence Cami Vera S-N Valencia 46022 Spain Univ Politecn Valencia Grp Sistemas Optimizac Aplicada Cami Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783031777370;9783031777387

the increase in urban population translates into denser urban traffic and, in turn, more environmental pollution. By taking advantage of the high level of sensorisation in smart cities, pollution levels can be monitored in real time, thus helping citizens to make informed decisions. In this research, we propose a healthy routing system. For this purpose, we estimate the pollution dispersion in the urban area from traffic intensity data. then, the fastest walking route for a given trip is calculated. this route is evaluated with respect to the pollution that a pedestrian would be exposed to by completing it at a certain time. Finally, the system proposes alternative routes to avoid streets with a higher accumulation of harmful particles. the results demonstrate the feasibility of our proposal for the creation of healthy routes and, furthermore, the usefulness of the system as a real-time pollution estimator.

关键词： Smart cities Optimization Traffic data Air pollution

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