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Intelligent Agent Based Clustering and Optimal Multipath Routing for Energy-Efficient Wireless Sensor Networks in Smart City Applications: A Distributed AI-Driven Approach 5th

Intelligent Agent Based Clustering and Optimal Multipath Rou...

引用

5th International Conference on Computing science, Communication and Security

作者： Patra, Binaya Kumar Mishra, Sarojananda Patra, Sanjay Kumar Mallik, Ashutosh Roy, Souveek Indira Gandhi Inst Technol Dept Comp Sci Engn & Applicat Sarang 759146 Odisha India GIET Univ Dept Comp Sci Engn Gunupur Odisha India

ISBN: (纸本)9783031751691;9783031751707

The study proposes an innovative approach to enhance energy efficiency in Wireless Sensor Networks (WSNs) for smart city applications. The primary focus is on leveraging distributed artificial intelligence (AI) and multipath routing techniques to address challenges such as unequal clustering, poor cluster head selection, and excessive power consumption within WSNs. The approach uses agent-based clustering, where autonomous AI agents dynamically form clusters of sensor nodes based on real-time data characteristics. These clusters are then used for multipath routing, optimizing energy consumption, reliability, and congestion reduction. The distributed nature of AI agents allows for adaptive cluster formations. This algorithm aims to address issues related to uneven clustering, inefficient cluster head selection, and excessive power consumption. Additionally, the integration of agent-based clustering is proposed, involving the deployment of autonomous AI agents that dynamically cluster sensor nodes based on real-time data properties. These AI agents facilitate self-organization and adaptability, ensuring that clusters accurately reflect the evolving data landscape in urban environments. The approach also employs sophisticated energy management strategies at the sensor node level, such as duty cycling, adaptive transmission power control, and sleep-wake scheduling. Simulations in a smart city environment show significant improvements in energy efficiency, prolonging the network's operational lifespan and improving service quality by mitigating data loss and latency issues. This approach contributes to the sustainable development and performance optimization of smart city infrastructure.

关键词： Wireless Sensor Networks Smart Cities Clustering Distributed Artificial Intelligence Multipath Routing Energy Efficiency

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Adaptive Resource Management in Massive Machine-Type communications Using Reinforcement Learning 4th

Adaptive Resource Management in Massive Machine-Type Communi...

引用

4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Boovarahan, N. C. A. Janani, R. Sri Chandrasekharendra Saraswathi Viswa Mahavidya Dept Comp Sci Engn Kanchipuram India Sri Chandrasekharendra Saraswathi Viswa Mahavidya Dept Mech Engn Kanchipuram India

ISBN: (纸本)9783031837951;9783031837968

This work outlines a Reinforcement Learning framework for dynamic resource allocation in Massive Machine-Type communications (mMTC) networks, such as IoT systems. The proposed model optimizes bandwidth and power allocation across numerous devices while meeting constraints like energy efficiency and low latency. The RL agent, representing the base station, learns to allocate resources by interacting with the environment and adjusting its decisions based on factors like channel state information and previous outcomes. The system uses deep reinforcement learning techniques (e.g., Deep Q-Learning) to maximize throughput while minimizing power consumption. Several reward-shaping techniques are introduced to refine the agent's learning, including adaptive penalties and negative reward discounting. This paper also presents performance evaluation metrics, such as average throughput, power efficiency, and fairness, to assess the effectiveness of the model.

关键词： Massive Machine-Type communications Reinforcement Learning Internet of Things Channel State information Deep Reinforcement Learning

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AGC-MAC: A Conceptual Model-Based Platform for the Management and Analysis of Clinical Data of Retina-Macula Diseases 4th

AGC-MAC: A Conceptual Model-Based Platform for the Managemen...

引用

24th International Conference Web Engineering (ICWE)

作者： Carreno-Bolufer, Jesus Reyes Roman, Jose Fabian Cabrera Beyrouti, Ruben Casamayor Rodenas, Juan Carlos Univ Politecn Valencia Valencian Res Inst Artificial Intelligence VRAIN Valencia Spain Hosp Vega Baja Orihuela Ophtalmol Serv Orihuela Spain

ISBN: (纸本)9783031751097;9783031751103

Macular diseases are the main cause of legal blindness worldwide. The macula is a small zone of the retina responsible for central vision. The elderly are the most impacted, though anyone is susceptible. However, the efficacy of the new treatments is still unclear. Clinicians depend on their experience and knowledge to treat the patients with the best medication. Consequently, many resources are wasted, and the patient's vision may get worse. In addition, many of them do not have proper software tools that help them manage and analyze their population of patients. To fight the lack of knowledge, it is essential to get a great amount of data efficiently, which implies using a well-defined information System (IS). With this knowledge, creating profile patients and personalizing treatments would be possible. In this work, we present AGC-MAC, a Conceptual Model-based platform that overcomes these challenges and provides a user-friendly tool for managing and analyzing patients who suffer from retina-macula diseases.

关键词： Macular Diseases Conceptual Modeling AMD information Systems

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Marine Ship Detection Under Fog Conditions Based on an Improved Deep-Learning Approach 5th

Marine Ship Detection Under Fog Conditions Based on an Impro...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Xu, Guokang Yin, Jianchuan Zhang, Zeguo Guangdong Ocean Univ Naval Architecture & Shipping Coll Zhanjiang Peoples R China Sea Marine Ranching Guangdong Prov Key Lab Intelligent Equipment Sout Zhanjiang 524088 Guangdong Peoples R China Guangdong Prov Engn Res Ctr Ship Intelligence & S Zhanjiang 524088 Guangdong Peoples R China

ISBN: (纸本)9789819770007;9789819770014

Target detection plays an essential role in automatic ship identification, ocean border monitoring, and fishing ships supervision, as well as providing environmental perception capability for booming unmanned marine vehicles. However, the maritime setting poses significant challenges, with frequent occurrences of adverse weather conditions like thick fog and dense mist. These harsh environmental factors can significantly impede ship detection efforts, as vessel images may suffer from diminished visual clarity in real-world scenarios. Prior research has primarily relied on image pre-processing methods, such as defogging and low-light enhancement, to enhance image quality before conducting target detection. However, the additional image pre-processing is time-consuming and would hinder us from realizing fast and efficient ship detection, particularly in adverse environmental conditions. To enhance the real-time ship image detection performance under fog conditions, a deformable attention mechanism is proposed to pay attention to relevant locations with more flexibility, which is immune to time-consuming image processing. Firstly, the publicly available new dataset SeaShips was aggregated by using a synthetic central fog algorithm, which can provide realistic visibility conditions for ship detection. Secondly, a deformable attention mechanism was used in the YOLOv8n model to focus on relevant locations more flexibly. Finally, the mechanism was validated based on the new dataset SeaShips_fog. The experimental findings illustrate that the suggested method attains outstanding detection performance even under foggy atmospheric conditions.

关键词： Ship detection fog weather deformable attention mechanism deep learning

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Exploration of Gesture-Based Text Input System for Indic Languages 1

引用

15th Indian Conference on Human-computer Interaction Design and Research

作者： Das, Amrita Natl Inst Design Gandhinagar India

ISBN: (数字)9783031808326

ISBN: (纸本)9783031808319;9783031808326

Many innovative text input methods exist but are scarce in Indian languages. In this paper, we aim to explore a novel text input method for the Malayalam language. This method is gesture-based, where specific hand movements using the natural demarcations of the hand are mapped to particular characters, which enables typing. This is implemented using an image processing program to track key points on our hands, which allows us to distinguish between different gestures accurately. We conducted a study to identify which gestures provide higher accuracy, and least confusion. Using the results, we mapped the gestures to Malayalam and conducted an empirical longitudinal study, which analyzed the speed and accuracy of this prototype. This method yielded a 40+ CPM, which is comparable to similar text input studies in other languages. This technology can be further developed to assist users in various ways, such as providing a communication guide or aiding in daily activities.

关键词： Indic Text Input computer Vision Longitudinal Study

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Convergence of Sharpness-Aware Minimization with Momentum 2nd

Convergence of Sharpness-Aware Minimization with Momentum

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2nd International Conference on information Technologies and Their Applications

作者： Pham Duy Khanh Luong, Hoang-Chau Mordukhovich, Boris S. Dat Ba Tran Truc Vo HCM Univ Educ Dept Math Ho Chi Minh City Vietnam VNU HCM Univ Sci Dept Comp Sci Ho Chi Minh City Vietnam Wayne State Univ Dept Math Detroit MI 48202 USA Wayne State Univ Dept Econ Detroit MI USA

ISBN: (纸本)9783031734199;9783031734205

This paper presents an analysis of Sharpness-Aware Minimization (SAM), a recently introduced efficient optimizer that has demonstrated remarkable improvements in the generalization of deep neural networks. A comprehensive analysis of the asymptotic convergence behaviors of the optimizer when integrated with momentum is proposed. We first show the convergence of the gradient sequence to zero and the topological properties of the set of accumulation points generated by the iterative sequence. Under the assumption of the isolation of stationary points, especially when the function is strongly convex, the convergence of the sequence of iterates is ensured. To validate the practical implications of our analysis, we conduct numerical experiments on classification tasks employing well-known deep learning models, including ResNet18 and ResNet34, with standard datasets CIFAR-10, CIFAR-100, MNIST, and Fashion-MNIST. The numerical results show that, in general, incorporating momentum improves both the training process and testing accuracy for SAM rather than just using standard SGD.

关键词： Deep Learning Gradient-based Optimizer Momentum Acceleration Neural Networks Sharpness-Aware Minimization

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Named Entity Recognition of Belt Conveyor Faults Based on ALBERT-BiLSTM-SAM-CRF 5th

Named Entity Recognition of Belt Conveyor Faults Based on AL...

引用

5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Zhu, Qi Cao, Jingjing Xu, Zhangyi Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan 430072 Peoples R China Wuhan Univ Technol State Key Lab Maritime Technol Wuhan 430072 Peoples R China

ISBN: (纸本)9789819770069;9789819770076

During operation and maintenance process of port belt conveyor, a great deal of fault texts that contain important entity information have been accumulated. However, the fault texts have a few characteristics such as text entity boundary ambiguity, amount of jargons, and being scarce of entity annotation. As a result, the traditional methods exist the problems of inaccurate entity feature information extraction and low recognition efficiency. In this work we firstly construct more than one thousand belt conveyor fault text dataset. Secondly, the paper proposes the method of entity definition of belt conveyor fault domain. In addition, we present the details of the ALBERT-BiLSTM-SAM-CRF. In this model, A Lite Bidirectional Encoder Representations from Transformers (ALBERT) is applied to convert text into word vectors. Secondly, the bidirectional long short-term memory (BiLSTM) encodes the trained word vectors bidirectionally to acquire long sequence semantic features. Then the entity boundary features are captured by Self-Attention Mechanism (SAM). Finally, prediction labels are then generated based on the annotation rules of the Conditional Random Field (CRF) and the sequence decoding ability. The experiment results on the self-constructed Chinese text dataset of belt conveyor faults indicate that our model has a excellent performance in the field of belt conveyor entity recognition. The precision, recall and F1 reaching 81.53%, 81.91% and 81.72%, respectively.

关键词： Belt Conveyor Named Entity Recognition ALBERT BiLSTM Self-Attention Mechanism

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Design and Evaluation of the UI/UX of GAmified LEarning Design Editor (GALEDE) 8th

Design and Evaluation of the UI/UX of GAmified LEarning Desi...

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8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Hammami, Jihed Khemaja, Maha Sierra-Rodriguez, Jose-Luis Univ Sousse PRINCE Res Lab ISITCom Sousse 4011 Tunisia Univ Complutense Madrid Fac Informat Madrid Spain

ISBN: (纸本)9783031838446;9783031838453

This paper is focused on the UI/UX aspects of the graphical learning design authoring tool GAmified LEarning Design Editor (GALEDE). GALEDE is designed to help educators create gamification-based flipped learning scenarios. It is built as a model-driven approach leveraging the capabilities of the Eclipse Sirius platform and Obeo Designer. It addresses the challenge of designing these learning scenarios by offering an intuitive interface that simplifies the design process. The functionalities and features of the tool provide various levels of user guidance and assistance. Moreover, the paper includes an evaluation that involves educators from different domains. Results highlighted its strengths. The latter involves an easy-to-use interface, rich support for gamification elements, and customisation. Improvement areas that lead to upcoming enhancements are also identified. These insights address GALEDE's potential as a valuable tool for designing gamified flipped learning scenarios.

关键词： Gamification Flipped learning GALEDE UI/UX design

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Interpretable Ensemble Learning Model for Enabling an IDS to Detect DNS Attacks 4th

Interpretable Ensemble Learning Model for Enabling an IDS to...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Mahmoud, Loreen Pillai, Sreedev Gangopadhyay, Sugata IIT Roorkee Dept Comp Sci & Engn Roorkee Uttar Pradesh India

ISBN: (纸本)9783031821523;9783031821530

Malicious domains pose a significant threat to internet security, with cyber-criminals exploiting the Domain Name System (DNS) to deceive users and host malicious content. The DNS services are very significant, and hence, the DNS traffic cannot be blocked. This situation is exploited by establishing a covert tunnel for communicating commands by the malicious server, thereby taking over the control of the compromised machine. Traditional methods of detecting malicious domains, such as blacklisting, have limitations in detecting newly generated domains. In this paper, we suggest a DNS intrusion detection system using ensemble learning, where we also use the Local Interpretable Model-agnostic Explanations (LIME) to further understand the predictions of the model. For evaluating the model, the CIC-Bell-DNS2021 dataset was used. To validate the generalizability of the model, we also check it on the UNSW-NB15 dataset. The Experiments show that the proposed method outperforms the state-of-the-art results on two datasets, CIC-Bell-DNS2021 and UNSW-NB15.

关键词： Malicious Domain DNS LIME technique Ensemble learning Intrusion Detection System Bagging Model Classification

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Wire Rope Damage Detection Method Based on Support Vector Machine Wavelet Kernel Function Algorithm 5th

Wire Rope Damage Detection Method Based on Support Vector Ma...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Jin, Zusong Wane, Tao Luo, Zhiheng Shandong Jianzhu Univ Sch Informat & Elect Engn Jinan 250000 Shandong Peoples R China

ISBN: (纸本)9789819770069;9789819770076

In response to the challenges of low detection accuracy and poor stability in traditional wire rope damage detection techniques, we propose a novel approach based on the Support Vector Machine (SVM) wavelet kernel algorithm. This method leverages the similarity between kernel functions and wavelets in decomposition and synthesis processes, integrating kernel functions with wavelet denoising to effectively suppress interference noise. To achieve non-destructive inspection of wire ropes, we designed a magnetic leakage detection device. This device undergoes three analytical processing steps: trend removal of leakage magnetic signals, intervention-based signal denoising, and normalization of feature values for model training. This facilitates the detection, extraction, and identification of leakage magnetic signals. Through comparative analysis using simulated data, our results demonstrate superior detection performance of the proposed fault detection method.

关键词： Wire Rope Inspection Wavelet Kernel Algorithm Magnetic Leakage Detection

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