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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Samudrala, Sai Santhoshi Haneesha Thambi, Jaishitha Vadluri, Sai Rishisri Rajagopal, Shinu M. Bhaskaran, Sreebha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bangalore India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India

ISBN: (纸本)9798350394474

Fire alarm systems play a vital role in providing early warnings, facilitating prompt evacuation in emergency situations. These systems ensure the security of individuals by alerting them to possible risks and allowing rapid responses to minimize the impact of fires. The research investigates the integration of IoT devices into a Cisco Packet Tracer simulated environment for fire detectionwithin a centralized architecture using a star topology. The significance of an IoT system determines its ability to simulate and analyse diverse IoT device integrations, evaluate various protocols. The System incorporates smoke detection sensors for precise identification of fire events and motion-detecting sensors seamlessly integrated with cameras for motion capture. Performance of different protocols, including BGP, DHCP, EIGRP, HSRP, ICMP, OSPF, RIPis evaluated in terms of response time. The findings contribute valuable insights into optimizing protocol selection for enhanced real-time responsiveness in IoT-based fire alarm systems. © 2024 IEEE.

关键词： Smoke

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Media Power Measuring via Emotional Contagion

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Journal of Social computing 2024年第1期5卷 15-35页

作者： Xue Lin Hong Huang Zongya Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China School of Journalism Huazhong University of Science and TechnologyWuhan 430074China

Media power,the impact that media have on public opinion and perspectives,plays a significant role in maintaining internal stability,exerting external influence,and shaping international dynamics for nations/***,prior research has primarily concentrated on news content and reporting time,resulting in limitations in evaluating media *** more accurately assess media power,we use news content,news reporting time,and news emotion simultaneously to explore the emotional contagion between *** use emotional contagion to measure the mutual influence between media and regard the media with greater impact as having stronger media *** propose a framework called Measuring Media Power via Emotional Contagion(MMPEC)to capture emotional contagion among media,enabling a more accurate assessment of media power at the media and national/regional *** also interprets experimental results through correlation and causality analyses,ensuring *** analyses confirm the higher accuracy of MMPEC compared to other baseline models,as demonstrated in the context of COVID-19-related news,yielding compelling and interesting insights.

关键词： explainability media power emotional contagion

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Leaf Detection: Techniques and Applications

Leaf Detection: Techniques and Applications

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Kumar, Rahul Pal, Arpit Singh, Tejendra Chauhan, Alok Singh School of Computing Science and Engineering Galgotias University Department of Computer Science and Engineering Greater Noida India School of Computing Science & Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350329186

Leaf detection is a critical task in various fields, including agriculture, ecology, and botany, with applications ranging from plant species identification to disease diagnosis. This research paper explores the techniques used for leaf detection, the applications where it is employed, and the challenges faced in this domain. Traditional image processing techniques, machine learning approaches, deep learning-based methods, and hybrid approaches are examined and compared. The applications of leaf detection include agriculture and crop monitoring, plant species identification, disease diagnosis and management, ecological studies, and remote sensing. The challenges in leaf detection arise from the variability in leaf shapes and textures, occlusion and complex backgrounds, realtime processing requirements, and robustness to environmental factors. Future directions and emerging trends in multi-modal leaf detection, domain adaptation, integration of remote sensing technologies, and advanced image analysis techniques are discussed. The findings of this research have implications for further research and practical applications, highlighting the need for more robust and accurate leaf detection techniques and the utilization of advanced technologies. The research contributes to a deeper understanding of plant ecosystems, optimization of agricultural practices, and improved management of natural resources. © 2023 IEEE.

关键词： Remote sensing

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Forecasting Pedestrian Traffic in Melbourne Central Business District: Integrating Sensor Data with COVID-19 Impacts to Inform City Planning 13

Forecasting Pedestrian Traffic in Melbourne Central Business...

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13th International Conference on Control, Automation and Information sciences, ICCAIS 2024

作者： Anh Dang, Nhat Vu, Tung Luong, Stanley Ngoc Dinh, Tan Seyedmahmoudian, Mehdi Chandran, Jaideep Stojcevski, Alex Department of Computing Technologies School of Science Computing and Engineering Technologies Swinburne University of Technology VIC Australia Siemens-Swinburne Energy Transition Hub School of Science Computing and Engineering Technologies Swinburne University of Technology VIC Australia School of Science Engineering and Technology Rmit University Viet Nam Curtin Singapore

ISBN: (纸本)9798331542047

Accurate forecasting of pedestrian counts in the Central Business District of Melbourne during extreme scenarios such as the COVID-19 pandemic is crucial for optimizing resource allocation and ensuring public safety. Current models lack the incorporation of major disruptions, leading to a critical gap in urban traffic forecasting. This study addresses this gap by developing an adaptive forecasting model, namely hyperparam-eter finetuning-convolutional neural network-multivariate-long short-term memory. Using hourly pedestrian counts (2020-2022) and COVID-19 case data, historical trends are integrated with pandemic factors to capture long-term patterns and sudden disruptions. Based on three evaluation metrics (NRMSE, MAPE, and R2), the results demonstrate the accuracy of the model in predicting pedestrian traffic during disruptive events. This research advances context-aware pedestrian traffic predictive modelling, enabling informed decision-making in urban environments. © 2024 IEEE.

关键词： Prediction models

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Spatial data trusts:an emerging governance framework for sharing spatial data

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International Journal of Digital Earth 2023年第1期16卷 1607-1639页

作者： Nenad Radosevic Matt Duckham Mohammad Saiedur Rahaman Serene Ho Katherine Williams Tanzima Hashem Yaguang Tao School of Science RMIT UniversityMelbourneAustralia School of Computing Technologies RMIT UniversityMelbourneAustralia Frontier SI MelbourneAustralia Department of Computer Science and Engineering Bangladesh University of Engineering and TechnologyDhakaBangladesh

Data Trusts are an important emerging approach to enabling the much wider sharing of data from many different sources and for many different purposes,backed by the confidence of clear and unambiguous data *** Trusts combine the technical infrastructure for sharing data with the governance framework of a legal *** concept of a data Trust applied specifically to spatial data offers significant opportunities for new and future applications,addressing some longstanding barriers to data sharing,such as location privacy and data *** paper introduces and explores the concept of a‘spatial data Trust’by identifying and explaining the key functions and characteristics required to underpin a data Trust for spatial *** work identifiesfive key features of spatial data Trusts that demand specific attention and connects these features to a history of relevant work in thefield,including spatial data infrastructures(SDIs),location privacy,and spatial data *** conclusions identify several key strands of research for the future development of this rapidly emerging framework for spatial data sharing.

关键词： Data governance spatial information location privacy indigenous data sovereignty spatial data quality

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Network Intrusion Detection System Using Optuna 5

Network Intrusion Detection System Using Optuna

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Parekh, Nishank Sen, Arzob Rajasekaran, P. Jayaseeli, J. D. Dorathi Robert, P. School of Computing Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Chengalpattu India School of Computing Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331518097

The rapid growth of the digital industry has created a higher demand for robust Network Intrusion Detection Systems (NIDS) to protect valuable information and the integrity of network infrastructures as the digital industry grows rapidly. One of the most important challenges in the current intrusion detection landscape is the growing sophistication of cyber threats, including zero-day attacks, polymorphic malware, and advanced persistent threats, which are difficult to detect using traditional methods. Furthermore, systems often suffer from high false positive rates and struggle to scale effectively in real-time applications. Traditionally, intrusion detection methods were quite effective, but performance is still lacking due to the inability to adapt to evolving threats. Recent breakthroughs include deep learning approaches, ensemble methods, and hybrid detection models. However, these are still plagued by high computational overhead and a lack of transparency in their decision-making processes. The work exploits Optuna for the optimization of hyperparameters, specifically in the performance improvement of various ML models. Among the best-ranked frameworks for the optimization of hyperparameters, Optuna provides a principled method for tuning hyperparameters, resulting in significantly enhanced accuracy and efficiency of the intrusion detection model. The implication of this research work is that it searches for the best configuration of parameters for each algorithm with balanced false positives and detection rates. The study includes an overall scenario of recent development in NIDS. More precisely, this paper shows how Hyperparameter tuning attains very superior model performance compared to other models. The comparative results presented have shown that models which are optimized using Optuna surpass the non-optimized ones by a huge margin with respect to accuracy, recall, precision, and F1-score. The paper also discusses ensemble techniques by integrating the

关键词： Network intrusion

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Sentimental Analysis with Various Classification Techniques—A Comprehensive Survey 5th

Sentimental Analysis with Various Classification Techniques...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Sharma, Rahul Kumar Dagur, Arvind School of Computing Science & Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819771776

Sentiment analysis is a subset of NLP and has encountered an outstanding change with the introduction of new approaches to increase its precision and performance. Neural networks and transformers in deep learning are very widely used techniques in sentiment analysis. This paper suggests the most recent research techniques that are currently used in sentiment analysis and also examines the difficulty and limitations of recently used techniques in sentiment analysis. This paper also provides directions for future studies. The increasing effectiveness of deep learning algorithms in sentiment analysis is an important finding from this survey. To analyze text data and determine the underlying sentiment, neural networks, and transformers have shown extraordinary potential. They are especially helpful in traversing the nuances of human language, which present difficulties for traditional machine learning techniques. Despite these developments, there are still difficulties with reliably identifying irony and sarcasm in sentiment analysis tools. To mitigate these issues, future research should concentrate on devising more robust sentiment analysis strategies capable of accurately interpreting sarcasm and irony. Additionally, efforts should be directed toward developing diverse datasets to ensure sentiment analysis techniques do not favor certain demographics or viewpoints. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Dynamic Offloading and Scheduling Strategy for Telematics Tasks Based on Latency Minimization

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Computers, Materials & Continua 2024年第8期80卷 1809-1829页

作者： Yu Zhou Yun Zhang Guowei Li Hang Yang Wei Zhang Ting Lyu Yueqiang Xu North China Institute of Computing Technology Beijing Beijing100080China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China

In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task offloading is often *** is frequently assumed that vehicles can be accurately modeled during actual motion ***,in vehicular dynamic environments,both the tasks generated by the vehicles and the vehicles’surroundings are constantly changing,making it difficult to achieve real-time modeling for actual dynamic vehicular network *** into account the actual dynamic vehicular scenarios,this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios,attempting to solve the dynamic decision-making problem in task offloading *** optimization objective is to minimize the average task completion time,which is formulated as a multi-constrained non-linear programming *** to the mobility of vehicles,a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and ***,the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient(MADDPG)is applied to solve the optimal solution of the optimization *** results show that the algorithm proposed in this paper is able to achieve lower latency task computation ***,the average task completion time of the proposed algorithm in this paper can be improved by 7.6%compared to the performance of the MADDPG scheme and 51.1%compared to the performance of deep deterministic policy gradient(DDPG).

关键词： Component vehicular dynamic task offloading resource scheduling

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Enhancing pre-trained models for text summarization: a multi-objective genetic algorithm optimization approach

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Multimedia Tools and Applications 2024年 1-17页

作者： Mohan, G. Bharathi Kumar, R. Prasanna Elakkiya, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Birla Institute of Technology and Science Dubai345055 United Arab Emirates

Pre-trained language models have significantly advanced text summarization by leveraging extensive pre-training data to enhance performance. Many cutting-edge models undergo an initial pre-training phase on a large corpus before being fine-tuned specifically for text summarization tasks. The challenge arises when the limited data available for these summarization tasks and the high complexity of the models lead to aggressive fine-tuning, which can cause the models to over fit the training data and fail to generalize well to new, unseen data. To address these challenges, the study proposes a multi-objective based Genetic Algorithm (MOGA) approach for optimizing pre-trained models for summarization The proposed approach first compares the performance of various pre-trained models by fine-tuning them with the CNN/Daily Mail dataset. Secondly, the proposed Multi-Objective Genetic Algorithm (MOGA) is applied to all validated pre-trained models and compared using a statistical approach. Experimental results reveal that Pre-training with Extracted Gap-sentences for Abstractive Summarization Sequence-to-sequence (PEGASUS) model performs best among the pre-trained models. Further optimization with the proposed MOGA method yields even better results, with the optimized model achieving a Recall-Oriented Understudy for Gisting Evaluation) ROUGE-1 score of 0.514, a ROUGE-2 score of 0.4, and a ROUGE-L score of 0.5. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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Denoising Graph Inference Network for Document-Level Relation Extraction

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Big Data Mining and Analytics 2023年第2期6卷 248-262页

作者： Hailin Wang Ke Qin Guiduo Duan Guangchun Luo School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China School of Computing and Artificial Intelligence Southwestern University of Finance and EconomicsChengdu 611130China

Relation Extraction(RE)is to obtain a predefined relation type of two entities mentioned in a piece of text,e.g.,a sentence-level or a document-level *** existing studies suffer from the noise in the text,and necessary pruning is of great *** conventional sentence-level RE task addresses this issue by a denoising method using the shortest dependency path to build a long-range semantic dependency between entity ***,this kind of denoising method is scarce in document-level *** this work,we explicitly model a denoised document-level graph based on linguistic knowledge to capture various long-range semantic dependencies among *** first formalize a Syntactic Dependency Tree forest(SDT-forest)by introducing the syntax and discourse dependency ***,the Steiner tree algorithm extracts a mention-level denoised graph,Steiner Graph(SG),removing linguistically irrelevant words from the *** then devise a slide residual attention to highlight word-level evidence on text and ***,the classification is established on the SG to infer the relations of entity *** conduct extensive experiments on three public *** results evidence that our method is beneficial to establish long-range semantic dependency and can improve the classification performance with longer texts.

关键词： Relation Eextraction(RE) document-level denoising linguistic knowledge attention mechanism

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