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Harnessing Technology for Mangrove Research in the Western Indian Ocean to Enhance Climate Change Resilience 4th

Harnessing Technology for Mangrove Research in the Western I...

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4th International Conference on Society 5.0

作者： Sunkur, Reshma Kantamaneni, Komali Bokhoree, Chandradeo Rathnayake, Upaka Fernando, Michael Univ Technol Mauritius Doctoral Sch La Tour Koenig Mauritius Univ Cent Lancashire United Nations SPIDER UK Reg Support Off Preston Lancs England Univ Cent Lancashire Sch Engn & Comp Preston Lancs England Atlantic Technol Univ Dept Civil Engn & Construct Fac Engn & Design Sligo 91 YW50 Ireland

ISBN: (纸本)9783031714115;9783031714122

Mangroves in the Western Indian Ocean face increasing threats from climate change such as rising sea levels and storm surges. To increase mangrove resilience in the region, there is a need to leverage technological innovations for research, management and monitoring purposes. The aim of this paper was thus to review the role of technology in supporting mangrove research regionally. A bibliometric analysis was conducted using Google Scholar for the period 2004-2024. From 31, 934 articles retrieved, 160 records were selected for the assessment and keyword co-occurrence was conducted on VosViewer. Remote sensing, GIS and modeling techniques were highlighted as key tools for monitoring mangrove health, extent and change over time. Community engagement through participatory strategies such as PGIS was noted as effective mangrove conservation and resilience building efforts. The keyword co-occurrence analysis showed that there was a strong link between mangrove, climate change and sea level rise suggesting the importance of mangroves in coastal resilience. By embracing advancements in technology and fostering collaboration between researchers, local communities and practitioners, coastal nations' resilience to climate change impacts can be enhanced by better managing and monitoring mangroves.

关键词： mangrove coastal management climate change

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Efficient Sparse Tensor Core Networks for Real-Time Insect Classification in Agriculture 4th

Efficient Sparse Tensor Core Networks for Real-Time Insect C...

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

作者： Rao, P. Kiran Prakash, P. Suman Kumar, N. Jaswanth Reddy, V. Kartheek Kumar, Arigela Satheesh Rajeev Gandhi Mem Coll Engn & Technol Dept CSE Data Sci Nandyal Andra Pradesh India G Pullaiah Coll Engn & Technol Autonomus Dept CSE Data Sci Kurnool Andra Pradesh India

ISBN: (纸本)9783031837890;9783031837906

Deep learning has become a powerful tool for various image-based applications, including the detection of agricultural pests. However, the deployment of deep neural networks in resource-constrained agricultural settings poses significant challenges due to their high computational demands. Traditional pruning methods designed to mitigate this challenge often result in sparse networks that may not perform efficiently on hardware optimized for dense operations. In this study, we present a novel approach called Efficient Sparse Tensor Core Networks (STCN), which leverages the inherent sparsity of neural networks to enhance computational efficiency significantly. Our method integrates a specialized sparse network architecture with hardware-accelerated tensor operations, optimized for real-time analysis on edge devices. We have tailored our model to exploit sparse tensor operations, facilitating faster computations and reduced energy consumption without compromising accuracy. When applied to the IP102 dataset, a comprehensive collection of insect pest images, the STCN method proves effective in classifying various pest species under different environmental conditions. Initial experiments show that our approach not only maintains a high classification accuracy of 92% but also achieves a substantial reduction in computational workload, reducing energy consumption by 30% and improving processing speed by 40% during inference. These findings highlight the potential of STCN for real-time pest detection, paving the way for the practical application of advanced AI in precision agriculture, optimizing resource utilization, and promoting sustainable farming practices.

关键词： Sparse Tensor Core Networks (STCN) Real-time Insect Pest Classification Agricultural AI Computational Efficiency Edge Computing Deep Learning Optimization

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Research on the Effectiveness of Wireless Networks Cellular communications Based on Digital Technologies 2nd

Research on the Effectiveness of Wireless Networks Cellular ...

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2nd International Conference on information Technologies and Their Applications

作者： Ibrahimov, Bayram G. Aliyev, Almaz Rafizade, Ulvi R. Azerbaijan Tech Univ Dept Radioelect & Aerosp Syst H Javid Ave 25 AZ-1073 Baku Azerbaijan Mil Def Univ Republ Azerbaijan Baku Azerbaijan Mingechaur State Univ Dept Informat Technol Zahid Khalilov 23 AZ-4015 Mingechaur Azerbaijan Azerbaijan Technol Univ Dept Comp Engn & Telecommunicat Sh I Khatai Ave 103 AZ-2011 Ganja Azerbaijan

ISBN: (纸本)9783031734168;9783031734175

The advantages using the architectural concept of the Network 2030 technology focus group using the recommendations ITU-T, FG NET-2030 (FG NET-2030) for building highly efficient multiservice wireless packet-switched cellular networks when providing a wide range multimedia services are analyzed. A review of the results of the Network 2030 study, carried out by the ITU-T FG NET-2030 focus group to study the capabilities and principles of building fixed communication networks for the period up to 2030 and beyond, is reviewed and analyzed. The efficiency indicators wireless cellular networks based on digital technologies have been studied and performance quality criteria have been selected to ensure acceptable quality of service QoS (QoS, Quality of Service) and Quality of Experience (QoE, Quality of Experience) of useful and service traffic. Based on the research, a new approach has been developed to create a method for calculating performance indicators of multi-service wireless cellular networks using end-to-end digital technologies. A general block diagram model of a digital wireless communication system under the influence of various noise sources is proposed to study the reliability of the transmission and reception discrete messages. Based on the proposed calculation method, complex indicators communication quality such as throughput were studied, taking into account the self-similarity property of heterogeneous traffic, the probabilistic-time characteristics of networks, and the energy consumption hardware and software of mobile wireless networks. As a result of the study, analytical expressions were obtained for estimating the parameters of the output modulated communication channel, predicting efficiency in the time and frequency domains, signal-to-noise ratio per bit, as well as energy consumption indicators in mobile wireless communication networks.

关键词： Multiservice Network Hardware and Software Complex efficiency Probabilistic-Time Characteristics Efficiency Wireless Cellular Communication Networks

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MCDPS: Enhancing Clinical Decision Support for Multiple Chronic Disease Prediction Systems Using Ensemble Machine Learning Approaches 4th

MCDPS: Enhancing Clinical Decision Support for Multiple Chro...

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

作者： Kumar, Kanak Verma, Anshul Verma, Pradeepika Banaras Hindu Univ Dept Comp Sci Varanasi 221005 Uttar Pradesh India Indian Inst Technol Technol Innovat Hub Patna 801103 Bihar India

ISBN: (纸本)9783031821554;9783031821561

People today suffer from various chronic diseases due to lifestyle choices and environmental conditions. Early prediction of these diseases is crucial to prevent them from worsening. However, it is often challenging for physicians to accurately diagnose these issues independently. A computational model based on big data analytics has numerous applications in the medical field. Advancements in machine learning (ML) and information technology have enabled more accurate recognition of diseases, health emergencies, and conditions. The advent of predictive algorithms has transformed disease diagnosis in the medical field. Previously, patient data records were meticulously examined to predict diseases and develop advanced models for trend analysis. However, with the rise of data analytics-driven intelligence, the evaluation of disease symptoms has become more accurate and efficient. These technologies enable the creation of predictive models capable of analyzing vast amounts of patient data to identify hidden patterns, aiding in early disease detection and diagnosis. This study explores the development of an ensemble ML-based multiple chronic disease prediction system (MCDPS) focused on three major diseases: chronic kidney disease (CKD), liver disease, and heart disease. The system utilizes ensemble ML models, including Decision Tree (DT), Gradient Boosting Classifier (GBC), Extreme Gradient Boosting (XGBoost), and Extra-Trees classifiers (ETC), to predict diseases. Its user-friendly and straightforward design makes it accessible to beginners. The system's performance in predicting the three chronic diseases has been evaluated and compared with other traditional models. The results show that the DT algorithm achieved an accuracy of 99%, 85%, and 83% for predicting CKD, heart disease, and liver disease, respectively.

关键词： Multiple Chronic Disease Prediction System Machine Learning Decision Tree Gradient Boosting Classifier Extreme Gradient Boosting Extra-Trees classifiers

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Fake News Detection Using Large Language Models 4th

Fake News Detection Using Large Language Models

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

作者： Ramya, G. R. Yasaswani, S. Veda Harshitha, P. Bapathi, Archana Thanuja, G. Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9783031837920;9783031837937

The spread of fake news, particularly across social media and digital news platforms, poses a growing challenge due to its influence on public opinion and social stability. This study introduces a novel framework for fake news detection, leveraging Large Language Models (LLMs), specifically GPT-4, with multimodal data fusion and an external fact-checking module. Unlike conventional approaches that rely mainly on text, the proposed framework addresses the multimodal nature of misinformation by integrating textual and visual features captured through Convolutional Neural Networks (CNNs) for images to detect misleading content more effectively. Additionally, the framework incorporates real-time fact-checking by cross-referencing statements with trusted sources, such as Wikipedia and ***, reducing false positives and model hallucinations. This approach beats current methods, offering a more thorough solution to the challenging problem of disinformation detection, according to extensive testing on benchmark datasets. Future work will explore multilingual and real-time applications to enhance its adaptability and impact.

关键词： Artificial Intelligence Deep Learning Natural Language Processing Multimodal Fusion GPT-4 Large Language Models

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Early Detection of Liver Fibrosis 1st

Early Detection of Liver Fibrosis

引用

1st MICCAI Meets Africa Workshop

作者： Maas, Cheikh Yakhoub Bousso, Mamadou Allaya, Mouhamad Sall, Ousmane Gaye, Papa Ba Iba Thiam Univ Thies Senegal Iba Thiam Univ Gastro Enterol Dept Dakar Senegal

ISBN: (纸本)9783031791024;9783031791031

Liver cirrhosis, a widely prevalent pathology, is characterized by a insidious progression, often asymptomatic until an advanced stage. Despite medical advances, this condition remains potentially fatal. Early detection of hepatic fibrosis is crucial to improve survival rates. However, current screening methods have significant constraints: ultrasound images, often with insufficient resolution, can complicate interpretation and lead to misdiagnoses;moreover, non-invasive alternatives such as FibroTest or FibroScan, while accurate, are expensive and less accessible. Furthermore, the delays associated with shipping samples abroad for detailed analysis can be detrimental, with speed being a key factor in managing this disease. In this context, the application of artificial intelligence in the diagnosis and detection of liver fibrosis has become indispensable. With this in mind, three new models have been developed: an EfficientNet model designed for precise identification of ultrasound images, a model based on Vision Transformer technology for classifying livers into 'healthy' or 'fibrotic' categories, and an occlusion model to identify important areas on which our vision model relies to classify our images as healthy or fibrotic livers.

关键词： Deep Learning computer Vision Models Images Liver Ultrasound Cirrhosis Early Detection

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Lookahead Secured AI/ML Platforms 8th

Lookahead Secured AI/ML Platforms

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2024 World Congress in computer science, computer Engineering and Applied Computing-CSCE

作者： Jung, Yong-Kyu Gannon Univ Erie PA 16541 USA

ISBN: (纸本)9783031856273;9783031856280

In an era where machine learning permeates critical domains such as finance and healthcare, safeguarding these systems against vulnerabilities is paramount. This paper explores the imperative of securing machine learning systems and advocates for a holistic strategy merging hardware and software co-design. Our proposed framework integrates hardware-level security mechanisms with software-based techniques, presenting a robust approach to fortifying machine learning algorithms and models against potential threats.

关键词： Lookahead Security Artificial intelligence Machine Learning Hardware/Software Co-Reconfiguration Hardware/Software Platform

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Use of Machine Learning Algorithms for Damage Detection on Heritage Buildings Using UAV Pictures 4th

Use of Machine Learning Algorithms for Damage Detection on H...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Toledo, Mauricio J. Tapia, Andres Univ Andres Bello Antonio Varas 8806th Floor Providencia Chile

ISBN: (纸本)9783031832093;9783031832109

The Ministry of Public Works (MOP) is responsible for preserving Chilean heritage buildings, using a documentation process that gathers general information about these structures. This process is often lengthy and typically occurs when the property has already deteriorated. Currently, damages are represented using sketches and drawings, which do not accurately reflect the building's condition. We propose a new method for damage detection using Unmanned Aerial Vehicles (UAVs) to capture images of heritage buildings and Machine Learning algorithms for damage analysis. Our methodology begins with UAVs capturing images, which are used to build an orthomosaic of the building's facade. This orthomosaic is divided into smaller sub-images that are processed by a pre-trained algorithm to identify known damage types. The images with detected damages are then reassembled into a final orthomosaic showing damage information. This method focuses specifically on damage detection, significantly reducing documentation time and improving the accuracy of damage representation. Our case study, Templo Votivo de Maipu, a 96-m-high reinforced concrete heritage building, was chosen due to its prevalent humidity stains. We validated our methodology through a survey of stakeholders, including the Templo management team and professionals from the Architecture Department at MOP. The survey, completed by nine respondents, rated the research an average of 3.9 out of 4 on a Likert scale (1: strongly disagree, 4: strongly agree). The final proposal was refined based on the feedback and recommendations from the validation survey.

关键词： Heritage Building Machine Learning Damage Detection UAV

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Parameter-Efficient Fine-Tuning of Pre-trained Large Language Models for Financial Text Analysis 5th

Parameter-Efficient Fine-Tuning of Pre-trained Large Languag...

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5th Southern African Conference for Artificial Intelligence Research

作者： Langa, Kelly Wang, Hairong Okuboyejo, Olaperi Univ Witwatersrand ZA-2017 Johannesburg South Africa

ISBN: (纸本)9783031782541;9783031782558

Recent advancements in natural language processing (NLP) have been driven by large language models (LLMs) that excel in understanding the complexities of natural language. These models have transformed NLP tasks through transfer learning, where pre-trained LLMs are fine-tuned on domain-specific datasets. Financial sentiment analysis is particularly challenging due to the complexity of financial language, requiring more advanced methods than traditional sentiment analysis approaches. Fine-tuning LLMs can enhance performance in the financial domain, but the high computational cost of standard full fine-tuning is a barrier. This study explores the effectiveness of four (4) parameter-efficient fine-tuning (PEFT) methods, namely, Low-Rank Adaptation (LoRA), prompt tuning, prefix tuning, and adapters, for financial sentiment analysis. The findings show that PEFT methods can match or exceed the performance of full fine-tuning while significantly reducing computational requirements. Specifically, adapting the Open Pre-trained Transformers (OPT) model with LoRA achieved the highest accuracy of 89% using only 0.19% of the model's parameters. PEFT methods also resulted in substantial graphics processing unit (GPU) memory savings of up to 80%. Small-scale fine-tuned LLMs outperformed cutting-edge large-scale general-purpose models like ChatGPT, highlighting the value of domain-specific fine-tuning. LLMs demonstrated superiority over conventional long short-term memory (LSTM) models by achieving a 18% increase in accuracy, thereby validating their higher implementation costs.

关键词： Natural language processing Large language models Parameter efficient fine tuning Transfer learning Financial sentiment analysis

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Optimization of Bucking Rules in the Forestry Industry Considering Taper Functions 4th

Optimization of Bucking Rules in the Forestry Industry Consi...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Mena-Reyes, Jorge Felix Soto-Concha, Raul Diaz-Palma, Matias Linfati, Rodrigo Univ Bio Bio Wood Engn Dept Concepcion Chile Univ Lagos Dept Engn Sci Puerto Montt Chile Univ Bio Bio PhD Engn Concepcion Chile Univ Bio Bio Dept Ind Engn Concepcion Chile

ISBN: (纸本)9783031832062;9783031832079

The optimization of bucking rules during the forestry harvest is essential for increasing efficiency and profitability. Researchers have explored various taper functions to estimate tree diameters at different heights, which helps generate average tree profiles and assess trunk shape evolution. This study addresses optimizing bucking rules using taper functions to model log shape and maximize the yield from harvested trees. Here, we show that the exponential model is more efficient than the polynomial model regarding raw material utilization and computational time. Compared to previous beliefs, the exponential model does not reach the resolution time limit and demonstrates a steady increase in computational times as the number of logs increases. In contrast, the polynomial model frequently reaches the time limit and shows significant variations in computational times. These findings suggest that the exponential model offers a higher performance in forest resource utilization, which is crucial for the forestry industry's efficiency and cost reduction. Future research could focus on improving and adapting the polynomial model for specific problems, potentially leading to new insights and innovations in forest resource optimization.

关键词： Exponential model Polynomial model Log cutting optimization Cutting Stock Problem Mathematical programming

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