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检索条件"任意字段=International Conference on Advances in Information Communication Technology and Computing"

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Artificial Intelligence in Financial Derivatives: A Comparative Analysis of PSO and ACO for American-Style Put Options 5

Artificial Intelligence in Financial Derivatives: A Comparat...

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Akter, Afroza Parvin, Salma Hossain, A. B. M. Shahadat Islam, Md Morshedul Bangladesh University of Engineering and Technology Department of Mathematics Dhaka Bangladesh University of Dhaka Department of Applied Mathematics Dhaka Bangladesh Concordia University of Edmonton EdmontonAB Canada

ISBN: (纸本)9798350356236

Artificial Intelligence (AI) enhances option pricing accuracy and risk management, enabling precise valuation of complex derivatives across various market scenarios. This paper compares Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in valuing American-style put options. We apply these metaheuristic algorithms to tackle the complexity of option pricing due to the early exercise feature, evaluating their performance across various boundary functions. We assess each method's accuracy and computational efficiency through extensive simulations. These simulations are benchmarked against findings from existing literature. Python's Computer Algebra System (CAS) is used for the simulations. The findings highlight AI's potential to enhance option valuation and contribute valuable insights for practitioners in finance. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Resource allocation and task scheduling for computing power aware networks

Resource allocation and task scheduling for computing power ...

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2024 international conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Peng, Guozheng Zhao, Chuanqi Chen, Lingying Wang, Lanruo Beijing100089 China State Grid Shandong Electric Power Research Institute Ji’nan250003 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

As an important cornerstone of future information and communication technology, the computing power network cleverly integrates cloud resources, third-party computing devices, and various heterogeneous computing resources scattered at the edge of the network through seamless fusion of widely covered network connections. This innovative architecture aims to break the boundaries of traditional computing models and achieve precise scheduling and global optimization of computing resources by building a highly collaborative computing network resource management system. This article proposes a resource allocation and task scheduling algorithm based on deep reinforcement learning (DRL), which simulates the human decision-making process and enables the system to autonomously learn and optimize strategies in a constantly changing computing network environment. This algorithm can not only effectively cope with large-scale and high dynamic computing power demands, but also accurately predict future resource trends, pre allocate resources in advance, and ensure smooth and efficient task execution. The experimental results show that the algorithm performs well in improving the utilization of computing power resources, reducing task execution latency, enhancing system stability and flexibility, and laying a solid foundation for the further development and application promotion of computing power networks. © 2025 SPIE.

关键词： computing power

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Multi-Camera Trajectory Based Vehicle Re-Identification for Robust Traffic Monitoring 5

Multi-Camera Trajectory Based Vehicle Re-Identification for ...

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Athilakshmi, R. Prasanna, Emandi Venkata Sanjeevi Sai Yasir, Syed Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (纸本)9798350356236

Vehicle Re-Identification (ReID) is the challenging task of identifying the same vehicle across multiple cameras in urban environments. In this paper, we propose a novel MultiCam TrajectoryID model that leverages trajectory-based features, addressing the limitations of single-image-based ReID methods. The system combines feature extraction methods like SIFT with the Kalman filter and You Only Look Once (YOLO) algorithms for trajectory prediction to guarantee accurate and effective tracking of objects across several camera views. This MultiCam TrajectoryID approach enhances traditional single-image vehicle ReID by focusing on the continuity of object movement, improving the robustness of identification in complex, real-world traffic environments. We evaluate the performance of our model on the challenging VeRi-776 dataset and compare it with state-of-the-art methods. The proposed model achieves a mean average Precision (maP) of 86% on VeRi-776 data, outperforming recent approaches such as Multi-Branch Enhanced Discriminative Network and Multi-Fine Grained Network models. This significant improvement demonstrates the effectiveness of trajectory-based re-identification in multi-camera setups and its contribution to vehicle ReID and real-time surveillance systems. © 2025 IEEE.

关键词： Vehicle detection

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Improvement of Image Classification Technique Using Transfer Learning 5

Improvement of Image Classification Technique Using Transfer...

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Bhonsle, Devanand Uzma, Ruhi Prasad, Priyanka Bankar, Rushikesh Nayak, Manjushree Massey, Sampada Vishwas Rizvi, Tanu Shri Shanakaracharya Technical Campus Bhilai India Anjuman College of Engineering & Technology Nagpur India Shri Shanakaracharya Institute of Professional Management & Tech Bhilai India G. H. Raisoni Institute of Engineering and Technology Nagpur India Nist University Odisha Berhampur India Shri Shanakaracharya Technical Campus Bhilai India

ISBN: (纸本)9798350356236

Image classification is one of the fundamental task in the digital image processing and computer vision. It is very essential to get high accuracy in it which requires substantial computational resources and a large dataset for the training using deep neural networks. Transfer learning technique is one of the solution which mitigate various challenges by leveraging pre-trained models on large datasets. In this paper;the effectiveness of the transfer technique is exhibited for the purpose of enhancing the image classification. A comprehensive survey has been done of the recent advancement for the purpose to investigate the strategies of transfer learning technique including feature extraction, fine tuning and domain adaption. Experimental results exhibits the efficacy of the transfer learning in the improvement of classification accuracy with less computational complexity. This paper also discusses practical consideration, challenges and future research scope for the utilization of transfer learning for image classification applications. © 2025 IEEE.

关键词： Transfer learning

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Enhancing Energy Efficiency in IoT Networks with AI optimized LEACH Protocol and Digital Twin Simulation 1

Enhancing Energy Efficiency in IoT Networks with AI optimize...

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1st international conference on advances in Computer Science, Electrical, Electronics, and communication Technologies, CE2CT 2025

作者： Harinaiha, Adarsha Chaudhary, Pavan Faculty of Engineering and Technology Department of Mechanical Engineering Ramnagar District Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

The convergence of the Internet of Things (IoT) via artificial intelligence (AI) will undoubtedly revolutionize networking. IoT is a technological paradigm shift facilitating the incorporation of many applications, including smart homes and wearable gadgets, via automation, wireless sensors, and embedded systems. Furthermore, the production of AI enhances this phenomenon by delivering data-driven insights, streamlining procedures, and creating new avenues for development. The extensive implementation of devices in Internet of Things (IoT) networks has raised worries over escalating energy usage. Utilizing environmentally friendly techniques is crucial to maintain network service for nodes that sense with limited power supplies. One approach that substantiates this assertion is LEACH, an acronym for Low Energy Adaptive Clustering Hierarchy. The objective of this form of communication is to optimize the transfer of information to base sites by successfully configuring clusters within networks and executing real-time adjustments across cluster nodes. This work enhances the effectiveness of IoT systems by using a virtual twin simulation to optimize the protocol known as LEACH. Employ artificial intelligence analysis and virtual network models to evaluate power consumption and efficiency. Cache nodes are essential in this architecture since they receive information from the cluster's head & transmit it through the main hub. Utilizing simulation tools and artificial intelligence, we can enhance the reliability and energy efficiency of IoT devices. The results indicate an 84% decrease in inactive nodes & a 1.67-fold rise in node energy use relative to traditional methods. The present study suggests a prospective future trajectory for LEACH-based, AI-augmented, low-power IoT infrastructure. © 2025 IEEE.

关键词： Energy utilization

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Pneumonia Detection in X-Ray Chest Images Based on Convolutional Neural Networks and Data Augmentation Methods 11

Pneumonia Detection in X-Ray Chest Images Based on Convoluti...

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11th international conference on information and communication Technologies for Ageing Well and e-Health, ICT4AWE 2025

作者： Dardouri, Samia Department of Computer Science College of Computing and Information Technology Shaqra University Saudi Arabia InnoV'COM Laboratory-Sup'Com University of Carthage Tunisia

ISBN: (纸本)9789897587436

Pneumonia, a widespread lung ailment, stands as a leading global cause of mortality, particularly affecting vulnerable demographics such as children under five, the elderly, and individuals with underlying health conditions. Accounting for a significant portion of childhood fatalities, at 18%, pneumonia remains a critical health concern. Despite advancements in imaging diagnostic methods, chest radiographs remain pivotal due to their cost-effectiveness and rapid results. The proposed model, trained on data sourced from a readily available Kaggle database, consists of two primary stages: image preprocessing and feature extraction/image classification. Utilizing a CNN model, the framework achieves remarkable performance metrics, with precision, recall, F1-score, and accuracy reaching 93%, 96%, 94%, and 96%, respectively. These results underscore the CNN model's effectiveness in pneumonia detection, showcasing superior consistency and accuracy compared to other pretrained deep learning models. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： Adam Optimizer CNN Deep Learning Early Detection Feature Extraction Image Data Augmentation Pneumonia Infection

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Harnessing Spatial computing for Enhanced Mental Health Monitoring and Support 19

Harnessing Spatial Computing for Enhanced Mental Health Moni...

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19th international conference on Ubiquitous information Management and communication, IMCOM 2025

作者： Oei, Ryan Ray Wantouw Amantha, Theo Justin Cen, Jennifer Marcellyn Sutoyo, Rhio Achmad, Said Andangsari, Esther Widhi Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Psychology Department Faculty of Humanities Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331507817

Millions are fighting to maintain mental well-being in our high-pressure world. Stress, isolation, and mental illnesses can all be relentless foes, and neglecting them can have a domino effect on physical health, leading to a decline in overall well-being. Technological advances, particularly spatial computing, offer promising solutions to this problem. Spatial computing’s main feature is the contextualization of digital information within users’ physical environments. Past studies have shown that this feature can support mental health by enabling remote therapy delivery and providing immersive therapeutic environments anywhere, at any time. While most studies on spatial computing in mental health highlight its benefits, the potential drawbacks and societal integration challenges remain largely unexplored. This work is a systematic literature review aimed at identifying opportunities for integrating spatial computing into mental healthcare, as well as the challenges it could face upon entering society and any potentially negative impacts it may have. The findings demonstrate that spatial computing can improve mental well-being through enhanced accessibility and immersive experiences. However, integration challenges and potentially negative impacts of spatial computing were also concerning, such as high costs and the risk of addiction. Due to the lack of extensive studies on these aspects, the findings on integration challenges and potential negative impacts require further investigation. Future research and real-world applications of spatial computing are necessary to validate this study’s findings and uncover any underlying effects. © 2025 IEEE.

关键词： Electronic health record

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An Improved Nimble Filter for Medical Image Sharpening 5

An Improved Nimble Filter for Medical Image Sharpening

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Eswar, R.B. Vishnukumar, S. Sabu, M.K. Shilen, Neena Cochin University of Science and Technology Department of Computer Applications Kerala Kochi India Sun Rise Hospital Department of Developmental Pediatrics Kerala Kochi India

ISBN: (纸本)9798350356236

Medical image sharpening is crucial for enhancing diagnostic accuracy and visual clarity. This paper proposes an Improved Nimble Filter, an advancement over the standard Nimble Filter, designed to improve the sharpness and detail in medical images. The proposed filter incorporates an adaptive window size, a lower tuning weight that adapts to local image characteristics, and a local Otsu thresholding step to refine the sharpening process. Additionally, the filter applies a Gaussian blur to the local region of interest to reduce noise and preserve edges. These enhancements enable effective edge preservation and noise minimisation, addressing the limitations of existing sharpening techniques. The effectiveness of the Improved Nimble Filter is demonstrated through comparative analysis with other well-known methods, including the Laplacian operator, unsharp mask, shock filter, and generalised unsharp masking, using Brain MRI images from the ABIDE dataset. Experimental results show that the proposed filter significantly outperforms these techniques in terms of visual quality, edge preservation, and processing times, making it a valuable tool for medical image processing applications. © 2025 IEEE.

关键词： Photointerpretation

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Optimized Method for Prediction and Recommendation of Crops Using Fusion Ensemble Learning Crop Recommendation Model 5

Optimized Method for Prediction and Recommendation of Crops ...

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Devi, K. Komala Kumar, Josephine Prem Karanth, Sourav Visvesvaraya Technological University Department of Ise Cmr Institute of Technology Karnataka Belagavi India Visvesvaraya Technological University Department of Cse Cambridge Institute of Technology Karnataka Belagavi India Cmr Institute of Technology Department of Ise Karnataka Bengaluru India

ISBN: (纸本)9798350356236

Fusion Ensemble Learning Crop Recommendation Model introduces an advanced crop recommendation system utilizing machine learning (ML), Robotic Process Automation (RPA) with UiPath, and Explainable AI (XAI). The system aggregates historical and real-time IoT data on weather and soil nutrient content to provide optimized recommendations for crops and NPK (Nitrogen, Phosphorus, and Potassium). By creating an FELCR (Fusion Ensemble Learning Crop Recommendation model) using Random Forest, CatBoost, and XGBoost models, the system achieves robust and accurate crop predictions under various environmental conditions. UiPath automates the collection of this data, streamlining the process and ensuring farmers have timely inputs. To enhance transparency, the model incorporates XAI techniques, specifically LIME (Local Interpretable Model-agnostic Explanations), which allows farmers to understand and trust the recommendations, making the system highly user friendly. The ensemble model demonstrated an accuracy of 99.70%, and its recommendations have been widely adopted by farmers. This study aligns with the United Nations Sustainable Development Goals (SDGs), particularly Goal 2, promoting sustainable and efficient agricultural practices. © 2025 IEEE.

关键词： Robots

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Transforming High-Frequency Trading Predictions with CNN-LSTM Networks 5

Transforming High-Frequency Trading Predictions with CNN-LST...

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5th IEEE international conference on advances in Electrical, computing, communication and Sustainable Technologies, ICAECT 2025

作者： Priya, S. Baghavathi Priyanga, S. Kamisetty, Sri Surya Shobith Neelapala, Venkata Sai Vinay Nithila, G. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Chennai India Vellore Institute of Technology Department of Electronics & Communication Engineering Vellore India Vellore Institute of technology Department of Computer Science & Engineering Vellore India

ISBN: (纸本)9798350356236

High-Frequency Trading (HFT) has transformed financial markets by enabling rapid and automated trading. However, the prediction of critical market metrics, such as trading volumes, remains a challenge due to the noisy and complex nature of financial data. This paper presents a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model designed specifically for predicting trading volumes in the NIFTY50 index using high-resolution, minutelevel Yahoo Finance data. By combining CNN's spatial feature extraction capabilities with LSTM's temporal modeling strengths, the proposed approach effectively captures the intricate patterns and sequential dependencies inherent in high-frequency trading datasets. Comprehensive data preprocessing, including noise reduction and temporal segmentation, coupled with advanced cross-validation techniques, ensures robust performance across market conditions. Comprehensive evaluations using metrics such as Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2) demonstrate the model's efficacy in capturing intricate financial dynamics. This work underscores the potential of hybrid deep learning models in enhancing predictive analytics for high-frequency trading applications in emerging markets. © 2025 IEEE.

关键词： Deep learning

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