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9th International Conference on Intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Liu, Yunxiang Cai, Shixuan Shanghai Institute of Technology School of Computer Information Engineering China

ISBN: (纸本)9798350363043

This review examines the integration of meteorological satellite imagery with artificial intelligence (AI), focusing on traditional machine learning and deep learning methods in weather forecasting. It begins by explaining the basic concepts and importance of satellite imagery in capturing highresolution atmospheric data. The study highlights how AI, especially deep learning, improves the analysis of these complex datasets, enhancing weather pattern identification and anomaly detection. We discuss the integration of satellite data with traditional forecasting methods, recent advancements in hybrid algorithms, and sophisticated data assimilation techniques. The review also covers deep learning models like CNNs, RNNs, and GANs, demonstrating their superior performance in predicting weather phenomena. Challenges such as high computational costs, resource requirements, and model generalizability are addressed, along with future directions in model optimization and hybrid approaches. This synthesis underscores the significant impact of combining satellite imagery with AI on weather forecasting accuracy and reliability. © 2024 IEEE.

关键词： Weather satellites

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Ultra-low power MoS2 optoelectronic synapse with wavelength sensitivity for color target recognition

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Science China(information Sciences) 2025年第4期68卷 187-196页

作者： Bo WEI Yabo CHEN Xiaotong HAN Yan KANG Bujia LIANG Cheng LI Xiaokuo YANG Liang FANG Yuanxi PENG Institute for Quantum Information & State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology Fundamentals Department Air Force Engineering University College of Advanced Interdisciplinary Studies National University of Defense Technology Institute of Quantum Information Science and Technology College of ScienceNational University of Defense Technology College of Computer National University of Defense Technology

Optoelectronic synapses that integrate visual perception and pre-processing hold significant potential for neuromorphic vision systems（NVSs）. However, due to a lack of wavelength sensitivity, existing NVS mainly focuses on gray-scale image processing, making it challenging to recognize color images. Additionally, the high power consumption of optoelectronic synapses, compared to the 10 fJ energy consumption of biological synapses, limits their broader application. To address these challenges, an energy-efficient NVS capable of color target recognition in a noisy environment was developed,utilizing a MoS2optoelectronic synapse with wavelength sensitivity. Benefiting from the distinct photon capture capabilities of 450, 535, and 650 nm light, the optoelectronic synapse exhibits wavelength-dependent synaptic plasticity, including excitatory postsynaptic current（EPSC）, paired-pulse facilitation（PPF）, and long-term plasticity（LTP）. These properties can effectively mimic the visual memory and color discrimination functions of the human vision system. Results demonstrate that the NVS, based on MoS2optoelectronic synapses, can eliminate the color noise at the sensor level, increasing color image recognition accuracy from 50% to 90%. Importantly, the optoelectronic synapse operates at a low voltage spike of0.0005 V, consuming only 0.075 fJ per spike, surpassing the energy efficiency of both existing optoelectronic and biological synapses. This ultra-low power, color-sensitive device eliminates the need for color filters and offers great promise for future deployment in filter-free NVS.

关键词： optoelectronic synapse neuromorphic vision system color recognition 2D materials image pre-processing

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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A Heuristic Task Scheduling Algorithm for Vehicular Fog Computing 8

A Heuristic Task Scheduling Algorithm for Vehicular Fog Comp...

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8th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2024

作者： Taghizadeh, Maryam Ahmadi, Mahmood Razi University Computer Engineering and Information Technology Kermanshah Iran

ISBN: (纸本)9798350374674

Vehicular fog computing (VFC) is a promising solution in addressing various applications in intelligent transportation systems. Computational resources of both parking and moving vehicles can be used for computation-intensive tasks. This paper proposes a straightforward heuristic method in two forms for resource allocation and task scheduling problem in a cloud-fog environment, based on the idea of VFC. First, tasks requesting maximum resources are scheduled to static fog nodes which have more computational resources. Dynamic fog nodes are assigned to tasks while there are no available static fog nodes. Static fog nodes refers to stationary servers, while and dynamic fog nodes refer to parked or moving vehicles, respectively. Moreover, static fog nodes show greater priority in comparison to dynamic fog nodes. This method is evaluated by stochastic data and measures makespan, cost, and wait time. Experimental results show the achievement of the proposed method in reducing both makespan and wait time. © 2024 IEEE.

关键词： Fog

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Intelligent biomedical image classification in a big data architecture using metaheuristic optimization and gradient approximation

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Wireless Networks 2024年第8期30卷 7087-7108页

作者： Almutairi, Laila Abugabah, Ahed Alhumyani, Hesham Mohamed, Ahmed A. Department of Computer Engineering College of Computer and Information Sciences Majmaah University Majmaah11952 Saudi Arabia College of Technological Innovation Zayed University Abu Dhabi Campus Abu Dhabi United Arab Emirates Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Information Technology Faculty of Computer and Information Assiut University Assiut71515 Egypt

Medical imaging has experienced significant development in contemporary medicine and can now record a variety of biomedical pictures from patients to test and analyze the illness and its severity. computer vision and artificial intelligence may outperform human diagnostic ability and uncover hidden information in biomedical images. In healthcare applications, fast prediction and reliability are of the utmost importance parameters to assure the timely detection of disease. The existing systems have poor classification accuracy, and higher computation time and the system complexity is higher. Low-quality images might impact the processing method, leading to subpar results. Furthermore, extensive preprocessing techniques are necessary for achieving accurate outcomes. Image contrast is one of the most essential visual parameters. Insufficient contrast may present many challenges for computer vision techniques. Traditional contrast adjustment techniques may not be adequate for many applications. Occasionally, these technologies create photos that lack crucial information. The primary contribution of this work is designing a Big Data Architecture (BDA) to improve the dependability of medical systems by producing real-time warnings and making precise forecasts about patient health conditions. A BDA-based Bio-Medical Image Classification (BDA-BMIC) system is designed to detect the illness of patients using Metaheuristic Optimization (Genetic Algorithm) and Gradient Approximation to improve the biomedical image classification process. Extensive tests are conducted on publicly accessible datasets to demonstrate that the suggested retrieval and categorization methods are superior to the current methods. The suggested BDA-BMIC system has average detection accuracy of 94.6% and a sensitivity of 97.3% in the simulation analysis. © The Author(s) 2023.

关键词： Image classification

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An Experimental Analysis of Machine Learning Model in the Context of Sales Forecasting: -A Walmart Dataset as A Case Study 15

An Experimental Analysis of Machine Learning Model in the Co...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Wankhede, Disha Athanikar, Abhishek Borude, Yash Chougule, Raviraj Diware, Om Department of Computer Engineering Vishwakarma Institute of Information Technology India

ISBN: (纸本)9798331300579

Software programmers may get gradually precise at expecting consequences without being clearly coded using machine learning techniques. Machine learning is based on the idea that models and algorithms may collect input data then utilize statistical investigation to determine an output, while updating results as information become obtainable, as underlying principle. For example, models may be adapted and trained to meet management expectations so that correct measures are followed to reach a certain goal. Wall Mart, a one-stop shopping mall, has been used in this system to estimate sales of various products and to study impact that various variables have on sales of products. Predictive models may be built using different features of a Wall Mart dataset and methods used to construct them, and these findings can be used to make better business choices. This Predictive Algorithm is used in many fields over the market world for their profitability. Recently, Lot of companies are going to used this Machine Learning for better AI and new technologies in market. This report Includes combination of machine learning algorithm and Features engineering to predict Walmart sales. The research topic of this paper primarily solves the problems of Walmart sales forecasting that is provided by the Kaggle competition. © Grenze Scientific Society, 2024.

关键词： Input output programs

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Siamese Visual Tracking with Feature Enhancement Fusion 24

Siamese Visual Tracking with Feature Enhancement Fusion

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10th International Conference on Computing and Artificial Intelligence, ICCAI 2024

作者： Wu, Zhixi Liu, Baichen Zhu, Shunzhi Xiamen University of Technology School of Computer and Information Engineering China

ISBN: (纸本)9798400717055

In object tracking tasks, the use of a Siamese-based approach to construct trackers inevitably involves a crucial step-cross-correlation operations, which are employed to assess the similarity relationship between the template and the search area. However, this method is limited to obtaining only local information, lacking in global contextual understanding. Inspired by the Transformer's capability to capture long-Term dependencies, we propose a novel Siamese visual target tracking method with an attention mechanism, named SiamTr. Specifically, we design a feature enhancement fusion network to replace the cross-correlation computation, composed of a Hybrid Attention Module and a Cross-Attention Fusion Module. Additionally, we incorporate an Attention Feature Fusion(AFF) to merge shallow and deep features, thus acquiring diverse information. Experiments demonstrate that our proposed method achieves competitive results in multiple benchmark datasets, including OTB100, UAV123, and LaSOT. © 2024 ACM.

关键词： Target tracking

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TISCMB: design of a highly efficient blockchain consensus model with trust integrated self-correcting miner selection

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International Journal of information technology (Singapore) 2023年第4期15卷 1845-1858页

作者： Ravi Kumar, Shipra Goyal, Mukta Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Blockchain is rapidly becoming the de facto standard for storage applications requiring high transparency, record traceability, immutable data, and distributed processing. Researchers have proposed a large number of such models, including Proof-of-Work (PoW), Proof-of-Stake (PoS), Proof-of-Authority (PoA), etc. Due to their respective limitations, each of these models is applied to context-specific blockchain deployments. In addition, the selection of the most efficient miners for hash calculation and verification is a complex task that must be executed with high efficiency in order to improve network performance. The novel contribution of this work is to design a hybrid consensus model which uses a combination of Proof-of-Work and Proof-of-Stake consensus methods for fast hash computations. Proposed model is supported by a highly efficient trust-based miner selection method that aids in choosing the most optimal miner nodes with low processing delay and high energy efficiency. In addition, this text proposes a self-correcting mechanism for the designed blockchain, which assists in the blockchain's correction in the event of any internal or external attacks. Due to these characteristics, it is observed that the proposed model has 20% less delay, 8% less energy consumption, and 15% higher levels of trust than standard PoS and PoW consensus models. The model was also subjected to various attack scenarios, and it was determined that it is capable of self-correcting the node's internal blockchain with a 99.9% success rate, thereby enhancing its real-time deployment capabilities. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Blockchain Consensus Delay Energy Self-correcting Trust

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Autonomous embodied navigation task generation from natural language dialogues

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Science China(information Sciences) 2025年第5期68卷 119-132页

作者： Haifeng XU Yongchang LI Lumeng MA Chunwen LI Yanzhi DONG Xiaohu YUAN Huaping LIU Department of Automation Tsinghua University School of Physics and Electronic Information Yantai University School of Electrical and Electronic Engineering Shanghai Institute of Technology Department of Computer Science and Technology Tsinghua University

Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.

关键词： proactive robot robot navigation service robot large language model

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Multi-Objective Offloading Optimization in MEC and Vehicular-Fog Systems: A Distributed-TD3 Approach

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IEEE Transactions on Intelligent Transportation Systems 2024年第11期25卷 16897-16909页

作者： Wakgra, Frezer Guteta Kar, Binayak Tadele, Seifu Birhanu Shen, Shan-Hsiang Khan, Asif Uddin The Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei106 Taiwan The School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India

The emergence of 5G networks has enabled the deployment of a two-tier edge and vehicular-fog network. It comprises Multi-access Edge Computing (MEC) and Vehicular-Fogs (VFs), strategically positioned closer to Internet of Things (IoT) devices, reducing propagation latency compared to cloud-based solutions and ensuring satisfactory quality of service (QoS). However, during high-traffic events like concerts or athletic contests, MEC sites may face congestion and become overloaded. Utilizing offloading techniques, we can transfer computationally intensive tasks from resource-constrained devices to those with sufficient capacity, for accelerating tasks and extending device battery life. In this research, we consider offloading within a two-tier MEC and VF architecture, involving offloading from MEC to MEC and from MEC to VF. The primary objective is to minimize the average system cost, considering both latency and energy consumption. To achieve this goal, we formulate a multi-objective optimization problem aimed at minimizing latency and energy while considering given resource constraints. To facilitate decision-making for nearly optimal computational offloading, we design an equivalent reinforcement learning environment that accurately represents the network architecture and the formulated problem. To accomplish this, we propose a Distributed-TD3 (DTD3) approach, which builds on the TD3 algorithm. Extensive simulations, demonstrate that our strategy achieves faster convergence and higher efficiency compared to other benchmark solutions. © 2024 IEEE.

关键词： Quality of service

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