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arXiv 2024年

作者： Schrader, Maxwell Kumar, Navish Sørig, Esben Yoon, Soonmyeong Srivastava, Akash Xu, Kai Astefanoaei, Maria Collignon, Nicolas Department of Mechanical Engineering University of Alabama United States Department of Mathematics and Computer Science University of Basel Switzerland Kale AI London United Kingdom MIT-IBM Watson AI Lab CambridgeMA United States Data-intensive Systems and Applications IT University of Copenhagen Denmark

Purpose: Light goods vehicles (LGV) used extensively in the last mile of delivery are one of the leading polluters in cities. Cargo-bike logistics and Light Electric Vehicles (LEVs) have been put forward as a high impact candidate for replacing LGVs. Studies have estimated over half of urban van deliveries being replaceable by cargo-bikes, due to their faster speeds, shorter parking times and more efficient routes across cities. However, the logistics sector suffers from a lack of publicly available data, particularly pertaining to cargo-bike deliveries, thus limiting the understanding of their potential benefits. Specifically, service time (which includes cruising for parking, and walking to destination) is a major, but often overlooked component of delivery time modelling. The aim of this study is to establish a framework for measuring the performance of delivery vehicles, with an initial focus on modelling service times of vans and cargo-bikes across diverse urban environments. Methods: We introduce two datasets that allow for in-depth analysis and modelling of service times of cargo bikes and use existing datasets to reason about differences in delivery performance across vehicle types. We introduce a modelling framework to predict the service times of deliveries based on urban context. We employ Uber's H3 index to divide cities into hexagonal cells and aggregate OpenStreetMap tags for each cell, providing a detailed assessment of urban context. Leveraging this spatial grid, we use GeoVex to represent micro-regions as points in a continuous vector space, which then serve as input for predicting vehicle service times. We show that geospatial embeddings can effectively capture urban contexts and facilitate generalizations to new contexts and cities. Our methodology addresses the challenge of limited comparative data available for different vehicle types within the same urban settings. Results: Our findings indicate a significant impact of urban context on the perf

关键词： Trucks

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Two-Stage Guided Constraint Differential Evolution Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1109-1133页

作者： Sung, Tien-Wen Liang, Qiaoxin Hong, Chuanbo Huang, Zeming Li, Wei Nguyen, Trinh-Dong College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam

The development of population intelligence has shown a great trend of a linear surge in recent years, and a large number of intelligent algorithms inspired by biology have been studied. Among them, differential evolution (DE) algorithms have attracted the attention of many researchers because of their simplicity, high efficiency, powerful search capability, etc. DE has strong exploitability and can approach the global optimum faster, but it cannot avoid the phenomenon of search stagnation and premature convergence. To address the drawbacks of DE, the two-stage bootstrap constrained differential evolution algorithm (TSGCDE) is proposed in this paper. In TSGCDE, instead of the traditional one population, the population is divided into two as a way to increase the diversity of the population. Divided into two stages, each stage searches for optimal solutions in different directions in different populations, and the optimal solutions and the bootstrap solutions generated by the populations in the two stages are bounded by each other’s bootstrap, which makes the DE have a more powerful optimal-seeking ability and easier to jump out of the local optimum. To validate the performance of TSGCDE, we conducted experiments on CEC2013 and CEC2014 benchmarks with some well-known algorithm variants, and the experimental results show that our proposed algorithm is competitive. © 2023, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Swarm intelligence

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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： adaptive coding chinese remainder theorem for polynomials encrypted images Reversible data hiding secret sharing

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Adaptive Fourier Decomposition of the First Three SARS-CoV-2 Infection Waves with Epidemic Intervention—London,UK,2020-2022

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China CDC weekly 2024年第21期6卷 478-486,I0006-I0008页

作者： Zige Liu Guibin Lu Cheokieng Vong Zhiqi Zeng Wei He Zhijie Lin Cuiyun Lin Kaichin Hsieh Zifeng Yang Arlindo L.Oliveira Chitin Hon Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Queen Ethelburga’s Collegiate Thorpe Underwood EstateYorkUK State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Faculty of Innovation Engineering School of Computer Science and EngineeringMacao University of Science and TechnologyMacao SARChina University College London UCL Faculty of Engineering SciencesLondonUK Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina Instituto de Engenharia de Sistemas e Computadores:Investigação e Desenvolvimento em Lisboa LisboaPortugal Instituto Superior Técnico Universidade de LisboaLisboaPortugal

Background:This study provides a detailed analysis of the daily fluctuations in coronavirus disease 2019(COVID-19)case numbers in London from January 31,2020 to February 24,*** primary objective was to enhance understanding of the interactions among government pandemic responses,viral mutations,and the subsequent changes in COVID-19 case ***:We employed the adaptive Fourier decomposition(AFD)method to analyze diurnal changes and further segmented the AFD into novel multi-component groups consisting of one to three *** restructured components were rigorously evaluated using Pearson correlation,and their effectiveness was compared with other signal analysis *** study introduced a novel approach to differentiate individual components across various time-frequency scales using basis decomposition ***:Analysis of London’s daily COVID-19 data using AFD revealed a strong correlation between the“stay at home”directive and high-frequency components during the first epidemic *** indicates the need for sustained implementation of vaccination policies to maintain their ***:The AFD component method provides a comprehensive analysis of the immediate and prolonged impact of governmental policies on the spread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).This robust tool has proven invaluable for analyzing COVID-19 pandemic data,offering critical insights that guide the formulation of future preventive and public health strategies.

关键词： offering sustained London

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ML Models and Big data Analytics to Predict the Cricket Match

ML Models and Big Data Analytics to Predict the Cricket Matc...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： V. Asha S. Sreekanth Boddu Rajasekhar Ginni Nijhawan Tamam Ali Abd Ulabbas Abedi Ashish Parmar Department of Computer Applications New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Hyderabad India Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India LPU Phagwara India Medical Technology The Islamic University Najaf Iraq Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

artificial intelligence (AI)and Machine learning have greatly improved the accuracy of cricket, one of the most popular, well-supported, and exhilarating sports of today. The quantity of matches played throughout time has led to an exponential growth in the data about cricket matches and individual players. Furthermore, big data analytics are in high demand, and this data has many potential applications. Some examples of its potential applications include team player selection and prediction utilizing machine learning models and significant data approaches. A machine learning linear reversion model was used to forecast the team's performance in the absence of massive data and the comprehensive Spark ML framework. Following the application of linear regression in Spark ML, the untried outcomes are evaluated using accuracy, mean square error (MSE) and mean absolute error (MAE) root mean square error (RMSE), which are reported as 95%, 30.2, 1350.34, and 28.2 correspondingly. More than that, may use this method for other kinds of sports.

关键词： Analytical models Accuracy Linear regression Urban areas Machine learning Predictive models Big data data models Sparks Sports

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Adaptive PUPM-based HEVC Video Steganography Balancing Embedding Performance and Security

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IEEE Transactions on Multimedia 2025年

作者： Yu, Lifang Yu, Zichao Weng, Shaowei Chen, Dewang Beijing Institute of Graphic Communication Department of Information Engineering Beijing102600 China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou350108 China Fujian University of Technology School of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology School of Transportation Fuzhou China

For the prediction unit partition modes (PUPM)-based steganography, a mainstream branch of high efficiency video coding (HEVC) video steganography, striking a balance between embedding performance and security is very challenging. Including the 2N × 2 N PUPMs having the maximum number of PUPMs into data embedding is indeed an effective way of enlarging the embedding capacity, but it necessarily causes a significant decline in security. Therefore, a multi-factor-involved cost function (MFICF) is proposed in this paper to evaluate the embedding cost for modifying each PUPM by comprehensively considering four different aspects affecting the embedding performance and security. With the assistance of MFICF, the 7-ary notational system is combined to use all the 7 types of PUPMs containing 2N × 2N for data embedding, thus enlarging the embedding capacity as well as enhancing the embedding efficiency. The syndrome-trellis code driven by MFICF, named CFSTC, is designed to preferentially select PUPMs with low embedding costs for data embedding, so that the embedding efficiency is largely enhanced. The security is effectively guaranteed by allocating a large embedding cost for modifying 2N × 2N to another type of PUPM. Finally, a lightweight convolutional neural network in combination with gated channel transformation, called GSCNet, is proposed to replace the in-loop filter in HEVC, further optimizing the visual distortion and bitrate increase caused by data embedding. Combining these components above, we design a PUPM-based steganography algorithm, GSAPM. Experimental results show that GSAPM effectively enhances the embedding performance while maintaining high security. © 1999-2012 IEEE.

关键词： Cost functions

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Integrating Artificial Intelligence and Multiple Intelligences for Advanced Educational Models

Integrating Artificial Intelligence and Multiple Intelligenc...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Suresh Palarimath Pyingkodi Maran R. Venkateswaran Wilfred Blessing N.R K. V Shiny S. Renuga College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence’s framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce.

关键词： Technological innovation Education Machine learning Transforms Real-time systems Problem-solving Artificial intelligence Pupils Creativity STEM

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Edge Intelligence Hybrid Framework for Real-Time IoT applications Using Multi-Stage Adaptive Processing Methods

Edge Intelligence Hybrid Framework for Real-Time IoT Applica...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Sajja Suneel Rakesh Kumar Yadav K.Nirmala Devi B.Teja Sree Aarthi R Shrikant Upadhyay Department of CSE(Data Science) Institute of Aeronautical Engineering Hyderabad Telangana India Amity School of Engineering and Technology Amity University Patna Bihar Department of Computer Applications Madanapalle Institute of Technology & Science Department of IT S.R.K.R Engineering College Bhimavaram Department of ECE Rajalakshmi Institute of Technology Chembarambakkam Tamil Nadu Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The rapid proliferation of IoT devices has created an unprecedented need for real-time data processing and decision-making. This paper proposes an Edge Intelligence Hybrid Framework designed for real-time IoT applications using multi-stage adaptive processing methods. The framework structured into three layers: edge-level processing, fog-level aggregation, and cloud-level computation, enabling efficient distribution of tasks across the hierarchy. At the edge, lightweight models and preprocessing techniques, such as Kalman Filtering and Huffman Coding, ensure low-latency and energy-efficient operation. Intermediate computations and model synchronization occur at the fog level, while high-complexity tasks like deep learning model training performed at the cloud level. Results demonstrate that edge-level processing achieves an accuracy of 85.4 %, while fog and cloud levels improve accuracy to 89.4% and 94.9%, respectively. Latency at the edge is as low as 9-13ms, compared to fog (23-29ms) and cloud (117-125ms), ensuring suitability for time-critical IoT tasks. The proposed framework effectively balances latency, accuracy, and resource optimization, making it ideal for applications such as predictive maintenance, healthcare, and smart surveillance.

关键词： Performance evaluation Adaptation models Accuracy Computational modeling Decision making Medical services Real-time systems Internet of Things Time factors Low latency communication

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Dimensionality Reduction with DLMNN Technique for Handling Secure Medical data in Healthcare-IoT Model

Dimensionality Reduction with DLMNN Technique for Handling S...

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Artificial Intelligence and Smart Systems (ICAIS), International Conference on

作者： M. Lalithambigai V. Kalpana A. Sasi Kumar J Uthayakumar J. Santhosh R Mahaveerakannan Department of Computer Applications Sri Krishna Adithya College of Arts and Science Coimbatore Vel Tech University Department of Cloud Technology & Data Science Institute of Engineering & Technology Srinivas University Mukka Surathkal Karnataka State India Department of AI&DS Dhanalakshmi Srinivasan College of Engineering Coimbatore Department of Computer Science and Engineering Saveetha School of Engineering Chennai Tamil Nadu India

Security and maintaining the integrity of sensitive patient information have emerged as major obstacles for healthcare service applications in the wake of IoT’s meteoric rise in the healthcare industry. The diagnostic information included in medical photographs is of particular importance, and this research presents a hybrid security strategy to protect against such breaches. Chronic diseases such as Heart Disease (HD), cancer, and chronic respiratory sickness are the main causes of death worldwide. Those who have a heart attack suddenly have a very poor chance of surviving. As a result, this paper proposes a heart disease monitoring scheme, wherein a Deep Learning Modified Neural Network (DLMNN) centred on the Internet of Things is used to aid in the diagnosis and treatment of HD. There are three stages to implementing this strategy: First, there is encryption; second, a combination of information gain (IG) and principle component analysis (PCA) to reduce dimensionality; and third, a classification system. To begin, the patient is fitted with a wearable Internet of Things (IoT) sensor device, which simultaneously uploads its sensor data to a remote server. The sensor data is encrypted using a hybrid encryption scheme that combines the Advanced Encryption Standard (AES) and the Rivest, Shamir, and Adleman (RSA) algorithms. A DLMNN classifier is then used to classify the data after it has been decrypted. There are two types of classification outcomes. It provides information on the patient’s heart and notifies the doctor in case of abnormalities. Estimated study results demonstrate that DLMNN is an improvement over previously used algorithms for HD diagnosis. The proposed AES-RSA used to support safe data transfer also yields the highest level of security, i.e. 94.32%, and does so in the quickest time for encryption and decryption.

关键词： Heart Dimensionality reduction Medical services data transfer data models Encryption Classification algorithms

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3D CT Liver Images Lesion Extraction and Classification for Using 3D-CNN with GLRLM

3D CT Liver Images Lesion Extraction and Classification for ...

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Advanced Computing Technologies (ICoACT), International Conference on

作者： S. Jayarathna R. Sowmiya G. Nallasivan G. Sahaana S. Ashok R. Saravanakumar Master of Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Computer Applications Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence & Data Science Dhanalakshmi College of Engineering Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The imaging technique known as computed tomography (CT) is often considered to be the most reliable way for non-invasive diagnosis. Through the use of three-dimensional (3D) computed tomography images, we were able to categorize aggressive tumors that were found in the liver for the aim of this research. When it comes to the three-dimensional (3D) CT image, the dimension reduction is accomplished by the use of principal component analysis (PCA). The Random Forest Algorithm is responsible for identifying the abnormal hepatic region, and a median filter is used to reduce the amount of noise occurring. Methods of data augmentation are also employed in this process. To extract Gray Level Run Length Matrix (GLRLM) features, using Pyradiomics python package. These features are then fed into a 3D-CNN classifier to classify malignant tumors and haemangiomas. Our model was found to have greater performance after being compared to the findings of other models. This was revealed after the comparison was made. For the training dataset, our model has an accuracy of 97.2%, and for the test dataset, it has an accuracy of 98.25%. This accuracy is determined by the performance metrics that our model incorporates.

关键词： Training Solid modeling Accuracy Three-dimensional displays Computed tomography Liver Feature extraction data augmentation data models Principal component analysis

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