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arXiv 2023年

作者： Yu, Zheqi Zhang, Chao Machado, Pedro Zahid, Adnan Fernandez-Hart, Tim Imran, Muhammad Abbasi, Qammer James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Opteran Ltd Sheffield United Kingdom LAN-XEN Technology INC. Shanghai China Computation Intelligence and Applications Group Department of Computer Science Nottingham Trent University Nottingham United Kingdom School of Engineering & Physical Sciences Heriot Watt University EdinburghEH14 4AS United Kingdom Brunel University CEDPS Department of Electronic and Computer Engineering UxbridgeUB8 3PH United Kingdom

The growing demand for optimal and low-power energy consumption paradigms for Internet of Things (IoT) devices has garnered significant attention due to their cost-effectiveness, simplicity, and intelligibility. In this article, an Artificial Intelligence (AI) hardware energy-efficient framework to achieve optimal energy savings in heterogeneous computing through appropriate power consumption management is proposed. The deep reinforcement learning framework is employed, utilising the Actor-Critic architecture to provide a simple and precise method for power saving. The results of the study demonstrate the proposed approach’s suitability for different hardware configurations, achieving notable energy consumption control while adhering to strict performance requirements. The evaluation of the proposed power-saving framework shows that it is more stable, and has achieved more than 34.6% efficiency improvement, outperforming other methods by more than 16%. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Preface

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2025年 1149卷 xv-xvi页

作者： Udgata, Siba Kumar Sethi, Srinivas Kuanar, Sanjay Kumar Ghinea, George University of Hyderabad Telangana Hyderabad India Department of Computer Science Engineering and Applications Indira Gandhi Institute of Technology Odisha Dhenkanal India Department of Computer Science College of Engineering Design and Physical Sciences Brunel University London Uxbridge United Kingdom School of Engineering and Technology GIET University Odisha Gunupur India

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Evaluating Large Language Models on Spatial Tasks: A Multi-Task Benchmarking Study

arXiv

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arXiv 2024年

作者： Xu, Liuchang Zhao, Shuo Lin, Qingming Chen, Luyao Luo, Qianqian Wu, Sensen Ye, Xinyue Feng, Hailin Du, Zhenhong School of Mathematics and Computer Science Zhejiang Agriculture and Forestry University Hangzhou311300 China School of Earth Sciences Zhejiang University Hangzhou310058 China Department of Landscape Architecture and Urban Planning Center for Geospatial Sciences Applications and Technology Texas A&M University College StationTX77843 United States Financial Big Data Research Institute Sunyard Technology Co. Ltd Hangzhou310053 China

The advent of large language models such as ChatGPT, Gemini, and others has underscored the importance of evaluating their diverse capabilities, ranging from natural language understanding to code generation. However, their performance on spatial tasks has not been comprehensively assessed. This study addresses this gap by introducing a novel multi-task spatial evaluation dataset, designed to systematically explore and compare the performance of several advanced models on spatial tasks. The dataset encompasses twelve distinct task types, including spatial understanding and simple route planning, each with verified, accurate answers. We evaluated multiple models, including OpenAI’s gpt-3.5-turbo, gpt-4-turbo, gpt-4o, ZhipuAI’s glm-4, Anthropic’s claude-3-sonnet-20240229, and MoonShot’s moonshot-v1-8k, through a two-phase testing approach. Initially, we conducted zero-shot testing, followed by categorizing the dataset by difficulty and performing prompt tuning tests. Results indicate that gpt-4o achieved the highest overall accuracy in the first phase, with an average of 71.3%. Although moonshotv1-8k slightly underperformed overall, it surpassed gpt-4o in place name recognition tasks. The study also highlights the impact of prompt strategies on model performance in specific tasks. For example, the Chain-of-Thought (CoT) strategy increased gpt-4o’s accuracy in simple route planning from 12.4% to 87.5%, while a one-shot strategy enhanced moonshot-v1-8k’s accuracy in mapping tasks from 10.1% to 76.3%. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Adaptive Methods for Kernel Initialization of Convolutional Neural Network Model Applied to Plant Disease Classification

Adaptive Methods for Kernel Initialization of Convolutional ...

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Electrical, Electronic and Information sciences (eStream), Open Conference of

作者： Saloua Lagnaoui Khaoula Boumais Saad El Fallah Zakariae En-Naimani Khalid Haddouch Dalius Matuzevičius Laboratory Engineering Systems and Applications ENSA Sidi Mohamed Ben Abdellah University Fez Morocco Laboratory AI Data Science and Emerging Systems Sidi Mohammed ben Abdallah University ENSA Fez Morocco Systems and Sustainable Environment Laboratory Faculty of Engineering Sciences Private University of Fez Fez Morocco Laboratory Computer Science AI and Cyber Security ENSET University of Hassan II Casablanca Mohammedia Morocco Department of Electronic Systems Faculty of Electronics Vilnius Gediminas Technical University Vilnius Lithuania

ISBN: (数字)9798350352412

ISBN: (纸本)9798350352429

Convolutional Neural Networks are instrumental in artificial intelligence, especially in image processing, where their ability to autonomously learn hierarchical features has led to significant breakthroughs. However, the success of these models is intricately tied to the judicious choice of hyperparameters, which include the configuration of convolutional layers, activation functions, and kernel initialization methods. This research explores kernel initialization methods in Convolutional Neural Network models, seven diverse initialization methods (Glorot Uniform, Ones initialization, Zero initialization, Constant initialization, Random initialization, HeNormal initialization, and Orthogonal initialization) are comprehensively compared. The primary objective is to showcase the sensitivity of Convolutional Neural Networks to these various initialization techniques. The study not only aims to reveal the nuanced impact of kernel initialization but also introduces an adaptive method to enhance model performance. By delving into the intricacies of initialization methods, this research contributes to the improvement of Convolutional Neural Networks effectiveness, especially in critical applications like Plant Disease classification.

关键词： Training Plant diseases Adaptation models Sensitivity Neural networks Convolutional neural networks Kernel

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Optimising Diabetic Retinopathy Classification using EfficientNetB7

Optimising Diabetic Retinopathy Classification using Efficie...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Ajisha Mathias J. Relin Francis Raj S R Raja R. Santhana Krishnan C. Puvanadevi S. Sujatha Electronics and Communication Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi Chennai India Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Master of Computer Applications Hindustan Institute of Technology and Science Chennai India Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Information Technology Adhiyamaan College of Engineering Hosur India Department of Information Technology Adhiyamaan College of Engineering Hosur India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Diabetic retinopathy (DR) is a significant cause of vision impairment and blindness in individuals with diabetes, necessitating early and accurate detection of its various stages for effective treatment. This study applies EfficientN etB7, an advanced deep learning model, to classify different stages of diabetic retinopathy and compares its performance with other established models such as DenseN et121, Inception V3,and VGG16. The study uses datasets like the Kaggle Diabetic Retinopathy Detection and Messidor datasets, which provide annotated retinal images across multiple stages of DR: mild, moderate, severe, proliferative diabetic retinopathy, and normal. Key preprocessing techniques are employed to enhance the quality of the input data. These include noise reduction, contrast enhancement using CLAHE, and data augmentation to enhance the model's reslience. EfficientNetB7 is selected due to its optimal network depth, width, and resolution balance, which facilitates high accuracy with fewer computational resources. The model's performance is evaluated using metrics including Fl score, accuracy, precision, and recall, which are crucial for assessing the reliability of medical image classification models. Comparative analysis reveals that EfficientN etB7 outperforms DenseNet121, InceptionV3, and VGG16 across all evaluation metrics. Specifically, EfficientN etB7 achieves superior accuracy, precision, recall, and Fl score in classifying the different stages of diabetic retinopathy. These results highlight the effectiveness of EfficientN etB7 in providing reliable and efficient detection of diabetic retinopathy, highlighting its potential for improving clinical outcomes through early and precise diagnosis.

关键词： Measurement Deep learning Diabetic retinopathy Accuracy Computational modeling Visual impairment Data augmentation Data models Robustness Medical diagnostic imaging

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SOME NEW RESULTS CONCERNING THE VALENCES OF (SUPER) EDGE-MAGIC GRAPHS

arXiv

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arXiv 2023年

作者： Takahashi, Yukio Muntaner-Batle, Frances A. Ichishima, Rikio Department of Science and Engineering Faculty of Electronics and Informatics Kokushikan University 4-28-1 Setagaya Setagaya-ku Tokyo154-8515 Japan Graph Theory and Applications Research Group School of Electrical Engineering and Computer Science Faculty of Engineering and Built Environment The University of Newcastle NSW2308 Australia Department of Sport and Physical Education Faculty of Physical Education Kokushikan University 7-3-1 Nagayama Tama-shi Tokyo206-8515 Japan

A graph G is called edge-magic if there exists a bijective function f: V (G)∪E (G) → {1, 2, . . ., |V (G)| + |E (G)|} such that f (u)+f (v)+f (uv) is a constant (called the valence of f) for each uv ∈ E (G). If f (V (G)) = {1, 2, . . ., |V (G)|}, then G is called a super edge-magic graph. A stronger version of edge-magic and super edge-magic graphs appeared when the concepts of perfect edge-magic and perfect super edge-magic graphs were introduced. The super edge-magic deficiency µs (G) of a graph G is defined to be either the smallest nonnegative integer n with the property that G ∪ nK1 is super edge-magic or +∞ if there exists no such integer n. On the other hand, the edge-magic deficiency µ (G) of a graph G is the smallest nonnegative integer n for which G ∪ nK1 is edge-magic, being µ (G) always finite. In this paper, the concepts of (super) edge-magic deficiency are generalized using the concepts of perfect (super) edge-magic graphs. This naturally leads to the study of the valences of edge-magic and super edge-magic labelings. We present some general results in this direction and study the perfect (super) edge-magic deficiency of the star K1,*** Codes 05C78 © 2023, CC BY.

关键词： Graphic methods

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Empirical Evaluation of Blockchain Based Remote Data Communication System over Wireless Sensor Network (WSN)

Empirical Evaluation of Blockchain Based Remote Data Communi...

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2021 International Conference on Technological Advancements and Innovations, ICTAI 2021

作者： Mehbodniya, Abolfazl Angra, Prikshat Kumar Hindumathi, V. Vishwakarma, Satyendra Rajasekar, P. Vijaya Karthik, S.V. Kuwait College of Science and Technology Department of Electronics and Communications Engineering Kuwait School of Computer Applications Lovely Professional University Phagwara India Electronics and Communication Engineering Bvrit Hyderabad College Engineering for Women Hyderabad India Electrical Engineering Babasaheb Bhimrao Ambedkar University Lucknow India Electronics and Communications Engineering Kings College of Engineering Thanjavur India

ISBN: (纸本)9781665420877

The Internet of Things (IoT) concept enables the connectivity and sending and receiving of data across millions of intelligent objects. This paradigm continues to evolve and increase tremendously, just as the dangers and assaults on IoT infrastructures do. Wireless sensor networks are viewed as excellent prospects for a variety of applications, including industrial monitoring, data gathering in hazardous settings, and military operations. The technology of Blockchain has emerged in recent years as among the most revolutionary technologies. This technology was developed to facilitate digital transactions and to enable safe and trustworthy access to the distributed ledger. Blockchain technology combined with smart contracts enables users to conduct safe transactions without trusting external parties or intermediaries. Attacks and dangers are increased in centralized IoT systems, particularly when data is transferred and shared with other companies between devices. To avoid such circumstances, this study proposes a decentralized approach that ensures an IoT system's autonomy and security. This paper addresses the recent approaches that are based on blockchain, we will review the present models and discuss their advantages and drawbacks to find out the solutions and the way to other studies. © 2021 IEEE.

关键词： Blockchain

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DeepTweet: Leveraging Transformer-based Models for Enhanced Fake News Detection in Twitter Sentiment Analysis

DeepTweet: Leveraging Transformer-based Models for Enhanced ...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： N. Vallileka P. Sundaravadivel U. Karthikeyan R. Santhana Krishnan K. Lakshmi Narayanan S. Sundararajan Department of Computer Applications PSNA College of Engineering and Technology Dindigul India Department of Artificial Intelligence & Machine Learning Saveetha Engineering College Thandalam India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Francis Xavier Engineering College Tirunelveli India Department of Mechanical Engineering SCAD College of Engineering and Technology Cheranmahadevi India

With the increase in the popularity of the social media sites, there arises lot of adverse effects including the spread of fake news. This research study proposes a novel algorithm, called DeepTweet, which leverages transformer-based models for enhanced fake news detection in Twitter sentiment analysis. The aim is to improve the accuracy and efficiency of identifying fake news in real-time by exploiting the contextual information and semantic representations captured by the transformer-based models. The proposed DeepTweet algorithm utilizes a pre-trained transformer model, such as BERT or GPT, to encode the textual content of tweets and extract rich contextual embeddings. These embeddings capture both local and global dependencies, enabling a more comprehensive understanding of the sentiment expressed in a tweet. Furthermore, a novel attention mechanism tailored for fake news detection is introduced to focus on key textual features that are indicative of potential misinformation. To assess the efficiency of DeepTweet, extensive simulations using a large dataset of labelled tweets containing both genuine and fake news are done. Further, this study compares the performance of DeepTweet with existing algorithms, including LSTM, CNN, and traditional machine learning approaches. Various simulation metric measures such as accuracy, precision, recall, and F1 score are employed to analyze the algorithm's performance. The simulation grades demonstrate that DeepTweet outperforms the prevailing algorithms by means of accuracy and overall detection performance. The proposed attention mechanism proves to be particularly effective in identifying subtle linguistic cues and deceptive patterns associated with fake news. The enhanced contextual embeddings captured by the transformer-based models contribute to improved sentiment analysis and subsequently lead to more accurate fake news detection.

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Colorectal cancer risk following appendectomy:a pooled analysis of three large prospective cohort studies

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Cancer Communications 2022年第5期42卷 486-489页

作者： Joseph A.Rothwell Nagisa Mori Fanny Artaud Agnès Fournier Marco Conte Marie-Christine Boutron-Ruault Simon S.M.Chan Marc J.Gunter Neil Murphy Gianluca Severi UniversitéParis-Saclay UVSQInsermGustave RoussyExposome and Heredity teamCentre for Epidemiology and Population Health(CESP U1018)Villejuif 94800France Nutrition and Metabolism Branch International Agency for Research on Cancer(IARC)150 Cours Albert ThomasLyon 69008France Department of Gastroenterology Norfolk and Norwich University Hospital NHS TrustNorwich NR47UYUnited Kingdom Department of Medicine Bob Champion Research and Education BuildingNorwich Medical SchoolUniversity of East AngliaNorwich NR47UQUnited Kingdom Department of Statistics Computer ScienceApplications“G.Parenti”University of FlorenceFlorence 50134Italy

Dear Editor,Appendectomy is usually performed to treat acute appendicitis although it may also be conducted in the absence of appendicitis when patients suffer from chronic abdominal *** is also known to mitigate the risk of ulcerative colitis[1],a risk factor for colorectal *** impact of appendectomy on colorectal cancer risk,particularly in the long term,is *** procedurewas initially hypothesized to increase colorectal cancer risk due to decreased immunocompetency.

关键词： cancer colorectal acute

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A technique for developping mobile applications 4

A technique for developping mobile applications

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4th International Symposium on Informatics and its applications, ISIA 2020

作者： Elmahdi Bourahla, Aissa Bourahla, Mustapha University Mohamed Boudiaf of m'Sila Computer Science Department M'Sila Algeria Laboratory of Informatics and Its Applications of m'Sila University Mohamed Boudiaf of m'Sila M'Sila Algeria

ISBN: (纸本)9781728196527

Software engineering principles are very required for the development of mobile applications, which are necessary for many life applications. In this paper, we present a development process based on transformation of UML models, which describe mobile applications based on migrant objects to Mobile Maude language that extends the Maude language and implements the Rewriting Logic (RL). The generated rewriting theories will be executed for simulation using located configurations, which are produced from UML object diagrams. © 2020 IEEE.

关键词： Application programs

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