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32nd ACM World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge servers have been included between clients and the parameter server to aggregate clients' local models. Recent studies on this edge-assisted hierarchical FL scheme have focused on ensuring or accelerating model convergence by coping with various factors, e.g., uncertain network conditions, unreliable clients, heterogeneous compute resources, etc. This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds;2) it does not recognize or utilize model diversity when producing a global model;and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. Extensive experiments are conducted on two widely-used public datasets. The results demonstrate that, compared with state-of-the-art FL schemes, FedEdge accelerates model convergence by 1.14 × -3.20 ×, and improves model accuracy by 2.14% - 6.63%. © 2023 ACM.

关键词： Edge computing

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ML_Recognition: A Robust Model for Handwritten Digit Recognition Using Machine Learning Classifiers 3

ML_Recognition: A Robust Model for Handwritten Digit Recogni...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Abdelminaam, Diaa Salama Madbouly, Magda M. Zeid, Magdy Abd-Elghany Abualigah, Laith Misr International University Faculty of Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt Alexandria University Faculty of Computer and Data Science Alexandria Egypt Alobour High Institute for Computer and Informatics Ibrahim Abd Elatif Gomaa Computer Science Department Cairo Egypt Lebanese American University Department of Electrical and Computer Engineering Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Middle East University Meu Research Unit Amman11831 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia

ISBN: (纸本)9798350306231

Handwritten digit recognition is a branch of machine learning in which a computer is taught to recognize hand-written numbers. Classification and regression are applied using deep learning and machine learning algorithms. In this paper, we discussed the efficiency of different algorithms: Random Forest, KNN, Naive Bayes, SVM, CNN, and Decision Tree Algorithm. Those algorithms were applied in the handwritten digit recognition process using the MINIST data set. By comparing each algorithm by training every one of them on the data set, then by testing, we got the best algorithm to get the optimal results with the highest accuracy. After comparing the results of each algorithm, it has shown that the neural network has the best results with 97.3% classification accuracy followed by K- the nearest neighbor with 97.2%. © 2023 IEEE.

关键词： Support vector machines

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Crop for Est - Crop Forecasting and Estimation. Crop Yield Estimation and Profitability Analysis for Precision Agriculture

Crop for Est - Crop Forecasting and Estimation. Crop Yield E...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： C. Shanmugasundaram C. Umamaheswari A. Vijayalakshmi Prabha Elizabeth Varghese Department of Electrical and Electronics Engineering Manakula Vinayagar Institute of Technology Puducherry Department of Computer Science with Data Science St. Thomas College of Arts and Science Tamil Nadu Department of Biomedical Engineering Sri Manakula Vinayagar Engineering College Puducherry

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

The agriculture industry significantly contributes to the nation's economy by employing a large segment of the population and enhancing the GDP. It is a primary source of livelihood for many people, particularly in rural areas. India's agricultural sector is known for its diversity, producing a wide range of crops including rice, wheat pulses, sugarcane, cotton, fruits, and vegetables. This industry faces a significant challenge in predicting crop yield production and crop demand accurately. Efforts have been made to address these challenges through various government initiatives such as subsidies and continued investment in agricultural research and development. The traditional methods of predicting crop yield and market demand is cumbersome and time consuming and is susceptible to errors, resulting in inefficient resource utilization and revenue losses. There is a need for predicting the most efficient crop based on various influential environmental markers and forecasting the profitability. This project aims to develop an intelligent platform that uses machine learning algorithms to estimate the crop for cultivation in each area based on environmental factors. The project also aims to forecast market demand and pricing trends for the identified crop with the aim of increasing the profitability for the farmers and other stakeholders in the agriculture industry.

关键词： Profitability Rural areas Estimation Production Pricing Stakeholders Resource management Forecasting Crop yield Research and development

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Integrating Branching and Pruning for Efficient Hyperdimensional Computing

Integrating Branching and Pruning for Efficient Hyperdimensi...

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IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Jing Liu Zhiqian Guan Di Liu Shengfa Miao Fei Dai School of Software Yunnan University China Department of Computer Science Norwegian University of Science and Technology Norway School of Big Data and Intelligent Engineering Southwest Forestry University China

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

As an emerging brain-inspired computing method, hyperdimensional computing (HDC) has attracted increasing attention. HDC encodes data into a high-dimensional hypervector and makes predictions based on the similarity comparison of the hypervector. However, the computational overhead of an HDC model increases, and the accuracy decreases as the number of predicted classes increases. Branching has been proposed to address this issue in HDC models, but it also introduces a new challenge: increased memory overhead for storing new branching hypervectors. In this paper, we aim to address this issue by integrating branching with the pruning method in HDC models. To this end, we propose a simple yet effective two-level branching structure that can reduce the memory overhead required by branching hypervectors. In addition, we propose a novel variable-position pruning method capable of removing redundant dimensions from hypervectors at various positions. This can best identify the important dimensions of a hypervector and further mitigate the memory issue caused by the branching structure. Experimental results demonstrate that our two-level branching structure can slightly increase the accuracy across various datasets. With only a 0.5 % accuracy loss, our pruning method can remove more than half of the dimensions, thereby improving the HDC model's inference latency and energy consumption.

关键词： Energy consumption Accuracy Computational modeling Memory management Predictive models Brain modeling Encoding

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BRANCH & LEARN WITH POST-HOC CORRECTION FOR PREDICT+OPTIMIZE WITH UNKNOWN PARAMETERS IN CONSTRAINTS

arXiv

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arXiv 2023年

作者： Hu, Xinyi Lee, Jasper C.H. Lee, Jimmy H.M. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Department of Computer Sciences Institute for Foundations of Data Science University of Wisconsin MadisonWI United States

Combining machine learning and constrained optimization, Predict+ Optimize tackles optimization problems containing parameters that are unknown at the time of solving. Prior works focus on cases with unknowns only in the objectives. A new framework was recently proposed to cater for unknowns also in constraints by introducing a loss function, called Post-hoc Regret, that takes into account the cost of correcting an unsatisfiable prediction. Since Post-hoc Regret is non-differentiable, the previous work computes only its approximation. While the notion of Post-hoc Regret is general, its specific implementation is applicable to only packing and covering linear programming problems. In this paper, we first show how to compute Post-hoc Regret exactly for any optimization problem solvable by a recursive algorithm satisfying simple conditions. Experimentation demonstrates substantial improvement in the quality of solutions as compared to the earlier approximation approach. Furthermore, we show experimentally the empirical behavior of different combinations of correction and penalty functions used in the Post-hoc Regret of the same benchmarks. Results provide insights for defining the appropriate Post-hoc Regret in different application scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Constrained optimization

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Triplet Attention-Enhanced UNet Architectures for Advanced Skin Lesion Segmentation 8

Triplet Attention-Enhanced UNet Architectures for Advanced S...

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8th International Artificial Intelligence and data Processing Symposium, IDAP 2024

作者： Al-Huda, Zaid Al-Antari, Mugahed A. Algburi, Riyadh Nazar Ali Al-Maqtari, Omar Rajeh, Taha M Khan, Ghufran Ahmad Chengdu University Stirling College Chengdu610106 China Sejong University College of Ai Convergence Department of Artificial Intelligence and Data Science Seoul Korea Republic of Al-Farahidi University Aeronautical Technical engineering College of Technical Engineering Baghdad Iraq Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu China Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798331531492

ccurate segmentation of skin lesions is critical the early detection and successful treatment of skin cancer, particularly melanoma, which has a significant impact on patient survival rates. This paper investigates a novel approach to skin lesion segmentation by integrating the Triplet Attention mechanism into UNet-based architectures, aiming to enhance the precision and reliability of automated segmentation in der-moscopic images. The proposed model leverages the strengths of both UNet's encoder-decoder structure and the attention mechanism's ability to focus on critical features while suppressing irrelevant background noise. Additionally, an Adaptive Weighted Loss function is introduced to tackle the class imbalance often present in skin lesion datasets, thereby enhancing segmentation performance. Extensive experiments were conducted using publicly available datasets, demonstrating significant improvements in segmentation accuracy and robustness, particularly in challenging cases with unclear lesion boundaries and varying illumination conditions. Our results indicate that the Triplet Attention-Enhanced UNet model, combined with the Adaptive Weighted Loss function, outperforms existing methods, providing a more reliable tool for dermatologists in the early diagnosis and treatment planning of skin cancer. © 2024 IEEE.

关键词： Dermatology

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Sentiment Analysis of Movie Reviews: Optimizing Predictions Through TF-IDF Vectorization

Sentiment Analysis of Movie Reviews: Optimizing Predictions ...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Mohd Irfan Anne Sreyas Venkatesam Hariharan Shanmugasundaram Mahendra Kumar Gourisaria Subrata Chowdhury Sudhansu Shekhar Patra Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad Telangana India Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India School of Computer Engineering Kalinga Institute of Industrial Technology (KIIT) Deemed to be University Bhubaneswar India Dept. of Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies(A) Chittoor Andhra Pradesh India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Sentiment analysis model is aimed to accurately interpret movie reviews, addressing challenges like sarcasm and noisy data. By integrating multiple machine learning classifiers Naive Bayes, Random Forest, K-Nearest Neighbors, Logistic Regression, Decision Tree, and XGBoost—the model enhances sentiment prediction accuracy. The Term Frequency-Inverse Document Frequency (TF-IDF) method is used to extract features from a dataset of 50,000 movie reviews. Performance is evaluated using accuracy, precision, recall, and F1-score, with Logistic Regression achieving the best results, including an highest accuracy of 84.33 % and an F1-score of 0.847. In comparison to traditional methods, this multi-classifier approach offers a deeper understanding of movie reviews and its influence on audience decisions. The study demonstrates the potential of using these machine learning techniques to provide real-time, scalable sentiment analysis, which is crucial for filmmakers and marketers seeking insights into audience satisfaction and box office performance.

关键词： Sentiment analysis Logistic regression Accuracy Reviews Predictive models Motion pictures Feature extraction Real-time systems Noise measurement Random forests

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FPGA Implementation of XOR-MUX based Full Adder and Truncated Multiplier for Signal Processing Applications 15

FPGA Implementation of XOR-MUX based Full Adder and Truncate...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Pavithara, P. Raghapriya, N.R. Dinesh, P.M. Gowtham, S. Viji, D. Kavin Kumar, K. Chandrasekaran, Gokul Department of Electronics and Communication Engineering Kongu Engineering College Perundurai Erode638060 India Department of Computer Science and Engineering Erode Sengunthar Engineering College Thuduppathi638057 India Department of Electronics and Communication Engineering Sona College of Technology Salem636005 India Department of Electrical and Electronics Engineering KSR College of Engineering Tiruchengode637215 India Department of Artificial Intelligence and Data Science Erode Sengunthar Engineering College Thuduppathi638057 India Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (纸本)9798350370249

There has been considerable advancement in the digital world, particularly in image processing applications, in recent years. The most important requirements are area efficiency, minimal delay, low power consumption, and fast speed. Due to the arithmetic operations of adders and multipliers, digital devices are prone to have increase in the size of logic because they take up more space in VLSI implementation. The proposed method of XOR-MUX based full adder and truncated multiplier concentrates on reducing the delay and area. Replacing normal multipliers with truncated multipliers computes n bit output instead of 2n bit outputs that is, the MSB bit of the truncated multiplier are retained whereas the LSB bits are truncated thereby preserving the information. This is suitable for certain applications of FIR filters that do not concentrate on lower bit length. In this project, the conventional full adders are replaced by XOR-MUX based full adders and are utilized in truncated multipliers which results in area reduction by 1 2 . 6 6 % and delay reduction by 11.68 % with a modest increase in power consumption. This reduction in truncated multipliers contributes towards the design of high performance FIR filters in signal processing applications. ©2024 IEEE.

关键词： FIR filters

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Deep Learning Techniques for Identification of Pneumonia: A CNN Approach

Deep Learning Techniques for Identification of Pneumonia: A ...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： Ruchika Das Debasish Swapnesh Kumar Nayak Chinmayee Priyadarshini Rout Lambodar Jena Tripti Swarnkar Department of Computer Science and Engineering Siksha ‘O’ Anusandhan University Bhubaneswar India Department of Computer Application Nalanda Institute of Technology Bhubaneswar India Department of Data Science Siksha 'O Anusandhan University Bhubaneswar India Department of Computer Application Siksha ‘O’ Anusandhan University Bhubaneswar India

Pneumonia is an inflammatory lung disease that mostly affects the tiny air sacs known as alveoli in human bodies. Pneumonia may be caused by bacteria, viruses, or fungi. Mild to severe symptoms may include a mucus-producing cough and a fever, appals, and difficulty to breath. The approach most usually used to diagnose pneumonia is chest X-ray (CXR) imaging. Images from a CXR screening can be used as an alternative or confirmation method because they are simple and affordable to acquire. Examining the chest X-rays however is a hard task that might be vulnerable to subjective variation. We used a deep learning technique in this investigation. Convolutional neural networks (CNNs) for deep learning were employed in this paper. In his work, the proposed model is applied to the original CXR image dataset and the augmented image dataset. We generated the augmented images from the existing CXR image dataset and deployed VGG-19, a component of the CNN model to create an autonomous Pneumonia detection system. The suggested techniques that have been employed include scaling the image, adding extra information, deep learning using Keras, batch normalization, and finally using weights from the pre-trained Keras model (VGG19). Finding pneumonia sickness is difficult and takes time. In this study, we address the issue and demonstrate how to detect pneumonia disease more quickly and accurately. Our proposed model shows an accuracy of 95% on the augmented CXR image dataset.

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Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation Test

arXiv

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arXiv 2024年

作者： Hedström, Anna Weber, Leander Lapuschkin, Sebastian Höhne, Marina Department of Electrical Engineering and Computer Science TU Berlin Germany Department of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin Germany Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Germany Department of Computer Science University of Potsdam Germany

The Model Parameter Randomisation Test (MPRT) is widely acknowledged in the eXplainable Artificial Intelligence (XAI) community for its well-motivated evaluative principle: that the explanation function should be sensitive to changes in the parameters of the model function. However, recent works have identified several methodological caveats for the empirical interpretation of MPRT. To address these caveats, we introduce two adaptations to the original MPRT—Smooth MPRT and Efficient MPRT, where the former minimises the impact that noise has on the evaluation results through sampling and the latter circumvents the need for biased similarity measurements by re-interpreting the test through the explanation’s rise in complexity, after full parameter randomisation. Our experimental results demonstrate that these proposed variants lead to improved metric reliability, thus enabling a more trustworthy application of XAI methods. © 2024, CC BY-NC-SA.

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