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arXiv 2024年

作者： Dütting, Paul Kesselheim, Thomas Lucier, Brendan Reiffenhäuser, Rebecca Singla, Sahil Google Research Zurich Switzerland Institute of Computer Science Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany Microsoft Research New England CambridgeMA United States Institute for Logic Language and Computation University of Amsterdam Netherlands School of Computer Science Georgia Tech AtlantaGA United States

In online combinatorial allocations/auctions, n bidders sequentially arrive, each with a combinatorial valuation (such as submodular/XOS) over subsets of m indivisible items. The aim is to immediately allocate a subset of the remaining items to maximize the total welfare, defined as the sum of bidder valuations. A long line of work has studied this problem when the bidder valuations come from known independent distributions. In particular, for submodular/XOS valuations, we know 2-competitive algorithms/mechanisms that set a fixed price for each item and the arriving bidders take their favorite subset of the remaining items given these prices. However, these algorithms traditionally presume the availability of the underlying distributions as part of the input to the algorithm. Contrary to this assumption, practical scenarios often require the learning of distributions, a task complicated by limited sample availability. This paper investigates the feasibility of achieving O(1)-competitive algorithms under the realistic constraint of having access to only a limited number of samples from the underlying bidder distributions. Our first main contribution shows that a mere single sample from each bidder distribution is sufficient to yield an O(1)-competitive algorithm for submodular/XOS valuations. This result leverages a novel extension of the secretary-style analysis, employing the sample to have the algorithm compete against itself. Although online, this first approach does not provide an online truthful mechanism. Our second main contribution shows that a polynomial number of samples suffices to yield a (2 + ǫ)-competitive online truthful mechanism for submodular/XOS valuations and any constant ǫ > 0. This result is based on a generalization of the median-based algorithm for the single-item prophet inequality problem to combinatorial settings with multiple items. Copyright © 2024, The Authors. All rights reserved.

关键词： Consensus algorithm

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The Impact of Blockchain and AI in the Healthcare

The Impact of Blockchain and AI in the Healthcare

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International Conference on Electrical Power and Energy Systems (ICEPES)

作者： Ramya Thatikonda M. Kempanna Reshmi Tatikonda A. Bhuvanesh PhD in Information Technology University of the Cumberlands Williamsburg USA Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bengaluru Karnataka India Masters in Computer Information Systems University of the Central Missouri Warrensburg USA Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli Tamil Nadu India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

Integrating blockchain and artificial intelligence (AI) in healthcare presents a transformative opportunity. Therefore, this can revolutionize patient care by improving data management and medical research. Decentralization and invulnerability granted by blockchain technology foster improved data consistency and interoperability. AI enhances blockchain by streamlining data examination, automatic tasks, and decision-making processes, resulting in more precise medical diagnoses and tailored therapy programs. The intersection of blockchain and AI is explored, along with their potential benefits and practical applications in healthcare. The document examines the hurdles encountered when integrating innovations, including scalability, interoperability, and regulatory adherence issues. Additionally, the study investigates moral questions surrounding data ownership and patient consent, thereby protecting confidentiality and trust. The synergy between blockchain, AI, and healthcare will lead to transformative advancements. It pledges to fortify data safety and expedite information sharing. Consequently, this will result in an improved, price-effective, and patient-focused healthcare environment. Recommendations were made for additional research and practical implementation in healthcare technology. The study should concentrate on creating scalable and compatible blockchain systems. A vital factor for extensive use is tackling regulatory and moral issues. Hybrid blockchain designs and federated learning strategies will bolster information privacy and utility.

关键词： Technological innovation Ethics Privacy Federated learning Medical treatment Medical services Blockchains

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learning Discrete Latent Variable Structures with Tensor Rank Conditions

arXiv

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arXiv 2024年

作者： Chen, Zhengming Cai, Ruichu Xie, Feng Qiao, Jie Wu, Anpeng Li, Zijian Hao, Zhifeng Zhang, Kun School of Computer Science Guangdong University of Technology China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Department of Applied Statistics Beijing Technology and Business University China Department of Computer Science and Technology Zhejiang University China Department of Philosophy Carnegie Mellon University United States

Unobserved discrete data are ubiquitous in many scientific disciplines, and how to learn the causal structure of these latent variables is crucial for uncovering data patterns. Most studies focus on the linear latent variable model or impose strict constraints on latent structures, which fail to address cases in discrete data involving non-linear relationships or complex latent structures. To achieve this, we explore a tensor rank condition on contingency tables for an observed variable set Xp, showing that the rank is determined by the minimum support of a specific conditional set (not necessary in Xp) that d-separates all variables in Xp. By this, one can locate the latent variable through probing the rank on different observed variables set, and further identify the latent causal structure under some structure assumptions. We present the corresponding identification algorithm and conduct simulated experiments to verify the effectiveness of our method. In general, our results elegantly extend the identification boundary for causal discovery with discrete latent variables and expand the application scope of causal discovery with latent variables. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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IoT-based Efficient Waste Management on University Campus: Enhancing Monitoring and Optimization through machine learning

IoT-based Efficient Waste Management on University Campus: E...

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3rd International Conference on machine learning and Data engineering, ICMLDE 2024

作者： Sumathi, M. Hanumanthappa, J. Santhosh Kumar, K.S. Shiva Prakash, S.P. Meghana, J. Department of Studies in Computer Science University of Mysore Manasagangothri Campus Mysuru Karnataka570006 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering University of Mysore Manasagangothri Campus Mysuru Karnataka570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru Karnataka570006 India Department of Computer Science Mahajana Post Graduate Center Mysuru Karnataka570016 India

The introduction of an IoT-based smart waste management system is proposed to optimize various aspects of university campus waste recycle management processes, such as garbage collection scheduling, waste type identification, and collection routes. This system utilizes advanced data analytics algorithms, wireless communication protocols, and sensors to measure and document variations in trash levels, enabling real-time monitoring of waste accumulation. Through this IoT-based infrastructure, university administrators and waste management staff gain fine-grained visibility and control over garbage collection processes. The implications for sustainability advocacy and environmental stewardship in academic environments are significant. By fostering a culture of environmental awareness and accountability, universities can instill ideals of conservation and resourcefulness in their student populations and staff. The proposed IoT-enabled waste bin system exemplifies the synergy between technological innovation and environmental responsibility, serving as a model for institutions worldwide. Collaboration among universities can leverage IoT technology to drive positive change, mitigate environmental degradation, and promote sustainable practices beyond campus boundaries. Efficient waste management is crucial for nurturing sustainability on college and university campuses, as evidenced by the urgent need for the collection and recycling of compost, general waste, and recyclables. Our models consistently achieve high performance, with perfect scores (1.000) across all split ratios, indicating flawless classification of waste types. Although the artificial Neural Network (ANN) demonstrates slightly lower accuracy, ranging from 0.973 to 0.997 depending on data splits, it still performs well. As a result, Naive Bayes produces less accurate results, typically surpassing 0.93 but falling short of Random Forest, K-Nearest Neighbors (KNN), and Support Vector machine (SVM)'s flawless

关键词： Electronic Waste

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Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks

arXiv

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arXiv 2023年

作者： Chang, Shizhen Kopp, Michael Ghamisi, Pedram Du, Bo The Institute of Advanced Research in Artificial Intelligence Landstraßer Hauptstraße 5 Vienna1030 Austria The Institute of Advanced Research in Artificial Intelligence Landstraßer Hauptstraße 5 Vienna1030 Austria Machine Learning Group Chem-nitzer Str. 40 FreibergD-09599 Germany The National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430079 China

Change detection, an essential application for high-resolution remote sensing images, aims to monitor and analyze changes in the land surface over time. Due to the rapid increase in the quantity of high-resolution remote sensing data and the complexity of texture features, several quantitative deep learning-based methods have been proposed. These methods outperform traditional change detection methods by extracting deep features and combining spatial-temporal information. However, reasonable explanations for how deep features improve detection performance are still lacking. In our investigations, we found that modern Hopfield network layers significantly enhance semantic understanding. In this paper, we propose a Deep Supervision and FEature Retrieval network (Dsfer-Net) for bitemporal change detection. Specifically, the highly representative deep features of bitemporal images are jointly extracted through a fully convolutional Siamese network. Based on the sequential geographical information of the bitemporal images, we designed a feature retrieval module to extract difference features and leverage discriminative information in a deeply supervised manner. Additionally, we observed that the deeply supervised feature retrieval module provides explainable evidence of the semantic understanding of the proposed network in its deep layers. Finally, our end-to-end network establishes a novel framework by aggregating retrieved features and feature pairs from different layers. Experiments conducted on three public datasets (LEVIR-CD, WHU-CD, and CDD) confirm the superiority of the proposed Dsfer-Net over other state-of-the-art methods. Compared to the best-performing DSAMNet, Dsfer-Net demonstrates significant improvements, with F1 scores increasing by 4.7%, 5.9%, and 2.3%. Furthermore, when compared to our previous FrNet, Dsfer-Net also achieves noteworthy enhancements, with F1 scores increasing by 2.0%, 1.4%, and 4.5% on three datasets. Code will be available online (http

关键词： Semantics

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Designing a Smart Healthcare framework based on disease prediction using Optimizer based AI

Designing a Smart Healthcare framework based on disease pred...

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Mysore Sub Section International Conference (MysuruCon), IEEE

作者： Garvit Vyas Jagadevi N Kalshetty Piyush Kumar Pareek Department of Electrical and Electronics Engineering Birla Institute of Technology & Science Pilani - Hyderabad Campus India Department of Computer Science & Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Artificial Intelligence & Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India

In light of the rapid shift towards an ageing population and the corresponding rise in healthcare spending, disease prediction and control through various healthcare devices and services have lately garnered considerable interest. The development of smart healthcare services is a direct result of the advancements in the Internet of Medical Things (IoMT), wearable sensors, and communication technologies that have occurred in this technological era. The Internet of Things (IoT) in healthcare also helps bring down the cost of many different diseases by analysing data quickly and thoroughly. One exciting new area of medical research is the use of machine learning to make illness predictions from patient symptoms. In order to collect signals, the framework makes use of (IoT) sensors, which then transmit them to a web server in the cloud. In order to ascertain the patients' conditions, these signals are further analysed by a deep learning network. By taking both spatial and temporal considerations into account, the given method shortens the time it takes to discover anomalies by minimising the vanishing gradient problem of Recurrent Neural Networks (RNN). Someone in the medical field has access to patients' medical records and the results of any processing that may be necessary in the event of an emergency. Heart Failure Clinical Records, a repository at UCI, provided the dataset utilised in this investigation. It has thirteen characteristics. The suggested model outperformed competing deep learning and ML models in the experiments, which yielded an accuracy value of 0.9417.

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Cross-Store Next-Basket Recommendation

Cross-Store Next-Basket Recommendation

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IEEE International Conference on Data Mining (ICDM)

作者： Liang-Chen Ma Ya Li Zi-Feng Mai Fei-Yao Liang Chang-Dong Wang Min Chen Mohsen Guizani School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi UAE

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Next-basket recommendation (NBR) infers a set of items that a user will interact with in the next basket. Existing methods often struggle with the data sparsity problem, particularly when the number of baskets is significantly large due to diverse user behaviors. Cross-domain recommendation (CDR) can effectively alleviate this problem in NBR by transferring knowledge across different domains. Nevertheless, these methods often rely on the similarities of overlapping users, which leads to the negative transfer problem and ignores the overlapping items that are general in real-world scenarios like chain stores. In this paper, we provide a clear symbolic definition of cross-store recommendation (CSR) and distinguish it from CDR. We also propose a novel CSNBR model for cross-store next-basket recommendation task. To fully model the transferable collaborative information between two stores, we learn the embeddings of users, baskets, and items by two intra-store bipartite graphs, and use an inter-store unified bipartite graph to transfer the previously learned knowledge. Furthermore, to alleviate the negative transfer problem, we propose to reconstruct the inter-store unified bipartite graph by utilizing user embeddings obtained from the transfer layer and the disentanglement layer. We also employ two sequence encoders to model the historical sequential information at basket-level and item-level. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of the CSNBR model.

关键词： Neural networks Collaboration Data models Bipartite graph Data mining

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Detection of Weed plant in Farm and Removing using Unmanned Ground Vehicle

Detection of Weed plant in Farm and Removing using Unmanned ...

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

作者： Sandeep Telkar R Rajesh Yakkundimath Likewin Thomas Thara Kl Department of Artificial Intelligence & Machine Learning PES Institute of Technology and Management Shivamogga and Visvesveraya Technological University Belagavi Karnataka India Department of Computer Science & Engineering KLE Institute of Technology Hubbali and Visvesveraya Technological University Belagavi Karnataka India

India’s agriculture sector has contributed greatly to the country’s economic expansion. Therefore, reducing expenses and raising crop productivity are essential. In a country like India, where there are hundreds of fields, weeds are always growing there. Weeds and crops fight for space, water, food, nutrients, and other resources, which reduces agricultural output. Currently, one of agriculture’s most difficult duties is weed control. Herbicide hand spraying is one of the most popular weed management techniques. However, this strategy has a number of shortcomings. Because manual labor is costly, an automated weed management system may be economically viable. There is currently no technology available that can be utilized in the field in real time to autonomously control in-row weeds, despite repeated attempts. Weed control has become more important as food grain production has increased per person. The suggested system makes an effort to provide a robotic, computer vision-based Unmanned Ground Vehicle (UGV) for real-time weed management in row crops in order to address the aforementioned issues. This tool can recognize weeds and remove them gently without endangering the environment. It would be ideal to achieve crop contact without the involvement of humans using IoT, machine learning, and picture analysis.

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Vehicle Prediction for BUS Identification Output from our Route Testing Real Time Algorithm

Vehicle Prediction for BUS Identification Output from our Ro...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： M. Misba R. Ramya Joel Dickson L. Sharmila J. Kavitha K. Udayakumar Department of Artificial Intelligence and Machine Learning Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Artificial Intelligence and Data Science St. Joseph's College of Engineering Chennai Bethlehem Institute of Engineering Karungal Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Traffic generation in Indian cities is of a mixed nature, comprising over a dozen types of vehicles that can be broadly categorized into slow-moving and fast-moving vehicles. This study highlights the challenges associated with the inefficient operation of the existing bus system. GPS and mobile networks form integral components of the current system, enabling data storage and management using PostgreSQL. The system provides robust and reliable data to fulfill requests from other software applications. Operating on cloud platforms, it is compatible with all major web browsers. The proposed algorithm continuously tracks the real-time location of buses and their corresponding WMS (Web Map Service) location, alerting users through marker notifications. This paper also explores measures to evaluate the efficiency and effectiveness of bus services. Recognizing that an inadequate public transport system impedes economic growth and development, this study emphasizes the need to enhance the overall quality and accessibility of public transportation. The findings aim to improve the operational efficiency of bus services, contributing to a more effective and sustainable transport system.

关键词： Urban areas Software algorithms Memory Prediction algorithms Real-time systems Software Maintenance Software reliability Public transportation Testing

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Advancing Vascular Dementia Diagnosis Through Salp Swarm Optimized Convolutional Neural Network

Advancing Vascular Dementia Diagnosis Through Salp Swarm Opt...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Nivedita Manohara Mathkunti U Ananthanagu Vidya N Abdulpur D. Mangaiyarkarasi Anitha S Department of Computer Science & Engineering Alliance School of Advanced Computing Alliance Univerisity Bengaluru Karnataka India Department of Electrical Engineering Dr.D.Y Patil Pratishthan's College of Engineering Kolhapur Maharastra India Computer Science & Engineering Vel Tech Rangarajan Dr.Sangunthala R&D Institute of Science and Technology Avadi India Department of Artificial Intelligence & Machine Learning Sri Sairam College of Engineering Anekal (VTU) Bengaluru Karnataka India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Vascular Dementia (VD) is a form of cognitive impairment seen in individuals, restricting from problems with blood vessels supplying the brain. These issues lead to strokes or reduced blood flow and gradually lead to the development of vascular dementia. Since it tends to progress slowly, diagnosis often comes after significant brain damage making prevention is crucial. Early identification of risk factors are vital to mitigate the risk of irreversible cognitive decline associated with vascular dementia. Accordingly, this research proposes an optimized neural network model for predicting vascular dementia at its earlier to improve the quality of life. The initial stage in this research involves preprocessing, where the input data is cleaned and normalized. These steps ensure that the vascular dementia dataset is cleaned of inconsistencies and prepared with normalized features for accurate analysis and modeling. Following preprocessing, relevant data features are selected using the chi-square approach, making the classification task easier. Finally, classification is performed utilizing a Convolutional Neural Network (CNN) and for fine-tuning of CNN parameters Salp Swarm Optimization (SSO) Algorithm is implemented. The execution of proposed work is processed using Python software and the outcome reveals superior performance in terms of accuracy and precision. Thereby the proposed method provides better performance with an accuracy of 94.5%, contrasted to existing methods in predicting VD.

关键词： Accuracy Software algorithms Data preprocessing Predictive models Software Classification algorithms Convolutional neural networks Biological neural networks Dementia Python

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