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Bayes-Q-Learning Algorithm in Edge Computing for Waste Tracking

Intelligent Automation & Soft Computing 2023年第5期36卷 2425-2440页

作者： D.Palanikkumar R.Ramesh Kumar Mehedi Masud Mrim M.Alnfiai Mohamed Abouhawwash Department of Computer Science and Engineering Dr.NGP Institute of TechnologyCoimbatore641048India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The major environmental hazard in this pandemic is the unhygienic dis-posal of medical *** wastage is not properly managed it will become a hazard to the environment and *** medical wastage is a major issue in the city,municipalities in the aspects of the environment,and *** efﬁcient supply chain with edge computing technology is used in managing medical *** supply chain operations include processing of waste collec-tion,transportation,and disposal of *** research works have been applied to improve the management of *** main issues in the existing techniques are ineffective and expensive and centralized edge computing which leads to failure in providing security,trustworthiness,and *** over-come these issues,in this paper we implement an efﬁcient Naive Bayes classiﬁer algorithm and Q-Learning algorithm in decentralized edge computing technology with a binary bat optimization algorithm(NBQ-BBOA).This proposed work is used to track,detect,and manage medical *** minimize the transferring cost of medical wastage from various nodes,the Q-Learning algorithm is *** accuracy obtained for the Naïve Bayes algorithm is 88%,the Q-Learning algo-rithm is 82%and NBQ-BBOA is 98%.The error rate of Root Mean Square Error(RMSE)and Mean Error(MAE)for the proposed work NBQ-BBOA are 0.012 and 0.045.

关键词： Binary bat algorithm naïve bayes supply chain edge medical wastage

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Sustainable Drone Surveillance System Using Eternal Vertex Cover and Periodic Charging

Sustainable Drone Surveillance System Using Eternal Vertex C...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Navya Dhawde Nilotpal Chakraborty Mirza Galib Anwarul Husain Baig Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati School of Mathematical and Statistical Sciences Indian Institute of Technology Mandi

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Unmanned aerial vehicles (UAVs), or drones, are transforming surveillance strategies in various fields, including border security and wildlife conservation. Their ability to monitor large, challenging areas in real time has established them as a valuable tool, particularly in scenarios where traditional surveillance methods face operational and logistical limitations. This research focuses on optimizing drone-based surveillance along the International borders, and in wildlife conservation areas, where challenges like illegal crossings, cattle smuggling, and poaching present serious security and economic threats. Leveraging UAVs provides a cost-effective, flexible, and highly deployable solution that overcomes the limitations of static surveillance systems and limited human patrols. We propose an optimized drone-based system designed to enhance operational efficiency, coverage, and adaptability in these complex environments, with potential applications in various sectors requiring comprehensive, real-time surveillance. We propose modeling the problem using the Eternal Vertex Cover problem, a dynamic extension of the classical Vertex Cover problem. Given the NP-hard nature of determining this cover number, our research explores heuristic and algorithmic approaches to achieve an efficient and scalable solution. We also propose an intelligent charging mechanism for the drones for their continuous operation. All proposed approaches are evaluated using randomly generated datasets as long as benchmark datasets.

关键词： Protected areas Surveillance Heuristic algorithms Wildlife Real-time systems Security Vehicle dynamics Environmental management Faces Drones

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An IoT Machine Learning Model-Based Real-Time Diagnostic and Monitoring System 4th

An IoT Machine Learning Model-Based Real-Time Diagnostic and...

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4th International Conference on Machine Intelligence and Signal Processing, MISP 2022

作者： Awotunde, Joseph Bamidele Misra, Sanjay Ajagbe, Sunday Adeola Ayo, Femi Emmanuel Gurjar, Ramchandra Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Computer Science and Communication Ostfold University College Halden Norway Department of Computer Engineering Ladoke Akintola University of Technology Ogbomosho Nigeria Department of Mathematical Sciences Olabisi Onabanjo University Ago-Iwoye Ogun State Nigeria Shri Govindram Seksaria Institute of Technology and Science Indore India

ISBN: (纸本)9789819900848

Internet of things (IoT) and machine learning (ML) have recently changed the paradigm in the area of applications in health-care systems with significant results. The ML-based models enabled with IoT-based systems have greatly expedited the medical diagnosis, real-time monitoring, and treatment in the health-care system, thus reducing the burden of health-care processes and the cost of taking care of patients by caregivers and physicians. Therefore, in this paper, a framework is proposed based on an ML model-enabled IoT-based system for real-time diagnosis of various diseases using data collected from wearable devices and sensors attached within the body of patients. The captured data are stored using an integrated cloud database for further preprocessing and processes using ML-based models. ML model was used to select the most relevant features from the cloud database and thus use the preprocessed data for the diagnosis of various diseases. The findings from the study show that the XGBoost performs reliably better when compared with other existing ML-based models with an accuracy of 95.7%, recall of 95.9%, precision of 96.0%, F1-score of 94.8%, and Receiver Operating Characteristic Curve (ROC) of 94.3%. The results proposed a framework to show that the system can be widely used to diagnose various diseases and can be used in remote areas for better monitoring of patients. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Solar Wind Density Forecasting with U-Net and LSTM-based Neural Networks 8

Solar Wind Density Forecasting with U-Net and LSTM-based Neu...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Iacobelli, Emanuele Fiani, Francesca Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Forecasting changes in solar wind properties accurately is crucial for predicting space weather, as it significantly impacts the majority of space operations and the telecommunication system. To meet this challenge, we introduce an architecture that combines U-Net’s capabilities for segmenting coronal holes from high-resolution sun images with the predictive abilities of Long Short-Term Memory (LSTM) and ConvLSTM models. This architecture predicts solar wind density using sun surface images obtained from the AIA 193 Å dataset (provided by NASA) and historical electron and proton density data from the OMNI and ELM2 datasets (also provided by NASA), covering the entire year 2012. Our findings demonstrate the system’s ability to generate reliable coronal hole segmentation maps and achieve good accuracy in forecasting solar wind density. © 2023 Copyright for this paper by its authors.

关键词： NASA

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Classification of benign and malignant breast lesions in mammograms using dense-unified multiscale attention network and data-efficient image transformers

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The European Physical Journal Special Topics 2025年 1-19页

作者： Paavankumar, S. Karthik, R. Idayachandiran, Gangatharan Sri, Pulaparthi Penchala Deepthi Illakiya, T. School of Computer Science Engineering Vellore Institute of Technology Chennai India Centre for Cyber Physical Systems Vellore Institute of Technology Chennai India Department of Computational Intelligence Faculty of Engineering and Technology School of Computing SRM Institute of Science and Technology Chennai India

Breast cancer is a common and a serious health problem and it is the major cause of morbidity and mortality for women. Early detection of the disease is particularly challenging because abnormalities such as masses and microcalcifications exhibit subtle and diverse characteristics that are often difficult to identify in mammograms. In recent years, advancement in artificial intelligence, particularly deep learning (DL), has shown to improve diagnostic accuracy and early-stage tumor detection. This study aims to improve performance of DL models by considering both masses and microcalcifications in the proposed work to classify breast cancer abnormalities. The proposed work introduces a novel dual-track network that employs a combination of dense-unified multiscale attention fusion (UMAF) track and data-efficient image transformer (DeiT). The DeiT track processes the entire image simultaneously using patch embeddings, enabling them to capture multiscale representations and dependencies across the entire image. Simultaneously, the Dense-UMAF track focuses on extracting localized features while utilizing connectivity of DenseNet architecture to enable effective feature reuse. This approach generates relevant input features through residual connections of varying lengths, thereby effectively addressing the vanishing gradient problem. The UMAF improves feature extraction by capturing multiscale information, resulting in a better representation of the input data. This dual-track architecture is specifically designed to capture the characteristics of mass and calcification abnormalities in mammograms, which display both localized features and global contextual patterns. The proposed network was evaluated on the Curated Breast Imaging Subset of Digital Database for Screening Mammography dataset, obtaining a classification accuracy of 88.69%.

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IoT Wearable Breast Temperature Assessment System 7

IoT Wearable Breast Temperature Assessment System

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Ashreetha, B. Dankan Gowda, V. Anandaram, Harishchander Nithya, B.A. Gupta, Neeraj Verma, Basant Kumar Department of Electronics & Communication Engineering College Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamilnadu Coimbatore India Department of Computer Science School of Applied Sciences Reva University Karnataka Bengaluru India Department of Computer Science and Engineering SGT University Haryana Gurugram India

ISBN: (纸本)9781665464086

Cancer is an undesirable cell with odd characteristic varies from normal cell of the breast tissue. This will develop swiftly and infiltrate surrounding tissue and forms as tumor which happens in both men and women. After lung cancer, breast cancer has become the largest cause of malignancies in women which raise the mortality rate. In this article, Sensor enabled wearable device framework to detect breast temperature abnormalities is suggested. Early work on wearable sensors supported to monitor breast temperature is presented. The concept of a wearable garment with sensor-enabled patches is also considered. Detailed architecture of the proposed framework is explained, and implementation specifics are examined. Later the statistical highlights help to process the sensor-generated breast temperature data are explained. The performance of the proposed framework is examined by taking abnormal and normal patient temperature dataset and the findings are described using attractive figure representations. © 2023 IEEE.

关键词： Internet of things

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Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry 38

Private Algorithms for Stochastic Saddle Points and Variatio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bassily, Raef Guzmán, Cristóbal Menart, Michael The Ohio State University United States Inst. for Mathematical and Comput. Eng. Fac. de Matemáticas Esc. de Ingeniería Pontificia Universidad Católica de Chile Chile Department of Computer Science & Engineering The Ohio State University United States Department of Computer Science University of Toronto Vector Institute Canada

In this work, we conduct a systematic study of stochastic saddle point problems (SSP) and stochastic variational inequalities (SVI) under the constraint of (ϵ, δ)differential privacy (DP) in both Euclidean and non-Euclidean setups. We first consider Lipschitz convex-concave SSPs in the p/q setup, p, q ∈ [1, 2]. That is, we consider the case where the primal problem has an p-setup (i.e., the primal parameter is constrained to an p bounded domain and the loss is p-Lipschitz with respect to the primal parameter) and the dual problem has an q setup. Here, we obtain a bound of O͠(Equation presented) on the strong SP-gap, where n is the number of samples and d is the dimension. This rate is nearly optimal for any p, q ∈ [1, 2]. Without additional assumptions, such as smoothness or linearity requirements, prior work under DP has only obtained this rate when p = q = 2 (i.e., only in the Euclidean setup). Further, existing algorithms have each only been shown to work for specific settings of p and q and under certain assumptions on the loss and the feasible set, whereas we provide a general algorithm for DP SSPs whenever p, q ∈ [1, 2]. Our result is obtained via a novel analysis of the recursive regularization algorithm. In particular, we develop new tools for analyzing generalization, which may be of independent interest. Next, we turn our attention towards SVIs with a monotone, bounded and Lipschitz operator and consider p-setups, p ∈ [1, 2]. Here, we provide the first analysis which obtains a bound on the strong VI-gap of (Equation presented). For p − 1 = Ω(1), this rate is near optimal due to existing lower bounds. To obtain this result, we develop a modified version of recursive regularization. Our analysis builds on the techniques we develop for SSPs as well as employing additional novel components which handle difficulties arising from adapting the recursive regularization framework to SVIs. © 2024 Neural information processing systems foundation. All rights reserved.

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Public-data Assisted Private Stochastic Optimization: Power and Limitations 38

Public-data Assisted Private Stochastic Optimization: Power ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Ullah, Enayat Menart, Michael Bassily, Raef Guzmán, Cristóbal Arora, Raman Meta United States Department of Computer Science & Engineering The Ohio State University United States Department of Computer Science University of Toronto Vector Institute Canada The Ohio State University United States Inst. for Mathematical and Comput. Eng. Fac. de Matemáticas Esc. de Ingeniería Pontificia Universidad Católica de Chile Chile Department of Computer Science Johns Hopkins University United States

We study the limits and capability of public-data assisted differentially private (PA-DP) algorithms. Specifically, we focus on the problem of stochastic convex optimization (SCO) with either labeled or unlabeled public data. For complete/labeled public data, we show that any (Ε, δ)-PA-DP has excess risk Ω˜(min {1/√npub, 1/√n + √d/nΕ}), where d is the dimension, npub is the number of public samples, npriv is the number of private samples, and n = npub + npriv. These lower bounds are established via our new lower bounds for PA-DP mean estimation, which are of a similar form. Up to constant factors, these lower bounds show that the simple strategy of either treating all data as private or discarding the private data, is optimal. We also study PA-DP supervised learning with unlabeled public samples. In contrast to our previous result, we here show novel methods for leveraging public data in private supervised learning. For generalized linear models (GLM) with unlabeled public data, we show an efficient algorithm which, given Õ(nprivΕ) unlabeled public samples, achieves the dimension independent rate Õ(1/√npriv + 1/√nprivΕ). We develop new lower bounds for this setting which shows that this rate cannot be improved with more public samples, and any fewer public samples leads to a worse rate. Finally, we provide extensions of this result to general hypothesis classes with finite fat-shattering dimension with applications to neural networks and non-Euclidean geometries. © 2024 Neural information processing systems foundation. All rights reserved.

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Sinhala Transliteration: A Comparative Analysis Between Rule-based and Seq2Seq Approaches

arXiv

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arXiv 2024年

作者： De Mel, Yomal Wickramasinghe, Kasun de Silva, Nisansa Ranathunga, Surangika Department of Computer Science & Engineering University of Moratuwa Katubedda10400 Sri Lanka School of Mathematical and Computational Sciences Massey University Auckland New Zealand

Due to reasons of convenience and lack of tech literacy, transliteration (i.e., Romanizing native scripts instead of using localization tools) is eminently prevalent in the context of low-resource languages such as Sinhala, which have their own writing script. In this study, our focus is on Romanized Sinhala transliteration. We propose two methods to address this problem: Our baseline is a rule-based method, which is then compared against our second method where we approach the transliteration problem as a sequence-to-sequence task akin to the established Neural Machine Translation (NMT) task. For the latter, we propose a Transformerbased Encode-Decoder solution. We witnessed that the Transformer-based method could grab many ad-hoc patterns within the Romanized scripts compared to the rule-based method. The code base associated with this paper is available on GitHub - https://***/ kasunw22/Sinhala-Transliterator/ Copyright © 2024, The Authors. All rights reserved.

关键词： Neural machine translation

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Enhancing Object Detection Robustness for Cross-Depiction Through Neural Style Transfer 8

Enhancing Object Detection Robustness for Cross-Depiction Th...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Fiani, Francesca Puglisi, Adriano Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Modern neural networks models for computer vision are trained on millions of images. The idea is that models are able to increase generalization when the dataset contains well diversified images, e.g. with varied illumination and environmental conditions of the same objects. Generalization is particularly relevant in object detection, especially for what concerns the cross-depiction problem. In this work we explore the use of Neural Style Transfer as a novel technique to morph the original data, with the aim to enhance model generalization. To verify the effect on performances for object detection models, we selected the Faster R-CNN model to be applied on the Pascal VOC 2012 dataset. A number of tests were performed through style variations on images and by tuning Neural Style Transfer parameters to maintain the content of the original images. The experiments showed promising results, which effectively provide a foundation for future studies on cross-depiction via Neural Style Transfer. © 2023 Copyright for this paper by its authors.

关键词： Object detection

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