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arXiv 2024年

作者： Wang, Siyi Wang, Zifan Yi, Xinlei Zavlanos, Michael M. Johansson, Karl H. Hirche, Sandra Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Mechanical Engineering and Material Science Duke University DurhamNC27708 United States

Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time. In such cases, it is favorable to adopt a strategy that minimizes the negative impact of change to avoid potentially risky situations. In this paper, we investigate risk-averse online optimization where the distribution of the random cost changes over time. We minimize risk-averse objective function using the Conditional Value at Risk (CVaR) as risk measure. Due to the difficulty in obtaining the exact CVaR gradient, we employ a zeroth-order optimization approach that queries the cost function values multiple times at each iteration and estimates the CVaR gradient using the sampled values. To facilitate the regret analysis, we use a variation metric based on Wasserstein distance to capture time-varying distributions. Given that the distribution variation is sub-linear in the total number of episodes, we show that our designed learning algorithm achieves sub-linear dynamic regret with high probability for both convex and strongly convex functions. Moreover, theoretical results suggest that increasing the number of samples leads to a reduction in the dynamic regret bounds until the sampling number reaches a specific limit. Finally, we provide numerical experiments of dynamic pricing in a parking lot to illustrate the efficacy of the designed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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AUTO-IceNav: A Local Navigation Strategy for Autonomous Surface Ships in Broken Ice Fields

arXiv

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arXiv 2024年

作者： de Schaetzen, Rodrigue Botros, Alexander Zhong, Ninghan Murrant, Kevin Gash, Robert Smith, Stephen L. Department of Computer Science and Operations Research Université de Montréal MontréalQC Canada Mila - Quebec AI Institute MontréalQC Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada National Research Council Canada

Ice conditions often require ships to reduce speed and deviate from their main course to avoid damage to the ship. In addition, broken ice fields are becoming the dominant ice conditions encountered in the Arctic, where the effects of collisions with ice are highly dependent on where contact occurs and on the particular features of the ice floes. In this paper, we present AUTO-IceNav, a framework for the autonomous navigation of ships operating in ice floe fields. Trajectories are computed in a receding-horizon manner, where we frequently replan given updated ice field data. During a planning step, we assume a nominal speed that is safe with respect to the current ice conditions, and compute a reference path. We formulate a novel cost function that minimizes the kinetic energy loss of the ship from ship-ice collisions and incorporate this cost as part of our lattice-based path planner. The solution computed by the lattice planning stage is then used as an initial guess in our proposed optimization-based improvement step, producing a locally optimal path. Extensive experiments were conducted both in simulation and in a physical testbed to validate our approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Marine navigation

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Optimizing Heart Failure Predictive Accuracy: An Effective Approach Using SMOTE Techniques

Optimizing Heart Failure Predictive Accuracy: An Effective A...

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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Ahmed Baber Faizan Ahmed Attique Ur Rehman Sabeen Javaid Menwa Alshammeri Azka Mir Deepak Kumar Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science College of Computer and Information Sciences Al Jouf University Saudia Arabia Division of Research and Development Lovely Professional University Phagwara KKR and KSR institute of technology and sciences Guntur Andhra Pradesh

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Heart failure occurs when the heart's lower cham-bers (ventricles) weaken and can't pump blood effectively. The heart contains 2 ventricles that are left and right. When left ventricular failure occurs, it causes shortness of breath and fatigue, and when right ventricular failure occurs, it causes peripheral and abdominal fluid accumulation. Heart failure is a disorder in breathing and oxygen supply to the whole body. As we know, Heart is responsible for circulating blood all over the body parts. When it stops pumping, blood can no longer circulate effectively causing fluid to build up in the lungs causing difficulty in breathing and in the legs, causing swelling. The fluid buildup can cause shortness of breath and swelling of the legs and feet. As a result, fluctuation in breathing takes place and, in the end, the patient dies. This study investigates the use of machine learning tools, like k-Nearest Neighbors, Decision Trees, Naïve Bayes, and Random Forests, to identify heart failure. We are proposing a model for Heart Failure Prediction using a machine learning algorithm and a dataset to determine the performance in terms of accuracy. As a result, the Random Forest classifier along with the SMOTE operator provides the highest accuracy of 91.12%, precision of 87.88%, recall of 95.39%, and F1 measure of 91.48% among the models considered. This research is a very important contribution to healthcare for effective predictions of this disease, as the number of patients is increasing day by day. The results hold significance for future modifications too in medical diagnostics and helping material for more accurate detection methodologies.

关键词： Heart Legged locomotion Accuracy Fluids Machine learning algorithms Pumps Nearest neighbor methods Cardiovascular diseases Random forests Blood

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Histopathology Image Analysis Utilizes Homology-Based Feature Extraction for Accurate Diagnosis of Various Lung Diseases

Histopathology Image Analysis Utilizes Homology-Based Featur...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Haider Alabdeli N Subhash Chandra Sowmya M R. Bhavani Bura Vijay Kumar The Islamic University Najaf Iraq CVR College of Engineering Telangana India Department of AI and DS Nitte Meenakshi Institute of Technology Bengaluru India Sathyabama Institute of Science and Technology Chennai India School of Computer Science and Artificial Intelligence SR University Warangal India

Histopathology image analysis is pivotal for diagnosing lung diseases, providing critical insights into tissue abnormalities for accurate medical assessments. Despite its significance, challenges arise from interpretational subjectivity and handling diverse tissue types, limiting its widespread application. The lung and colon histopathology dataset, comprising 25,000 color images, serves as a valuable resource in comprehensive medical research, offering diverse samples for detailed analysis and diagnosis. The proposed method introduces innovation by leveraging homology-based image processing for feature extraction, integrating the Unsharp Masking method in preprocessing. Classification through a Convolutional Neural Network (CNN) ensures a thorough and accurate analysis of histopathological images in lung disease diagnosis. Comparative analysis with existing approaches like 2D wavelet features and CRCNN-net underscores the undeniable superiority of the proposed method. The results demonstrate exceptional performance metrics, with an accuracy of 98.67%, precision at 93.70%, an F1-score of 80.22%, recall reaching 89.76%, and specificity standing at 90.82%. These outcomes affirm the proposed method's efficacy in advancing the field of histopathological lung disease diagnosis.

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Energy-Efficient Transmission in Wireless Networks Using Resnet with Gravitational Search Algorithm

Energy-Efficient Transmission in Wireless Networks Using Res...

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Disruptive Technologies (ICDT), International Conference on

作者： Rajakumari S Kantharaju HC Shadab Ahmad Rupa Rani Sharma S. Sudha T Ramesh Department of Mathematics R.M.D. Engineering College Department of AI & ML Vemana Institute of Technology Bengaluru Karnataka India Department of Electrical and Computer Engineering Samara University Afar region Ethiopia Department of Applied Science G.L Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh India Department of ECE Sri Ranganathar Institute of Engineering and Technology Coimbatore Tamilnadu India Department Of Computer Science and Engineering R.M.K Engineering College

Although wireless networks are extremely important in modern communication, one of the most significant challenges they face is the quantity of energy that they use. Within this study, a new way of sending data over wireless networks is suggested. The gravitational search algorithm (GSA) and residue networks of the ResNet type are used together in this method. By using wireless networks, the main goal of this method is to find answers to problems that come up with using energy more efficiently. The creation and use of a hybrid ResNet-GSA model for wireless communication is one of the most important parts of our method. Because it follows the rules of gravity, GSA can quickly look through the solution space to find the right parameter values. At the same time, the network works better when the deep learning features of ResNet are used. The results of our research show that there are big improvements in how much energy is used compared to using traditional ways. If you want to cut down on the amount of energy used, the ResNet-GSA strategy that was suggested also works well. The findings demonstrate that optimisation techniques and deep learning methodologies are not only compatible with one another but also perform effectively together to make wireless networks more environmentally friendly.

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An Analysis of Smart Clinical Trial Investigation Methods in Research Medicine Industries

An Analysis of Smart Clinical Trial Investigation Methods in...

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International Conference on Cognitive Computing and Information Processing

作者： Sonia Maria D'Souza Suchethana H C Harshan KG Nanda Ashwin Veeresh KM Praveen Murthy K S Department of AI&ML NHCE Bangalore Department of Information Science and Engineering JNNCE Shimoga Department of Computer science Engineering JSS Academy of Technical Education Noida Department of Information Science and Engineering East Point College of Engineering Department of Computer Science and Engineering HKBK College of Engineering Bangalore India Department of Information Science and Engineering JSSSTU Mysuru

ISBN: (数字)9781665456487

ISBN: (纸本)9781665456494

Research is being done to see if it is possible to preserve the bodies of patients who have died in the absence of a cure and keep them alive after a cure is found. Researchers say that their bodies can be preserved by freezing and treated and brought back to life when a cure for the disease is found. They conduct this research on human and animal bodies by processing the bodies at minus 1% degree temperature with the help of nitrogen. It is questionable whether this experiment will be successful in the presence of nitrogen gas. However, they hope for success in the future. Studies have found that patients who undergo surgery at night are more likely to die without treatment than patients who undergo surgery during the day. In this paper a smart analysis of research medicine industries are evaluated with the help of clinical trial investigation methods. Clinical research is defined as a quality improvement process that seeks to improve patient care and outcomes by systematically reviewing care against defined criteria and implementing change. In a cut-off point, the proposed model reached 99.56% of cross sectional calculations, 97.52% of population computation, 95.18% of Event controlled modelling, 97.53% of clinical trial reports and 96.34% of confounding factors.

关键词： Industries Computational modeling Sociology Surgery Clinical trials Safety Nitrogen

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Enabling Mmwave Connectivity in 5G Base Stations: Key Requirements Overview

Enabling Mmwave Connectivity in 5G Base Stations: Key Requir...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Nikhil Garg Rahul Vishnoi Bharat Singh K. Ranjith Singh S. Kannimuthu Monali Ravindra Borade Department of Civil Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of ECE TMU School of COMPUTER SCIENCE AND APPLICATIONS IIMT University Meerut Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Information Technology Karpagam College of Engineering Coimbatore AI & Ds Vishwakarma Institute of Information Technology Pune

The search for novel solutions to allow the era networks because the various difficulties be solved before connections in wireless networks can be fully characterized. Many essential components to facilitate communications are included in this study. The explanation of channel information on signal caused by air gases, rain, and open space propagation. It is detailed how the design works. The blocking effect in caused by penetration loss is given, along with potential solutions. It is suggested to use as well as to use it backhaul. Introduction Throughout the course of multiple generations of development, e seen enormous technological advancements and economic success. The term “s” describes the most fundamental analogue voice-only systems using brick phones s. phone use has skyrocketed as a result of revolutionary totally digital networks. offered, and voice quality had significantly improved. surfing are examples data applications that may be offered.

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Predictive Prescription of Unit Commitment Decisions Under Net Load Uncertainty

Predictive Prescription of Unit Commitment Decisions Under N...

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Innovative Smart Grid Technologies (ISGT)

作者： Ogun Yurdakul Feng Qiu Sahin Albayrak Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Energy Systems Division Argonne National Laboratory Lemont IL USA

To take unit commitment (UC) decisions under uncertain net load, most studies utilize a stochastic UC (SUC) model that adopts a one-size-fits-all representation of uncertainty. Disregarding contextual information such as weather forecasts and temporal information, these models are typically plagued by a poor out-of-sample performance. To effectively exploit contextual information, in this paper, we formulate a conditional SUC problem that is solved given a covariate observation. The presented problem relies on the true conditional distribution of net load and so cannot be solved in practice. To approximate its solution, we put forward a predictive prescription framework, which leverages a machine learning model to derive weights that are used in solving a reweighted sample average approximation problem. In contrast with existing predictive prescription frameworks, we manipulate the weights that the learning model delivers based on the specific dataset, present a method to select pertinent covariates, and tune the hyperparameters of the framework based on the out-of-sample cost of its policies. We conduct extensive numerical studies, which lay out the relative merits of the framework vis-à-vis various benchmarks.

关键词： Radio frequency Training Costs Uncertainty Stochastic processes Weather forecasting Predictive models

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Analyzing The Effect of Edge Computing on Real-Time Data Processing and Latency Reduction

Analyzing The Effect of Edge Computing on Real-Time Data Pro...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Sampath T Amit Dutt Vivek Veeraiah BK aishwarya Karu Lal Dhiraj Kapila School of CS & AI SR University Warangal Telangana India Lovely Professional University Phagwara Department of Computer Science Sri Siddhartha Academy of Higher Education Tumkur Karnataka India Institute of Aeronautical Engineering Hyderabad Ohio National Financial Services USA Department of Computer Science & Engineering Lovely Professional University Phagwara Punjab India

The concept of edge computing is a fresh approach that has garnered notable attention in recent times, primarily driven by the use of the Internet of Thingsand the requirement for instantaneous data processing and examination. Edge computing technology offers various benefits, including diminished latency, enhanced efficiency, and heightened dependability. Nonetheless, it also introduces a range of obstacles, such as the safeguarding of security and the management of dispersed computational assets. Edge computing minimizes latency and cuts down on bandwidth expenditures in the realm of Health Information Technology by relocating a portion of the data processing and analytical tasks from the central server or cloud to the periphery of the network, precisely where data originates or is utilized. This approach ensures that only indispensable or pertinent data is dispatched to the cloud or server, while the remaining data is either managed locally or disregarded. As a result, the volume of data requiring transmission is reduced, leading to bandwidth preservation and heightened swiftness. Furthermore, edge computing facilitates real-time or nearly real-time data processing and analysis, contributing to latency reduction and overall improved responsiveness. Edge gateways function as intermediaries connecting edge devices and cloud servers, aiding in data routing and supplying supplementary processing capabilities whenever required. Cloud servers furnish extra computational potency and storage aptitude, affording enterprises the ability to handle and scrutinize substantial data quantities instantaneously. Edge computing architecture latency reduction accuracy. The main contribution of the study is to analyze the effects of edge computing on latency reduction and real-time data processing.

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An Experimental Analysis on Mining Proportional Process Models from Process Logs

An Experimental Analysis on Mining Proportional Process Mode...

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IEEE/ACM International Symposium on Big Data Computing (BDC)

作者： Joo-Eun Bae Jin-Hyong Lee Young-In Park So-Hyang Park Kyoung-Sook Kim Kwanghoon Pio Kim Data & Process Engineering Research Laboratory Divison of AI Computer Science and Engineering College of Software & Management Kyonggi University Suwon-si Republic of Korea

In this paper, we carry out an experimental analysis to show how much perfectly the conceptual mining framework is working on mining process patterns and their enacted proportions from the business process enactment event histories logged with the IEEE-XES standardized format. In principle, the framework must be able to properly handle all the process patterns based upon the four types of control-flow primitives, such as linear (sequential), disjunctive (selective), conjunctive (parallel), and iterative (loop) patterns, together with the execution proportions. The paper focuses on not only verifying the conceptual feasibility of the procedural process mining framework through a series of experimental activities by using the implemented algorithmic mining framework and system, but also proving the functional correctness of the implemented process mining framework by applying to the real process instance enactment event histories of 10,000 workcases instantiated from the Large Bank Transaction Process Model and visualizing the details of the analytical artifacts and results, as well.

关键词： Analytical models Visualization Computational modeling Process control Big Data Knowledge discovery Data models

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