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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： Sheshang Degadwala Dhairya Vyas Ankita Kothari Urmila Khunt Computer Engineering Department Sigma University Vadodara Gujarat The Maharaja Sayajirao University of Baroda Vadodara Gujarat India Dept. of Comp. Sci. & Eng. Krishna School Of Emerging Technology And Applied Research KPGU Vadodara Computer Science Engineering ITM vocational University Vadodara

In India, like in the rest of the world, cancer is a major killer. This research objective is to predict cancer mortality in India, using supervised machine learning methods. Cancer mortality rates in India between 1990 and 2017 are provided by age group, gender, and region using data from the Global Burden of Disease Study. We employ three distinct supervised learning algorithms—linear regression, decision tree regression, and random forest regression—after performing data preprocessing, which includes missing value imputation and feature engineering. Using a variety of criteria, we analyze the effectiveness of these models and conclude that the random forest regression model is superior to the other two. The scope of research is provide a long-term prediction of cancer mortality in India using the best model so it will help health department to work on it. Our research has implications for policymakers and healthcare providers in India, where it may inform efforts to reduce cancer rates and improve cancer care.

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Multi-Center CNN-Based Spine Segmentation from T2W MRI Using Small Amounts of Data

Multi-Center CNN-Based Spine Segmentation from T2W MRI Using...

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IEEE International Symposium on Biomedical Imaging

作者： Satheshkumar Kaliyugarasan Magnhild H. Dagestad Evin I. Papalini Erling Andersen John-Anker Zwart Helena Brisby Hanna Hebelka Ansgar Espeland Kerstin M. Lagerstrand Alexander S. Lundervold Dept. of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Norway Dept. of Radiology MMIV Haukeland University Hospital Norway Dept. of Radiology Haukeland University Hospital Norway Dept. of Clinical Medicine University of Bergen Norway Dept. of Medical Physics and Biomedical Engineering Sahlgrenska University Hospital Sweden Inst. of Clinical Sciences The Sahlgrenska Academy at the University of Gothenburg Sweden Dept. of Clinical Engineering Haukeland University Hospital Norway Faculty of Medicine University of Oslo Dept. of Research and Innovation Division of Clinical Neuroscience Oslo University Hospital Dept. of Orthopaedics Sahlgrenska University Hospital Sweden Dept. of Radiology Sahlgrenska University Hospital Gothenburg Sweden

Segmentation of the spinal tissues on MRI is the basis for quantitative analyses, but time-consuming if done manually. In this work, we construct a pipeline for automatic vertebrae segmentation from T2w MRI scans, assessing performance and generalizability by external validation. Our study used 15 scans from one site (Haukeland University Hospital, HUH) and 10 scans from another (Sahlgrenska University Hospital, SUH). MRI experts manually delineated the vertebral bodies Th12-L5 on all the HUH data and a subset of six scans from SUH. We trained multiple convolutional neural networks, assessing the performance in an experimental design tailored to small-data contexts and also on external data. Our best model achieved a mean Dice score of 0.899. This is comparable to results in the literature, but our system required much less training data. 1 .

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Two-Area Power System with Automatic Generation Control Utilizing PID Control, FOPID, Particle Swarm Optimization, and Genetic Algorithms

Two-Area Power System with Automatic Generation Control Util...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Madan Mohan Sati Dinesh Kumar Akhilesh Singh Mohan Raparthi Faisal Yousef Alghayadh Mukesh Soni Deptt. of Mathematics Graphic Era Hill University Bhimtal India Dept. of Computer Science & Engg. SRM Institute of Science & Technology Ghaziabad UP India Deptt. of Electrical Engg. Uttarakhand Technical University Campus Institute Pithoragarh Software Engineer Alphabet Life Science Dallas Texas USA Computer Science & IT Department College of Applied Sci Almaarefa Univ Riyadh Saudi Arabia School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune India

This study aims to present a feasible LFC (Load Frequency Control) model that integrates physical security limitations, namely GRC, GDB, and TD. The presence of this characteristic guarantees that the design is not only accurate but also capable of being materialized in a physical form. It is suggested that the traditional PI controller be replaced with a PID controller in order to handle the issues that are posed by large-scale power systems that are interconnected. Developing extremely efficient soft computing approaches that are capable of producing the necessary results for both individual and extensive power systems while taking into consideration flaws in modeling is the goal of this endeavor. It is important to get the frequency deviation to zero while still following the rules set by real issues and different working conditions. In order to maintain the electrical flow between the inter-area tie lines that are located within the various control zones at the schedule values that have been specified. In order to achieve optimal levels of performance in both steady state and transient conditions. The purpose of this is to obtain valuable multiple solutions with the intention of future planning and design. It is important to simplify the robust load frequency control design problem by changing it into a low-order proportional integral derivative (PID) control synthesis problem in order to address the practical benefits. This approach is commonly employed in real-world power systems applications.

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Advanced Cryptocurrency Price Prediction: Incorporating Time-Reversed ARIMA and BVIC

Advanced Cryptocurrency Price Prediction: Incorporating Time...

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International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Minh Tuan Luc Roman Senkerik Tran Khanh Dang Dept. of Computer Science Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava-Poruba Czech Repulic Center for Applied Information Technology Ton Duc Thang University Ho Chi Minh city Vietnam Institute for Experiential Technology Van Lang University Ho Chi Minh city Vietnam Faculty of Information Technology School of Technology Van Lang University Ho Chi Minh city Vietnam

ISBN: (数字)9798331507817

ISBN: (纸本)9798331507824

The highly volatile nature of cryptocurrencies, particularly Bitcoin, exhibits extreme price volatility, making accurate price prediction a challenging task for traditional forecasting models. This paper proposes an enhanced ARIMA model, called ARIMA_T, which incorporates time-reversed data and optimizes model selection using the Backward Validated Information Criterion (BVIC). The ARIMA_T model aims to improve forecasting accuracy by capturing both linear and non-linear patterns in the data that standard ARIMA models often overlook. We apply this model to predict Bitcoin prices over short and medium-term horizons, specifically 1-day, 7-days, and 30-days ahead predictions. The dataset spans from April 2013 to February 2024. Experimental results demonstrate that ARIMA_T consistently outperforms the traditional ARIMA model across all forecast horizons, as evidenced by lower Root Mean Square Error (RMSE) values. The ARIMA_T model provides more accurate and robust predictions, particularly for short-term forecasting, making it a valuable tool for navigating the volatile cryptocurrency market. This study also discusses the limitations of both models in handling longer-term predictions and suggests future work in integrating ARIMA_T with machine learning techniques for further improvements.

关键词： Accuracy Uncertainty Navigation Bitcoin Predictive models Data models Robustness Forecasting Root mean square Standards

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Constructing a Cryptocurrency-Price Prediction Model Using Deep Learning 8

Constructing a Cryptocurrency-Price Prediction Model Using D...

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8th International Conference on Engineering and Emerging Technologies, ICEET 2022

作者： Tran, Toai Kim Le, Thanh Thi Tuyet Bui, Thinh Tien Dang, Vang Quang Senkerik, Roman Ho Chi Minh City University of Technology and Education Faculty of Economics No 1 Vo Van Ngan Street Linh Chieu Ward Thu Duc City Viet Nam VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Dept. of Computer Science 17. Listopadu 2172/15 Ostrava-Poruba708 00 Czech Republic Tomas Bata University in Zlin Faculty of Applied Informatics T. G. Masaryka 5555 Zlin76001 Czech Republic

ISBN: (纸本)9781665491068

The purpose of this study is to discover the optimal Deep Learning model for Bitcoin prediction among the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Our empirical results indicate that LSTM is the optimal model for predicting Bitcoin price and trend with the prediction accuracy of 88.9%. Our study serves as a stepping stone for novice cryptocurrency investors and future studies of more advanced and sophisticated algorithms. Finally, given that the ideal model for predicting the price of cryptocurrencies is still a topic of controversy, the findings of this study will serve as a valuable empirical resource for future studies. © 2022 IEEE.

关键词： Forecasting

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Video Manipulation Detection and Localization Using Deep Learning 2

Video Manipulation Detection and Localization Using Deep Lea...

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2nd IEEE International Conference on Advances in Computing, Communication Control and Networking, ICACCCN 2020

作者： Mamtora, Hemal Doshi, Kevin Gokhale, Shreya Dholay, Surekha Gajbhiye, Chandrashekhar Sardar Patel Institute of Technology Dept. of Computer Engineering Mumbai India Sardar Patel Institute of Technology Dept. of Applied Science and Humanities Mumbai India

ISBN: (纸本)9781728183374

Nowadays, several low-cost yet productive video editing software tools have been developed due to which it is essential to check the genuineness of digital videos, before they can be used as legal evidences. Thus, detecting and localizing forgery in videos has become an important research field. In this paper we have proposed a solution to detect and localize spatio-Temporal manipulation in videos using a deep learning approach involving an architecture based on LSTM or Long short-Term memory. Our proposed model is able to perform temporal as well as spatial localization of forgery. The experiments are carried out on the REWIND dataset having 10 original and 10 forged videos. 16 videos are used for training and 4 are used for testing the model. The evaluation of model performance is done at pixel-level, frame-level and video-level for computing the spatial localization, temporal localization and forgery detection accuracy values respectively. © 2020 IEEE.

关键词： Long short-term memory

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On the Way to Realize the 5th Industrial Revolution: Achievements, Challenges and Research Areas

On the Way to Realize the 5th Industrial Revolution: Achieve...

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IEEE Symposium on Network Operations and Management

作者： George Dimirakopoulos Thomas Gutt Hans Ehm Alfred Hoess Konstantina Karathanasopoulou Anna Lackner Germar Schneider dept. of Informatics and Telematics Harokopio Univesity of Athens Athens Greece Infineon Technologies A.G. Munich Germany dept. of Electrical Engineering Media and Computer Science University of Applied Sciences Amberg-Weiden Amberg Germany eesy-innovation GmbH Unterhaching Germany Infineon Technologies Dresden GmbH&Co.KG Dresden Germany

The industrial world is rapidly moving towards its $5^{\text {th}}$ industrial revolution (a.k.a. “Industry5.0”). Industry5.0 is reflected on the digital sovereignty in comprehensively sustainable production, through adopting, extending and implementing $A I$-enabled hardware, as well as $A I$ tools & methods and semiconductors technology across the whole industrial value chain. It is expected that in doing so, manufacturing costs will be decreased, while at the same time, product quality will be increased through AI-enabled innovation, time-to-market will be shortened and user acceptance of versatile technology offerings will be achieved and global supply chains stabilized. The above will, in turn, foster a sustainable development, in an economical, ecological and societal sense and act as enablers for the Green Deal. This paper accordingly discusses on the fundamental research areas relevant to further advancing the digitalized industry, its research and development achievements so far, as well as the challenges confronted, in order to boost industrial competitiveness through interdisciplinary innovations, establishing sustainable value chains and therefore contribute to the Digital Sovereignty.

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Solving the Multi-Allocation p-Hub Median Problem with Stochastic Travel Times: A Simheuristic Approach

Solving the Multi-Allocation p-Hub Median Problem with Stoch...

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Simulation Winter Conference

作者： Niklas Jost Aleksandra Grochala Christin Schumacher Majsa Ammouriova Angel A. Juan Institute of Transport Logistics TU Dortmund University Dortmund Germany Computer Science Department Universitat Oberta de Catalunya Barcelona Spain Dept. of Applied Statistics and Operations Research Universitat Politècnica de València Alcoy Spain

The p-hub median problems (pHMPs) are a well-researched topic within the fields of Operations Research and Industrial Engineering. These problems have been found to have a wide range of practical applications in various areas such as logistics, retailing, and Internet computing. These applications have made pHMPs an important area of study, leading to numerous research efforts aimed at solving different variations of the problem. This paper presents a simheuristic algorithm for solving the uncapacitated version of the pHMP with stochastic travel times. The proposed approach combines simulation with biased-randomized heuristics to generate high-quality solutions quickly. The proposed method is validated by testing it on huge benchmark instances, which include stochastic travel times. The results demonstrate the efficiency of the proposed approach for this particular problem variation. The simulation-optimization approach provides a promising solution to a practical problem that arises in many real-world applications.

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Predictive Control and Production Process Forecasting Under Deterministic Chaos

Predictive Control and Production Process Forecasting Under ...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Vadim Z. Manusov Dmitry V. Orlov Vitaly S. Karmanov Alexander O. Khusnutdinov Dmitry I. Kovalenko Gennady V. Ivanov Dept. of Industrial Power Supply Systems Novosibirsk State Technical University Novosibirsk Russian Dept. of Theoretical and Applied Computer Science Novosibirsk State Technical University Novosibirsk Russian Federation Dept. of Radio Electronics and Radio Engineering Surgut State University Surgut Russian Federation

ISBN: (数字)9781665498043

ISBN: (纸本)9781665498050

Electricity consumption forecasting is an integral part of the workflow in most industries. At the same time, daily load schedules of the mining industry are characterized by deterministic chaotic fluctuations in the electricity consumption curve caused by the technology of the production process. The paper proposes two mathematical forecasting models, one of which is based on the recurrent method, and the second one is based on the vector method. At the same time, the structure of the models allows taking into account the individual features of the operating cycle of the production process, as well as identifying and smoothing the “noise” components of the rank parametric surface of this production process. The Singular Spectrum Analysis method was chosen as the algorithm for preprocessing and smoothing time series of electricity consumption. The method was applied for the first time for a multidimensional array of electricity consumption data. As a result, rank parametric surfaces were plotted, one of which is the initial surface, and the second one is the final surface.

关键词： Schedules Smoothing methods Production Predictive models Data models mathematical models Forecasting

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Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion

arXiv

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arXiv 2025年

作者： Puglisi, Lemuel Alexander, Daniel C. Ravì, Daniele Dept. of Math and Computer Science University of Catania Viale Andrea Doria 6 Catania Italy Centre for Medical Image Computing University College London 90 High Holborn London United Kingdom MIFT Department University of Messina Viale Ferdinando Stagno d’Alcontres 31 Messina Italy

The growing availability of longitudinal Magnetic Resonance Imaging (MRI) datasets has facilitated Artificial Intelligence (AI)-driven modeling of disease progression, making it possible to predict future medical scans for individual patients. However, despite significant advancements in AI, current methods continue to face challenges including achieving patient-specific individualization, ensuring spatiotemporal consistency, efficiently utilizing longitudinal data, and managing the substantial memory demands of 3D scans. To address these challenges, we propose Brain Latent Progression (BrLP), a novel spatiotemporal model designed to predict individual-level disease progression in 3D brain MRIs. The key contributions in BrLP are fourfold: (i) it operates in a small latent space, mitigating the computational challenges posed by high-dimensional imaging data;(ii) it explicitly integrates subject metadata to enhance the individualization of predictions;(iii) it incorporates prior knowledge of disease dynamics through an auxiliary model, facilitating the integration of longitudinal data;and (iv) it introduces the Latent Average Stabilization (LAS) algorithm, which (a) enforces spatiotemporal consistency in the predicted progression at inference time and (b) allows us to derive a measure of the uncertainty for the prediction. We train and evaluate BrLP on 11,730 T1-weighted (T1w) brain MRIs from 2,805 subjects and validate its generalizability on an external test set comprising 2,257 MRIs from 962 subjects. Our experiments compare BrLP-generated MRI scans with real follow-up MRIs, demonstrating state-of-the-art accuracy compared to existing methods. The code is publicly available at: https://***/LemuelPuglisi/BrLP. © 2025, CC BY.

关键词： Magnetic resonance imaging

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