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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Joshi, Shivani Kumar, Rajiv Rai, Vipin Dwivedi, Avinash Tripathi, Vikash Chandra G L Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India G L Bajaj Institute of Technology and Management Nvidia Ai and Learning & Research Center Department of Applied Computational Science and Engineering Greater Noida India Galgotias University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350383522

The complexity and seriousness of diseases in ICU patients, though, make it difficult for medical practitioners to identify high-risk patients who may face irreversible harm. With ANN (Artificial Neural Network), this study will provide an ICU admission prediction model to help identify high-risk patients at an earlier stage. After collecting and analyzing extensive data from various perspectives including demographics, medical history, vital signs, test results and treatment modalities, exploratory data analysis has been conducted. These are used as markers on how statistical techniques and machine learning algorithms can be used to predict mortality among critically ill patients. Consequently, the parameters are designed within the ANN model to create a predictive model that will be able to effectively classify those at higher risk. Advanced machine learning algorithms together with modern tools such as deep learning techniques will aid in the prompt analysis of complex information for accurate forecasts by this predictive model. The ANN model's training set will include a large dataset of ICU patients so that it can become reliable and valid. This makes the predictive model developed capable of accurately identifying high risk cases expeditiously © 2024 IEEE.

关键词： accuracy Artificial Neural Networks (ANN) exploratory data analysis high-risk patients ICU patient admission medical technology mortality outcomes periodic updates prediction model

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Approximation Schemes for Geometric Knapsack for Packing Spheres and Fat Objects 51

Approximation Schemes for Geometric Knapsack for Packing Sph...

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Acharya, Pritam Bhore, Sujoy Gupta, Aaryan Khan, Arindam Mondal, Bratin Wiese, Andreas Department of Mathematics Indian Institute of Science Education and Research Pune India Department of Computer Science and Engineering Indian Institute of Technology Bombay India Department of Computer Science and Automation Indian Institute of Science Bengaluru India Department of Mathematics Technical University of Munich Germany

ISBN: (纸本)9783959773225

We study the geometric knapsack problem in which we are given a set of d-dimensional objects (each with associated profits) and the goal is to find the maximum profit subset that can be packed non-overlappingly into a given d-dimensional (unit hypercube) knapsack. Even if d = 2 and all input objects are disks, this problem is known to be NP-hard [Demaine, Fekete, Lang, 2010]. In this paper, we give polynomial time (1 + Ε)-approximation algorithms for the following types of input objects in any constant dimension d: disks and hyperspheres, a class of fat convex polygons that generalizes regular k-gons for k ≥ 5 (formally, polygons with a constant number of edges, whose lengths are in a bounded range, and in which each angle is strictly larger than π/2), arbitrary fat convex objects that are sufficiently small compared to the knapsack. We remark that in our PTAS for disks and hyperspheres, we output the computed set of objects, but for a OΕ(1) of them we determine their coordinates only up to an exponentially small error. However, it is not clear whether there always exists a (1 + Ε)-approximate solution that uses only rational coordinates for the disks’ centers. We leave this as an open problem which is related to well-studied geometric questions in the realm of circle packing. © Pritam Acharya, Sujoy Bhore, Aaryan Gupta, Arindam Khan, Bratin Mondal, and Andreas Wiese.

关键词： Approximation algorithms

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Layered Division Multiplexing in 5G NR 2

Layered Division Multiplexing in 5G NR

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2nd International Conference for Advancement in technology, ICONAT 2023

作者： Shanbhag, Jayaram Malarvizhi, S. Krithiga, S. Singh, Supragya Vikram Pushilal, Ritisha Sinha, Suvodeep Saibal Mukherjee, Satyaki Samsung Research and Development Bangalore India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur India SRM Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur India

ISBN: (纸本)9781665475174

This research work presents the implementation of Layered Division Multiplexing (LDM) in 5G New Radio. Layered-Division-Multiplexing (LDM) is basically a non orthogonal multiplexing technique which allows the data transmission in different layers with various power levels. The LDM transmitter has been modelled with two layers namely the Core layer (CL) and Enhanced layer(EL) with two different modulation schemes and various injected power level. The data streams are superimposed at different layers to form a multilayer signal with distinct features of each layer. This work analyses the BER performance and constellation diagram at the receiver for various channel models such as Additive White Gaussian Noise (AWGN) and Rayleigh. The proposed system has been implemented in MATLAB simulink and simulation results are also obtained exhibiting improvement in the system performance © 2023 IEEE.

关键词： White noise

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Corona Virus Chest CT Scan Classification Using Deep Learning 1

Corona Virus Chest CT Scan Classification Using Deep Learnin...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Ishwarya, K. Arunadevi, R. Nandhini, G. Gurumoorthy, Sasikumar Abinaya, K. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India J.J. College of Engineering and Technology Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350323795

People can perceive an occasion from this project investigates how well profound learning models prepared on chest CT pictures can analyze Coronavirus tainted individuals in a quick and mechanized process. To this end, we embraced progressed profound organization structures and proposed a consecutive convolutional brain organization to accomplish the best presentation. We led broad arrangements of tests on two CT picture datasets, specifically, the SARS-CoV-2 CT-filter and the Coronavirus CT. The outcomes illustrate predominant exhibitions for our models contrasted and past investigations. Our best models accomplished normal exactness, accuracy, review, and F1-score upsides of 99.2%, 93.9%, 92.0%, 92.9. For better understandable of the outcomes, applied perception procedures to give graphic clarifications to models' expectations. Include perceptions of the learned elements show all around isolated bunches addressing CT pictures of Coronavirus and non-Coronavirus cases. Additionally, the representations demonstrate that our models are equipped for recognizing Coronavirus cases as well as give precise confinement of the Coronavirus related locales, as shown by thoroughly prepared radiologists. © 2023 IEEE.

关键词： Coronavirus

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NEAT Wings: Algorithmic Strategy for Intelligent Flappy Bird Navigation

NEAT Wings: Algorithmic Strategy for Intelligent Flappy Bird...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Jogekar, Ravindra Kadu, Mayur Patel, Bhavarth Rai, Jaskaran Kaur Kawale, Tejas Sapolia, Karan Thakur, Priyanka S. B. Jain Institute of Technology Management & Research Department of Computer Science and Engineering Maharashtra Nagpur441501 India

ISBN: (纸本)9798350348460

This research explores the application of the NeuroEvolution of Augmenting Topologies (NEAT) algorithm in training an artificial intelligence (AI) model to play the popular mobile game Flappy Bird. The primary objective is to investigate the effectiveness of NEAT in enabling an AI agent to learn and adapt to the dynamic challenges presented by the game environment. The Flappy Bird game serves as an ideal testing ground for evaluating the adaptability and learning capabilities of the NEAT *** methodology involves implementing the NEAT algorithm within the game framework, allowing the AI to learn from scratch without prior knowledge of the game dynamics. Various parameters and configurations are fine-tuned to optimize the learning process. The results indicate the AI's ability to navigate through the game obstacles and improve its performance over *** addition to detailing the NEAT algorithm and its application in the Flappy Bird context, this paper provides insights into the learning process, challenges encountered during implementation, and a discussion on the obtained results. Comparative analyses with other AI approaches highlight the unique advantages and contributions of the NEAT algorithm in the domain of gaming *** findings contribute to the growing body of research on AI in gaming and showcase the potential of evolutionary algorithms, specifically NEAT, in training agents for complex and dynamic environments. The implications of this research extend beyond the realm of gaming, offering valuable insights into the adaptability of evolutionary algorithms in real-world scenarios. © 2024 IEEE.

关键词： DQN evolutionary fitness flappy bird NEAT NeuroEvolution of Augmenting Topologies

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GUARDEV: Guided Unified Adaptive Response for Defending Electric Vehicles 27th

GUARDEV: Guided Unified Adaptive Response for Defending Elec...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Mondal, Koustav Kumar Das, Debasis Inter-Disciplinary Research Division - IoT and Applications Indian Institute of Technology Jodhpur Rajasthan Jodhpur India Department of Computer Science Engineering Indian Institute of Technology Jodhpur Rajasthan Jodhpur India

ISBN: (纸本)9783031783821

In the rapidly evolving landscape of electric vehicles (EVs), integrating the Internet of Vehicles (IoV) has opened up new dimensions in automotive technology. While enhancing vehicle performance, safety, and security, this advancement also exposes EVs to heightened cyber threats. To address this critical concern, we introduce GUARDEV: Guided Unified Adaptive Response for Defending Electric Vehicles, a novel Machine Learning (ML)-enhanced conflict resolution-based ensemble Intrusion Detection System (IDS) architecture. GUARDEV leverages the power of adaptive ML techniques to provide a unified and guided response in safeguarding EVs against sophisticated cyber-attacks. Our framework evaluates the effectiveness of three prominent ML models - XGBoost, Random Forest, and Extra Trees - in detecting specific categories of cyber threats. By harnessing the prediction confidence scores from these models, GUARDEV offers a nuanced and intelligent approach to recognizing and thwarting diverse cyber-attacks. Our research, grounded in rigorous experimentation using public IoV security datasets, demonstrates GUARDEV’s exceptional accuracy of 99.47% in intrusion detection. This remarkable achievement not only underscores the potential of ML in enhancing vehicular cybersecurity but also establishes GUARDEV as a powerful tool for creating safer, more secure EVs. GUARDEV paves the way for robust protection of internet-connected electric vehicles in today’s digital age by providing a guided, unified, and adaptive response to cyber threats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Network intrusion

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A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization 27

A First-Order Multi-Gradient Algorithm for Multi-Objective B...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Ye, Feiyang Lin, Baijiong Cao, Xiaofeng Zhang, Yu Tsang, Ivor W. Department of Computer Science and Engineering Southern University of Science and Technology China Australian Artificial Intelligence Institute University of Technology Sydney Australia The Hong Kong University of Science and Technology Guangzhou China School of Artificial Intelligence Jilin University China Shanghai Artificial Intelligence Laboratory China Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781643685489

In this paper, we study the Multi-Objective Bi-Level Optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level subproblem is for scalar optimization. Existing gradient-based MOBLO algorithms need to compute the Hessian matrix, causing the computational inefficient problem. To address this, we propose an efficient first-order multi-gradient method for MOBLO, called FORUM. Specifically, we reformulate MOBLO problems as a constrained multi-objective optimization (MOO) problem via the value-function approach. Then we propose a novel multi-gradient aggregation method to solve the challenging constrained MOO problem. Theoretically, we provide the complexity analysis to show the efficiency of the proposed method and a non-asymptotic convergence result. Empirically, extensive experiments demonstrate the effectiveness and efficiency of the proposed FORUM method in different learning problems. In particular, it achieves state-of-the-art performance on three multi-task learning benchmark datasets. © 2024 The Authors.

关键词： Optimization algorithms

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A stacking ensemble approach for identification of informative tweets on twitter data

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International Journal of Information technology (Singapore) 2023年第5期15卷 2651-2662页

作者： Dasari, Siva Krishna Gorla, Srinivas Prasad Reddy, P.V.G.D. Department of Computer Science and Engineering GMR Institute of Technology GMRIT Rajam and Research Scholar Andhra University Visakhapatnam India Department of Computer Science and Engineering GITAM Deemed to Be University Visakhapatnam India Department of Computer Science and Systems Engineering Andhra University Visakhapatnam India

In the Present era, social media information plays an impact on our daily activities. Accurate media information identification is also challenging because of fake or spam information. Social media may receive this information via Twitter, Facebook, Youtube, LinkedIn, etc. In the context of natural disasters, people share their opinions via social media. Identifying such tweets which are informative or non-informative via these social media is a challenging issue. This work focuses on when a natural disaster happens and how social media information has been categorized, such as informative or non-informative. In this paper, we collected seven datasets from various disaster events from crisisNLP and categorized the tweet information using the stacking ensemble approach. In the proposed work, we considered ensemble stacking approach, in which for base learner we chosen the top five best-performed machine learning models where as for meta-learner we chosen the logistic regression. We tested our stacking model performance with matrices, namely accuracy, precision, recall, and F1 score. We also compared our approach results with other state-of-the-art models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： CrisisNLP embedding Machine Learning Stacking Tweets Vectorization

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Periodic poling of single crystal thin film barium titanate-on-insulator

Periodic poling of single crystal thin film barium titanate-...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Aashna, Pragati Lin, Hong-Lin Cao, Yu Gao, Yuan Mohanraj, Sakthi Sanjeev Zhu, Di Danner, Aaron Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore Department of Material Science and Engineering National University of Singapore 117575 Singapore Institute of Materials Research and Engineering Agency for Science Technology and Research 138634 Singapore

ISBN: (纸本)9781957171395

We report, for the first time, electric field periodic poling of single-crystal thin film barium titanate grown using pulsed-laser-deposition on dysprosium scandate substrate. Uniform domains with periods 5-7 μm are obtained and imaged using piezo-response-force-microscopy. CLEO 2024 © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Barium titanate

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Real-time programmable metasurface for terahertz multifunctional wave front engineering

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Light(science & Applications) 2023年第9期12卷 1795-1806页

作者： Feng Lan Luyang Wang Hongxin Zeng Shixiong Liang Tianyang Song Wenxin Liu Pinaki Mazumder Ziqiang Yang Yaxin Zhang Daniel M.Mittleman Sichuan THz Communication Technology Engineering Research Center School of Electronic Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 611731China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou 313000China Zhangjiang Laboratory Shanghai 201204China National Key Laboratory of Solid-State Microwave Devices and Circuits Hebei Semiconductor Research InstituteShijiazhuang 050051China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China University of Chinese Academy of Sciences School of ElectronicElectrical and Communication EngineeringBeijing 101408China Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMI 48109USA School of Engineering Brown UniversityProvidenceRI 02912USA

Terahertz(THz)technologies have become a focus of research in recent years due to their prominent role in envisioned future communication and sensing *** of the key challenges facing the field is the need for tools to enable agile engineering of THz wave ***,we describe a reconfigurable metasurface based on GaN technology with an array-of-subarrays *** subwavelength-spaced array,under the control of a 1-bit digital coding sequence,can switch between an enormous range of possible configurations,providing facile access to nearly arbitrary wave front control for signals near 0.34 *** demonstrate wide-angle beam scanning with 1°of angular precision over 70 GHz of bandwidth,as well as the generation of multi-beam and diffuse wave fronts,with a switching speed up to 100 *** device,offering the ability to rapidly reconfigure a propagating wave front for beam-forming or diffusively scattered wide-angle coverage of a scene,will open new realms of possibilities in sensing,imaging,and networking.

关键词： wave scattered arbitrary

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