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Emerging technology (INCET), International Conference for

作者： Vinay K Shashanka Ashwini Kodipalli Swetha P Kumaraswamy S Dept. of Computer Science and Engineering Global Academy of Technology Bengaluru Karnataka India Dept of Artificial Intelligence and Data Science Global Academy of Technology Bengaluru Karnataka India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

A good design for image classification problems is the CNN architecture you presented, which consists of 5 convolution blocks followed by 4 fully connected layers. From the input X-ray images, the convolutional blocks extract pertinent features, and the fully connected layers assist in determining the final classification based on those learned features. You have integrated various approaches to improve the performance of your model. The inputs to each layer are normalized through batch normalization, which can speed up training and enhance generalization. By removing certain neurons at random during training, dynamic dropout helps avoid overfitting. L2 regularization weight decay and learning rate decay are two efficient strategies for preventing overfitting and enhancing the model's capacity to expand to new data. Popular optimization algorithm Adam optimizer effectively neural network training. For binary classification problems like the diagnosis of pneumonia, the loss function for binary Cross-Entropy is the best option. To determine your model's efficacy, you must validate it using benchmark datasets that are available to the general public. You can evaluate your model's effectiveness by comparing its performance to that of current methods by conducting experimental investigations on these datasets. Your model performs well as evidenced by accuracy scores of 90.93%, 89.17% for multi-class classification and binary classification. tasks. Automated methods, such as the one you suggested, might help medical practitioners recognize pneumonia and spot diseased spots in chest X-ray pictures. However, it's crucial to remember that automated systems shouldn't take the place of professional radiologists' and doctors' skills and judgment; rather, they should be used as supportive tools. Medical To ensure accurate diagnosis and suitable patient care, specialists should always review and interpret the system's data. It's also crucial to take into account potential drawbac

关键词： Training Pneumonia Accuracy Transfer learning Feature extraction Real-time systems Data models

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Machine Learning Heart Disease Prediction Using KNN and RTC Algorithm 3

Machine Learning Heart Disease Prediction Using KNN and RTC ...

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3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Senthil, G.A. Prabha, R. Razmah, M. Veeramakali, T. Sridevi, S. Yashini, R. Agni College of Technology Dept of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Chennai India School of Computing SRM Institute of Science and Technology Dept of Data Science and Business Systems Chennai India Sri Ramachandra Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462754

Heart Disease or cardiovascular disease refers to the range of heart conditions like cardiac arrest, coronary artery disease. Heart disease can be very well hindered through certain lifestyle changes. There is a significant increase in the mortality rate recently due to the distinctive heart diseases. Machine learning uses mathematical models to work efficiently with the enormous amount of data. It plays a crucial role in medical science in the prediction of distinctive diseases. Cardiologists inspects the heart functionality using electrocardiography, computed tomography. These tests are quite expensive for a common man. Recent times, the life span of a human is guaranteed only with the support of medications. As prevention is better than the cure, machine learning helps to predict the vulnerability of a heart disease with few elemental symptoms and health factors. It is been fed by the basic data of the patients like age, sex. Machine learning helps to predict the vulnerability in advance which provides the cardiologists with great acumen for the adaption of the treatment. Machine learning algorithms have proven to produce reliable and accurate output with the help of the inputs. The algorithms used in the article include KNN and decision tree classifier which is compared to yield the desired and efficient output. © 2022 IEEE.

关键词： Heart

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CrimeVerse: Exploring Crime Scene through Virtual Reality

CrimeVerse: Exploring Crime Scene through Virtual Reality

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International Conference on Inventive Computation Technologies (ICICT)

作者： Jay Deshmukh Srishti Shetty Mrunmayee Waingankar Gauri Mahajan Richard Joseph Dept. of Computer Engineering Vivekanand Education Society's Institute of Technology Mumbai India

Modern technologies are applied in order to enhance the caliber of crime inves tigations. The documentation of crime scenes plays a crucial role in the inves tigation and pros ecution of criminal cas es. The challenges faced by traditional methods of crime scene documentation such as photographs, sketches, and written notes have their limitations in terms of accuracy, completeness, limited accessibility, and the risk of losing crucial evidence. The precis e scene recording offered by 3D data capture techniques is free from the spatial dis tortion effects pres ent in 2D photographic recordings. This research study explores the use of LIDAR scanning and virtual reality in enhancing crime scene documentation. The paper focuses on evaluating the effectiveness of LIDAR scanning and VR walkthroughs and also focuses on evidence tagging for enhancing crime scene documentation. It also helps in inves tigating the usability of the VR system for law enforcement professionals. The proposed model faces several challenges during its development, including the high cost and technical complexity of LIDAR scanning, the need for efficient data proces sing and vis ualiz ati on, and the limited accessibility of the VR system due to hardware and software requirements. Through user testing and analys is, the project demons trated significant improvements in the accuracy and completeness of crime scene documentation, providing a more detailed and immersive experience for investigators. The VR system was found to be us er-friendly and effective in aiding decision-making and communication among law enforcement professionals. Overall, the project provides valuable insights which let you see through the eyes of a witness by documenting their perspective, which aids in the investigation and comprehension of the incident. They can also be us ed as proof in court.

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Mathematical Model of Asset and Liability Management in the Presence of Normative and Internal Constraints 5

Mathematical Model of Asset and Liability Management in the ...

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5th International Conference on Control Systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2023

作者： Zhukova, Aleksandra Flerova, Anna Chernov, Aleksey Pilnik, Nikolay FRC CSC of RAS Dept. of Mathematical Modelling of Economic Systems MIPT Moscow Russia FRC CSC RAS Dept. of Mathematical Modelling of Economic Systems Moscow Russia Moscow Institute of Physics and Technology MIPT-School of Applied Mathematics and Computer Science Moscow Russia

ISBN: (纸本)9798350309126

In this paper we present the mathematical formulation of the problem of planning the Asset Liability Management if the Presence of Normative and Internal Constraints. As a nonlinear optimal control problem, it has specifics due to the state constraints. The maximum principle involves verification of regularity property in order to apply computational methods. We reformulate the problem as the problem with convex objective function and linear, but mixed system of constraints. This enables application of optimization methods to compute the optimal controls, and study the dependence of controls on the parameters that describe the external environments and the management objective. © 2023 IEEE.

关键词： Optimal control systems

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IFA in Named Data Networking (NDN) : Case Study on INHERENT Topology

IFA in Named Data Networking (NDN) : Case Study on INHERENT ...

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International Conference on Wireless and Telematics (ICWT)

作者： Ribka Tiarama Agil Fuad Gumelar Muhammad Rayhan Nauroha Shofiyyah Nur Pirusita Jupriyadi Syaiful Ahdan Nana Rachmana Syambas Dept. of Telecommunication Engineering Bandung Institute of Technology Bandung Indonesia Faculty of Engineering & Computer Science Universitas Teknokrat Indonesia Lampung Indonesia

Named Data Networking (NDN) is part of an NSF research project that started in 2010 and was created to be implemented as a future Internet architecture. NDN as a network service has evolved from an Internet host-based packet delivery model to an Internet content-based packet delivery model. Attackers design various attacks for NDN networks by exploiting the interaction between interest packets and data packets. IFA is one type of DDoS attack on NDN networks. IFA attackers would overload intermediate routing nodes by requesting a large number of fake Interest packets. In this paper, the interest-flooding attack model is tested by varying the attack time and location of the attacker. The results show that IFA attacks are more effective when the consumer is closer to the producer due to the increased latency resulting from the attack.

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Leveraging Language-based Representations for Better Solving Symbol-related Problems with Large Language Models 31

Leveraging Language-based Representations for Better Solving...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Wang, Yile Cheng, Sijie Sun, Zixin Li, Peng Liu, Yang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University Beijing China Tsinghua University Beijing China Jiangsu Collaborative Innovation Center for Language Competence Jiangsu China

ISBN: (纸本)9798891761964

Symbols such as numerical sequences, chemical formulas, and table delimiters exist widely, playing important roles in symbol-related tasks such as abstract reasoning, chemical property prediction, and tabular question-answering. Compared to tasks based on natural language expressions, large language models (LLMs) have limitations in understanding and reasoning on symbol-based representations, making it difficult for them to handle symbol-related problems. In this paper, we propose symbol-to-language (S2L), a method that converts symbol-based representations to language-based representations, providing valuable information for language models during reasoning. We found that, for both closed-source and open-source LLMs, the capability to solve symbol-related problems can be largely enhanced by incorporating such language-based representations. For example, by employing S2L for GPT-4, there can be substantial improvements of +21.9% and +9.5% accuracy for 1D-ARC and Dyck language tasks, respectively. There are also consistent improvements in other six general symbol-related tasks such as table understanding and Tweet analysis. We release the GPT logs in https://***/THUNLP-MT/symbol2language. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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CSI based Performance Investigation of Single Cell - Multi user MaMIMO System 2

CSI based Performance Investigation of Single Cell - Multi u...

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2nd Asian Conference on Innovation in technology, ASIANCON 2022

作者： Hosamani, Ravi Yerriswamy, T. K.L.E.Institute of Technology Dept. of Electronics and Communication Engg Karnataka Hubballi580027 India K.L.E.Institute of Technology Dept. of Computer Science and Engg. Karnataka Hubballi580027 India

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

In the revolution of wireless communication system multi-user MIMO is one of the proliferation antenna technology in 5G currently being developed to meet the need for high data speed and better service quality for high band applications. This one of theextensive techniques that can support the 5G communication system for advanced multi-antenna system is the Massive MIMO (MaMIMO) technique is that base station with a larger number of antennas A cell with a lesser number of *** study focuses on fading channels in the system using the OFDM *** system overall performance is simulated with distinctive situations of channel state data by the use of pilot training. Linear precoding methods are used to resolve the Multi-User Interference (MUI) at receiver. That knowledge of CSI has an impact on system performance due to channel estimation errors. In the unknown CSI conditions, the Bit error rate (BER) of the system is a bit higher than that of perfect. using a massive range of antennaarray considerably growth the spectral performance without bound, each in best and substandard CSI condition and it's visible that spectral performance also suffers from the channel, in which the LOS channel performance outperforms Rayleigh channel performance. © 2022 IEEE.

关键词： Fading channels

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Compositional Semantic Mix for Domain Adaptation in Point Cloud Segmentation

arXiv

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arXiv 2023年

作者： Saltori, Cristiano Galasso, Fabio Fiameni, Giuseppe Sebe, Nicu Poiesi, Fabio Ricci, Elisa The Dept. of Information Engineering and Computer Science University of Trento Italy The Dept. of Computer Science Sapienza University of Rome Italy Nvidia Ai Technology Center Italy Trento Italy

Deep-learning models for 3D point cloud semantic segmentation exhibit limited generalization capabilities when trained and tested on data captured with different sensors or in varying environments due to domain shift. Domain adaptation methods can be employed to mitigate this domain shift, for instance, by simulating sensor noise, developing domain-agnostic generators, or training point cloud completion networks. Often, these methods are tailored for range view maps or necessitate multi-modal input. In contrast, domain adaptation in the image domain can be executed through sample mixing, which emphasizes input data manipulation rather than employing distinct adaptation modules. In this study, we introduce compositional semantic mixing for point cloud domain adaptation, representing the first unsupervised domain adaptation technique for point cloud segmentation based on semantic and geometric sample mixing. We present a two-branch symmetric network architecture capable of concurrently processing point clouds from a source domain (e.g. synthetic) and point clouds from a target domain (e.g. real-world). Each branch operates within one domain by integrating selected data fragments from the other domain and utilizing semantic information derived from source labels and target (pseudo) labels. Additionally, our method can leverage a limited number of human point-level annotations (semi-supervised) to further enhance performance. We assess our approach in both synthetic-to-real and real-to-real scenarios using LiDAR datasets and demonstrate that it significantly outperforms state-of-the-art methods in both unsupervised and semi-supervised settings. © 2023, CC BY.

关键词： Mixing

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Information Retrieval System of Arabic Alphabetic Characters by Using Hidden Markov Model 4

Information Retrieval System of Arabic Alphabetic Characters...

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4th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2022

作者： Shaker, Atheel Sabih Khaleel, Mahdi Fadil Ismael, Omar Ayad Majeed, Rand S. Ahmed, Mohammed Rashid Baghdad College of Economic Sciences University Dept. Computer Engineering Techniques Baghdad Iraq Northern Technical University Technical Institute Kirkuk Electronics Department Kirkuk Iraq University of Diyala College of Islamic Sciences Diyala Iraq Al-Esraa University College Dept. Computer Technology Engineering Baghdad Iraq Karabuk University Computer Engineering Istanbul Turkey

ISBN: (纸本)9781665468350

In this paper, recognizing Arabic printed text is presented. A long time ago Recognizing the Arabic text has received great interest in information technology applications, as the Arabic language is among the different languages, and what distinguishes it from other languages is also shared by the Persian language and the ancient Turkish language (Ottoman) the difficulty of recognizing the letter because the letter has several forms in The beginning of the word and a shape in its middle and a shape at the end of the word. In languages other than the letters are written, the shape of the letter is one in the word, so it is easy to manipulate it through ready-made Optical Character Recognition (OCR) programs as letters A to Z. As a result, this study proposes a method for distinguishing Arabic printed text from other printed text. He's been looking forward to this moment for a long time. We demonstrate an off-line system that recognizes printed Arabic text by employing a Hidden Markov Model and an algorithm that divides the recognize reviews literature into sections and characters. © 2022 IEEE.

关键词： Hidden Markov models

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Integrating AI-Driven IoT Solutions for Enhanced Predictive Analytics in Healthcare a Comprehensive Study on Chronic Disease Management

Integrating AI-Driven IoT Solutions for Enhanced Predictive ...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Sreenivasu, S.V.N. Katta, Siva Koteswararao Auguskani, Jerlin Priya Lovelin Priya, Dumpa Vamsi Jagadish, V. Raghunath, Vedaprada Narasaraopeta Engineering College Dept of Computer Science and Engineering Narasaraopet India Kitew Department of Mechanical Kakinada India Jazan University Department of Nursing Saudi Arabia Vignan Institute of Information and Technology Visakhapatnam India Department of Cse Hyderabad India Visveswaraya Technological University India

ISBN: (纸本)9798331508432

The high incidence of chronic non-communicable diseases (NCDs) places a significant financial strain on public health systems. Brazil is projected to see a rise in mortality rates caused by non-communicable diseases (NCDs) by 2030. These include cancer, cardiovascular disease, respiratory illness, and diabetes. The unique challenge of managing chronic and integrated care for NCDs is well-documented. This study presents an AI-based framework for managing chronic diseases and providing digital health services globally. The suggested platform is constructed and supported by healthcare 4.0 technologies, which enable the implementation of a smart healthcare system. Among these innovations are AI-enabled cloud solutions, the internet of medical things, and wearable technology. Also included in the publication is a case study of diabetes prediction that demonstrates the platform's viability. An initial dataset was created for the research case with many attributes, including bio-impedance, skin impedance, skin temperature, pulse rate, and oxygen concentration. In this study, we investigate how the IoT and Artificial Intelligence (AI) are changing the face of illness detection in connected healthcare systems. AI has quickly become an indispensable tool in the healthcare industry, providing advanced algorithms for evaluating medical data and facilitating forecasting and decision-making. The internet of things (IoT) improves upon this by allowing web-enabled devices, such as implanted sensors and wearables, to continuously gather data. By integrating AI and IoT, smart healthcare systems improve medical procedures, patient experiences, and operational operations. Rapid and reliable disease diagnosis is made possible by combining AI-driven procedures with IoT data streams. This paper aims to overcome the limitations of traditional methods, which are typically influenced by human biases. © 2024 IEEE.

关键词： Diagnosis

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