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Performance Analysis of Machine Learning Classification Approaches for Monkey Pox Disease Prediction

Performance Analysis of Machine Learning Classification Appr...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： K.P. Haripriya H. Hannah Inbarani Dept.of computer science Periyar University Salem India

ISBN: (纸本)9781665482721

After covid-19 pandemic, many countries have the possibility of getting affected by Monkey Pox Virus. Monkey pox has the same symptoms of smallpox, chicken pox, and measles virus. In this work, the computational models are construed to predict the presence or absence of monkey pox virus. Eight different Classification algorithms including Decision Tree (DT), Random Forest Classification (RF), Naïve Bayes (NB), K-Nearest Neighbor algorithms (KNN), Support Vector Machine (SVM), Logistic Regression (LR), Ada Boosting algorithm (AB), Gradient Boosting (GB) algorithm are used for the Classification of Monkey Pox disease. Four evaluation measures are used in this work to compute the accuracy of classification. Four measures F-Score, Accuracy, Precision, and Recall are used to compare the eight different types of classification algorithms. Based on experimental analysis, it was observed that highest accuracy of 71% is achieved by Gradient Boosting algorithm when compared to other algorithms.

关键词： Support vector machines Radio frequency Machine learning algorithms computer viruses Predictive models Prediction algorithms Boosting

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Multi-Class Flat Classification of Lung Diseases Utilizing Deep Learning 1

Multi-Class Flat Classification of Lung Diseases Utilizing D...

引用

1st IEEE IAS Global Conference on Emerging Technologies, GlobConET 2022

作者： Gargees, Rasha S. University of Missouri Dept. of Electrical Engineering and Computer Science ColumbiaMO United States

ISBN: (数字)9781665443579

ISBN: (纸本)9781665443579

Early diagnosis of diseases is very critical for recovery. However, this is not always feasible due to the limited available staff or expensive and inadequate tools as we have witnessed in the recent COVID-19 pandemic. Lung diseases are life-threatening, but fortunately, they can be detected from X-ray images, which are cost-effective approaches. However, they need experts who are sometimes unavailable. Thus, using cutting-edge technology to diagnose diseases automatically and fast is the key solution to saving people's lives. In this research, deep learning techniques have been utilized to classify several lung diseases in a cost-saving, time-saving, and efficient manner. Examples of lung diseases studied in this research are COVID-19, Lung Opacity, Pneumonia, and Tuberculosis. Several pre-trained deep learning models have been employed for flat multi-class classification of these lung diseases instead of using binary classification to recognize one disease from normal cases, as most state-of-the-art studies carry out. The models' performance has been evaluated on imbalanced data of X-ray images with various resolutions and types. Finally, multiple measurements metrics have been utilized to evaluate the performance. The best accuracy achieved in this research is 95.643%. © 2022 IEEE.

关键词： COVID-19

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Smart Medical Box using Arduino

Smart Medical Box using Arduino

引用

International Conference on ent Computing and Control Systems (ICICCS)

作者： Sweadha. M M. Selvi Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The Smart Medical Box is a sophisticated IoT-driven solution designed to enhance medication adherence by utilizing computer vision and machine learning. The system automates the identification and extraction of essential information from medication labels, ensuring precision in dosage reminders and reducing human error. It incorporates a high-resolution camera, an OCR engine, and an Arduino-based microcontroller to enable seamless image processing and real-time analysis. With an intuitive interface and GPS-enabled location services, users can efficiently manage their medication schedules while accessing nearby pharmacies when needed. This research focuses on designing and implementing a scalable and cost-effective system that improves medication compliance and minimizes health risks associated with missed or incorrect dosages. By integrating advanced image processing techniques and AI-driven recognition algorithms, the Smart Medical Box ensures high accuracy in identifying medications. Additionally, features such as automated alerts, real-time monitoring, and pharmacy locator tools enhance the system's usability and accessibility. This innovation is particularly beneficial for elderly individuals and patients with chronic conditions, offering a simplified and automated approach to medication tracking. Performance evaluations of the system indicate a significant improvement in medication adherence, a high recognition accuracy rate, and positive user feedback. Future enhancements, including AI-based OCR, voice assistance, and mobile application connectivity, are expected to further improve functionality and ease of use. This paper presents the conceptualization, development, and testing of the Smart Medical Box, highlighting its potential to revolutionize personal healthcare management by providing an intelligent, efficient, and user-friendly solution.

关键词： Accuracy Image recognition Microcontrollers Optical character recognition Medical services Real-time systems Usability Older adults Biomedical imaging Global Positioning System

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Supporting Disaster Resilience Building through Cross-Community Knowledge Sharing 13

Supporting Disaster Resilience Building through Cross-Commun...

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13th Annual IEEE Global Humanitarian Technology Conference, GHTC 2023

作者： Koga, Motoya Kaneko, Tetsuya Leleito, Emanuel Karashima, Kazuki Aharari, Ari Sojo University Department of Architecture Kumamoto Japan Nagoya University Graduate School of Engineering Nagoya Japan Maebashi Institute of Technology Dept.of Architecture Maebashi Japan Sojo University Department of Computer and Information Sciences Kumamoto Japan

ISBN: (纸本)9798350321326

Collaborative action in disaster mitigation efforts contributes to knowledge and experience sharing, resource sharing, improvement of response and coordination, and standardization of good practice. In this study, we propose a workshop model and develop a virtual collaboration tool to support and promote cross-community knowledge sharing and collaboration focusing on disaster mitigation efforts. The workshop model involves pre-fieldwork training, actual fieldwork, and post-fieldwork information exchange. The collaboration tool enables information collection, sharing and record keeping. The effectiveness of the workshop model and the collaboration tool is demonstrated through use in a citizen participatory disaster preparedness workshops linking two elementary schools located in the central and southern regions of Japan. © 2023 IEEE.

关键词： Disaster prevention

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Machine Learning Based Early Fire Detection System

Machine Learning Based Early Fire Detection System

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2022 International Conference on Computing, Communication, Security and Intelligent Systems, IC3SIS 2022

作者： Venugopal, Athira Paul, Rosebell SCMS School of Engineering and Technology Dept of Computer Science Vidya Nagar Palissery Ernakulam India

ISBN: (数字)9781665468831

ISBN: (纸本)9781665468831

This paper describes fire detection using SVM and CNN. Fire is a potentially deadly event of immense damage. A fire alarm is an integral part of any building. It must be able to provide reliable and functional detection. A video camera is used to capture the video and convert into images from a certain distance and then fed to a classifier. Support Vector Machine (also known as SVM) and Convolutional Neural Network (or CNN) is used to detect the fire. It is then used to generate an alert as an SMS is sent to the authorities. The performance of the two algorithms is compared to identify the model that gives the best accuracy. © 2022 IEEE.

关键词： Support vector machines

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Credit Risk Assessment using Ensemble Models and Explainable AI

Credit Risk Assessment using Ensemble Models and Explainable...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Ramya Srikanteswara Krinal Naghera Shashank Balnad Kukkaje Adarsh Kumar Shrey P Dept of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

New challenges are still there in the credit risk management in this context of the financial system, primarily because of the lack of good models for prognosis as well as low interpretability. To address this issue, this research brings together CatBoost, XGBoost, and LightGBM to improve the prospect of identifying high-risk borrowers. A minimal level of Explainable AI (XAI) is utilized to make confirmations that the model follows them and to build confidence in the outcome. Moreover, based on a difference to basic models, the obtained ROC-AUC score equals 0.9693, proving the feasibility of the proposed ensemble approach in questions of impact developing default risks. The end product is therefore a robust method with ensemble modeling and compliant with regulatory requirements in operational financial environments.

关键词： Explainable AI Computational modeling Scalability Predictive models Data models Risk management Ensemble learning Stakeholders Streams Synthetic data

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Key Exchange in the Quantum Era: Evaluating a Hybrid System of Public-Key Cryptography and Physical-Layer Security 59

Key Exchange in the Quantum Era: Evaluating a Hybrid System ...

引用

59th Annual Conference on Information sciences and Systems, CISS 2025

作者： Staat, Paul Dorpinghaus, Meik Sheikholeslami, Azadeh Paar, Christof Fcllwcis, Gerhard Goeckel, Dennis Max Planck Institute for Security and Privacy Bochum Germany Technische Universität Dresden Vodafone Chair Mobile Communications Systems Dresden Germany Suffolk University Math and Computer Science Department Boston United States University of Massachusetts Dept. Electrical and Computer Engineering Amherst United States

ISBN: (纸本)9798331513269

Today's information society relies on cryptography to achieve security goals such as confidentiality, integrity, authentication, and non-repudiation for digital communications. Here, public-key cryptosystems play a pivotal role to share encryption keys and create digital signatures. However, quantum computers threaten the security of traditional public-key cryptosystems as they can tame computational problems underlying the schemes, i.e., discrete logarithm and integer factorization. The prospective arrival of capable-enough quantum computers already threatens today's secret communication in terms of their long-term secrecy when stored to be later decrypted. Therefore, researchers strive to develop and deploy alternative *** this work, we evaluate a key exchange protocol based on combining public-key schemes with physical-layer security, anticipating the prospect of quantum attacks: If a powerful quantum attacker cannot immediately obtain a private key, legitimate parties have a window of short-term secrecy to perform a physical-layer jamming key exchange (JKE) to establish a long-term shared secret. Thereby, the protocol constraints the computation time available to the attacker to break the employed public-key cryptography. In this paper, we outline the protocol, discuss its security, and point out challenges to be resolved. © 2025 IEEE.

关键词： Quantum computers

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Investigation of Immersion Cooling Efficiency for Lithium-Ion Battery Pack with Machine Learning Validation 14

Investigation of Immersion Cooling Efficiency for Lithium-Io...

引用

14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shivaranjini, S. Srivastava, Neetu Pati, Peeta Basa Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Mathematics Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350335095

Battery thermal management systems play a critical role in improving their power capability, extending lifetime, and minimizing the probability of thermal runaway. These systems aim to maintain a regulated operating temperature by removing heat from the battery pack and homogenizing the temperature within and between the cells. Precisely predicting the outlet fluid temperature is crucial to enhance the efficiency and functionality of these systems through proper design and operation. In this study, we applied machine learning techniques to predict the outlet fluid temperature of immersion cooling for a lithium-ion battery pack. Nine regression models have been employed to accurately predict the outlet fluid temperature. The results indicate that the Light GBM model can significantly enhance the efficiency and performance of immersive cooling systems for Li-ion batteries. We validated our approach by comparing the predicted temperature distributions with experimental data with MAE and RMSE of 0.0136 and 0.0209, which demonstrated that our model effectively captures the heat transfer in the battery pack. © 2023 IEEE.

关键词： Lithium-ion batteries

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Implementing HR Management System with an AI Powered ChatBot

Implementing HR Management System with an AI Powered ChatBot

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： D Susitra Pratheesh G Karthiga JD K Dhanalakshmi B Shamreen Ahamed Sylvia Grace J Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Human Resource (HR) management is one important section that increases the success of an organization to handle some important functions like employee data manipulation, attendance tracking, payroll processing, and performance evaluation. With the management of these processes becoming key to the success of small-scale startups, it poses a very big challenge for many of them to be able to manage these processes efficiently and cost efficient. By contrast, the old era of traditional HR systems was based on database driven architectures that are expensive in terms of operational cost, require storage space and complex infrastructural needs. These limitations often make traditional HR systems impossible for a startup that would prefer lightweight, affordable, and scalable solutions. Startups need robust platforms to streamline HR work streams, but often lack with financial resources for building complex database driven systems. In this paper, an AI powered HR management system is developed to simplify employee management processes for small scale startups. In this system. cache memory is used instead of traditional databases, thereby the operational cost of the system will be reduced for storage and retrieval of employee details. Automated Attendance Management enhanced with Face Recognition technology, Payroll Management, Leave Management, Feedback Submission modules are included. It is integrated with an AI powered chatbot, which facilitates a smooth interaction by the employees with query for information, submit leave requests and pull up all payroll information etc. A user friendly dashboard for a consolidated view of employee profiles, attendance records, leave approvals, and payroll data can be served over an optimized and time efficient HR workflow. This system addresses the needs of the startups such as flexible usage, cost effective operations and improved resource management.

关键词： Adaptation models Costs Databases Face recognition Cache memory Organizations Chatbots Data models Resource management Artificial intelligence

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Info-ExtNet: An Information Extraction framework from Noisy Structured Documents

Info-ExtNet: An Information Extraction framework from Noisy ...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Sk Shamim Aktar Debapriya Banik Dept. of Computer Science & Engineering ICFAI University Tripura Agartala India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The exponential growth of digital data necessitates efficient and accurate information extraction (IE) from structured documents. Traditional rule-based systems, limited by domain-specific constraints, struggle with scalability and adaptability. This paper proposes Info-ExtNet, an innovative model that leverages the LayoutLM architecture by integrating text, 2D positional, and image embeddings for enhanced document understanding. Our approach operates through three key stages: preprocessing, modeling, and post-processing. Advanced Optical Character Recognition (OCR) techniques and layout parsing are used during preprocessing to transform scanned documents into structured data while retaining essential spatial information. The core modeling phase employs a transformer-based architecture with multimodal embeddings to capture both textual and visual features effectively, and the post-processing stage refines the extracted data to ensure precision and *** model was evaluated using benchmark datasets such as FUNSD and SROIE, achieving high performance metrics, including a Precision of 0.94, Recall of 0.94, and an F1-Score of 0.95. These results surpass state-of-the-art (SOTA) methods, demonstrating the robustness of our model in handling complex document layouts. The extracted information, saved in a structured CSV format, supports immediate integration into various downstream applications. This work provides a scalable solution for document intelligence, paving the way for future research in document processing and analysis.

关键词： Adaptation models Visualization Optical character recognition Layout Transforms Transformer cores Information retrieval Feature extraction Transformers Data models

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