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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Madhurya, J.A. Meena, K. Department of Computer Science and Engineering GITAM School of Technology GITAM University Bangalore India

ISBN: (纸本)9789819998104

Secure storage and exchange of health information has emerged as a focus of research in the area of medical technology. The healthcare sector has a high requirement for data security and privacy because most of the healthcare systems maintain data in a centralized manner, storing it all in a single location. Such systems have a significant probability of allowing an anonymous user to access and alter the data, which frequently results in data leakage problems and lack of transparency. In the proposed work, the blockchain technology is used which is a decentralized and distributed ledger that enhances the data security by storing the record on blocks and encryption algorithm to encrypt the data. Here patient is allowed to provide the access permission for the record. An advanced encryption standard (AES) and data encryption standard (DES) algorithm is used for encryption and its encryption time is compared and represented in graph. The objective of this work is to do the performance analysis between algorithms and to represent the faster encryption algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Web Based Application to Recognize Diseases and Providing Consultant to Patients 3

Web Based Application to Recognize Diseases and Providing Co...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Ajitha, P. Sathish Kumar, P.J. Navaneethakrishnan, Sundara Rajulu Sivasangari, A. Surendran, R. Sathyabama Institue of Science and Technology School of Computing Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Dhanalakshmi Srinivasan University Trichy Department of Computer Science and Engineering Samayapuram Campus Trichy India Sathyabama Institue of Science and Technology School of Computing Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

One of the most important aspects of civilization is human wellness. The best and most accurate condition analysis seeks to offer the necessary assistance as soon as practical. The clinical research factor necessitates knowledge from other fields, such as computer science and statistics, as this comprehension is frequently difficult. The need to pursue novel approaches places a heavier burden than normal on these fields. The variety of new strategies allows for a comprehensive investigation while avoiding specific elements. As a solution to this issue, a systematic evaluation of human illnesses with system mastering is proposed. This study centers on current techniques for system learning advancement applied to the prognosis of human diseases in the medical field in order to discover exciting trends, make light predictions, and assist in decision-making. This initiative includes a scientific chatbot that uses synthetic intelligence to answer patients' questions. This article explains a one-of-a-kind set of device learning algorithms that are used in healthcare programmes to provide excellent preferred choice assistance. This article is intended to cover a research gap in the development of practical choice aid structures for scientific applications. © 2024 IEEE.

关键词： Clinical research

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Enhancing text understanding of decoder-based model by leveraging parameter-efficient fine-tuning method

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Neural Computing and Applications 2025年第9期37卷 6899-6913页

作者： Feroze, Wasif Cheng, Shaohuan Jimale, Elias Lemuye Jakhro, Abdul Naveed Qu, Hong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Information Technology Shaheed Benazir Bhutto University Naushahro Feroze Campus Naushahro Feroze Pakistan

Machine reading comprehension (MRC) is a fundamental natural language understanding task in natural language processing, which aims to comprehend the text of a given passage and answer questions based on it. Understanding implicit information, deducing the logical structure of information, and connecting context from different pieces of information make the MRC task difficult. Most current state-of-the-art approaches for MRC are using encoder-based models. However, no earlier research proposed a decoder-only model for MRC question-answering datasets, although language models based on this category achieved unprecedented performance in different generative tasks. In this paper, we propose a parameter-efficient fine-tuning framework that effectively increases MRC capabilities on decoder-only large language models. This framework designs the process for MRC and introduces the low-rank adaptation (LoRA) method to effectively fine-tune the large model with many parameters, even with lower hardware resource requirements than the previous methods. In addition, we also integrate a quantized model inference strategy for the fine-tuned model to improve practicability further. We conducted experiments on four types of MRC datasets. After extensive experiments, our results show that our model achieved a significant performance boost over baselines and outperformed other strong models for MRC. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Decoding

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Detecting ugly and derogatory words in Serbian language using a web browser extension 32

Detecting ugly and derogatory words in Serbian language usin...

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32nd Telecommunications Forum, TELFOR 2024

作者： Milakovic, Adrian Jocovic, Vladimir Cincovic, Jelica Micovic, Marko Radenkovic, Uros Draskovic, Drazen University of Belgrade School of Electrical Engineering Department of Computer Science and Information Technology Serbia

ISBN: (纸本)9798350391053

In natural language processing, data acquisition and preprocessing techniques are significant for experiments involving training models on cleaned data. This paper describes the formation of a dataset of ugly and derogatory words in the Serbian language and the development of a web extension to analyze texts in which such words appear and then their censoring. Developing such an add-on is essential in creating a safer and healthier digital environment for young users who are just developing their Internet habits and adults who want to avoid unwanted content. It is crucial to point out that there are few software tools for natural languages with limited resources, such as the Serbian language. This software could analyze the frequency of certain foul words on different sites, which would contribute to a better understanding of the impact of such language on users. © 2024 IEEE.

关键词： Web browsers

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Early Wildfire Detection Using One Class Learning

Early Wildfire Detection Using One Class Learning

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Le Hong, Wen Zhao, Xinghui Washington State University School of Engineering and Computer Science VancouverWA United States

ISBN: (纸本)9798350362480

Wildfires cause major damages to forests and infrastructures every year. The cost of wildfire control and damage repair are rapidly increasing, leading to significant financial burdens for many communities, especially remote and rural communities. An effective way to mitigate the damage wildfires cause is by detecting the fire early. Recently, a combination of Unmanned Aerial Vehicles (UAVs) and deep learning has been proposed as a method for early detection. However, a major challenge is the lack of data to train learning models. This causes current models to lack the ability to generalize the task of wildfire detection. In this paper, we present One Class Classification (OCC) as an alternative solution to alleviate the lack of data. Our models achieved F1 scores of 0.94 when trained and tested on the FLAME data set and 0.97 when trained and tested on the FLAME2 data set. © 2024 IEEE.

关键词： Premixed flames

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Landslide extraction model for remote sensing images based on improved DeepLabv3+

Landslide extraction model for remote sensing images based o...

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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Wu, Yanling Lang, Wenhui School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China

ISBN: (纸本)9781510680425

In order to realize the accurate recognition of landslides in remote sensing images, an improved DeepLabv3+ landslide extraction model is proposed in this paper.(1) Hybrid Module and Attention Module based CSPNet (HA-CSPNet) is constructed as the backbone feature extraction network to enhance the feature information of small landslides and suppress the interference of irrelevant background information.(2) Combining the advantages of Residual Structure and Dense Connected Module, Residual-dense ASPP is designed to focus landslide features at different scales, enhance feature reuse and prevent gradient *** experimental results show that the landslide extraction model proposed in this paper is *** can improve the accuracy of landslide semantic segmentation. © 2024 SPIE.

关键词： Semantic Segmentation

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Real-Time Task Manager: A Python-Based Approach Using Psutil and Tkinter 8

Real-Time Task Manager: A Python-Based Approach Using Psutil...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Sai, P. Charan Karthik, K. Prasad, K. Bhargav Pranav, C. Venkata Sai Divya, K.V. Amrita School of Computing Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331505462

The creation of a Real-Time Task Manager with a user-friendly interface and effective system monitoring is presented in this work utilising Python. The Psutil library was used to retrieve comprehensive system information, and Tkinter was used to develop the graphical user interface (GUI). The task manager's capacity to show parent processes at first, enabling users to expand and examine matching child processes upon request, is one of its primary features. In addition, the system permits the termination of user processes and facilitates the search for running processes. Real-time process data refreshing is another essential feature that makes sure the most recent information is shown. The Process ID, Process Name, Username, Memory Usage Percentage, and CPU Usage Percentage are among the prominent metrics displayed. These indicators are essential for locating operations that use a lot of resources and for efficiently controlling system performance. Iterative development was prioritised in the design process, including user feedback to improve stability and performance. The task manager is a potent tool for users and system administrators, providing real-time updates and responsive monitoring and management features through its modular and scalable architecture. © 2024 IEEE.

关键词： Graphical user interfaces

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Smart Contract-Based Blockchain Technique for Logistic Shipping Management System 6th

Smart Contract-Based Blockchain Technique for Logistic Shipp...

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6th International Conference on Communications and Cyber-Physical engineering, ICCCE 2023

作者： Haritha, T. Anitha, A. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819971367

The logistics industry is a vital component of the global economy, but it still faces several challenges, including inefficient processes, lack of transparency, and high costs due to intermediaries. Blockchain technology has the potential to address these challenges by providing a decentralized and transparent platform that can automate and streamline logistics processes through smart contracts. This research paper presents a study on Logistics shipping based on the blockchain using smart contracts in Solidity language. The paper discusses the challenges in the logistics industry and the potential of blockchain technology to solve them. It then presents a Smart Contract for Logistics shipping, including the required variables, a struct for shipments, a mapping to hold the shipments, events for shipment creation and delivery, and functions to create a new shipment, mark a shipment as delivered, and get the shipment details. The paper also discusses the benefits and limitations of the proposed solution on logistics systems. It concludes that the proposed solution can provide a secure, transparent, and efficient platform for logistics shipping that can eliminate intermediaries, reduce costs, and improve the overall logistics ecosystem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart contract

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Sentiment-aware drug recommendations with a focus on symptom-condition mapping

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International Journal of Information Technology (Singapore) 2024年第8期16卷 5195-5212页

作者： Anbazhagan, E. Sophiya, E. Prasanna Kumar, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology

The adoption of digital health records and the rise of online medical forums resulted in massive volumes of unstructured healthcare data. Most of the data used by traditional drug recommendation systems is obtained from patient Electronic Health Records (EHR) and subjective feedback and experiences included in patient evaluations. Nevertheless, the current systems based on sentiment analysis fail consider Symptom based diagnosis whereas researches that proposes Graph models doesn’t not include patient satisfaction and Health History as some has specific needs. To address the draw backs of existing drug recommendation systems, this study suggests a novel approach that combines symptom-disease mapping with sentiment analysis of patient reviews. The primary objective of the research is to utilize machine learning classifiers to make symptom-based predictions about probable medical conditions as Phase I. Then, before being fed into sequence network and machine learning models, patient reviews that are relevant to the predicted condition are filtered as Phase II. This method generates probabilities for suggesting certain drugs by evaluating sentiments and incorporating review ratings. With a Performance score of Ensemble Model up to 99.25% in Phase I and accuracy of 99.45% for sentiment analyser in Phase II. The performance of the model was evaluated based on accuracy, Receiver Operating Characteristic Curve (ROC)-Area Under Curve (AUC) score, sensitivity, selectivity. The proposed system helps in recommending the optimal drug for any type of symptom samples which is available in database. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Disease Drug recommendation system Electronic Health Records Personalized healthcare Sentiment analysis Symptom

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Stationary wavelet transform and SpinalNet trained light spectrum Tasmanian devil optimization enabled DR detection using fundus images

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Multimedia Tools and Applications 2025年第4期84卷 1597-1626页

作者： Tamboli, Mubin Shoukat Dhumane, Amol Prasad, Rajesh Prasad, Jayashree Rajesh Ranjan, Nihar M. Computer Engineering Pimpri Chinchwad College of Engineering Pradhikaran Maharashtra Pune411044 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune India Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Diabetes is a common disease that causes complications in the eyes known as Diabetic Retinopathy (DR). It aids in discovering the DR and tends to be the main cause behind people’s blindness amidst the previous decades. For prevent the complications of DR and to control the level of blood Glucose, the timely and crucial diagnosis is essential. A model is provided for detecting the DR. Here, Light Spectrum Tasmanian Devil Optimization (LSTDO) is utilized with SpinalNet for DR detection. The method undergoes an adaptive median filter and Region of interest mining process to generate a pre-processed image. The Sparse Fuzzy C-Means (FCM) is adapted for segmenting the optic disks followed by feature extraction. Then, the Hough line transformation with Tyler Coye algorithm (HLTCA) is used for blood vessel segmentation. Concurrently, the Stationary Wavelet Transform (SWT) is adapted to input image to overwhelm the deficiency of translation-invariance issue and then the feature mining is done. The feature vector is generated by unifying the outputs of the three-feature extraction phases and is employed to detect DR. The accuracy, sensitivity, and specificity obtained by the LSTDO-based SpinalNet are 91.6%, 89.9% and 92.1%, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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