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Classification Algorithms for Liver Epidemic Identification

EAI Endorsed Transactions on Pervasive Health and Technology 2023年 9卷 1-13页

作者： Makkena, Koteswara Rao Natarajan, Karthika School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

Situated in the upper right region of the abdomen, beneath the diaphragm and above the stomach, lies the liver is a crucial organ essential for the proper functioning of the body. It performs many important functions in the body. The principal tasks are to eliminate generated waste produced by our organs, and digestive food and preserve vitamins and energy materials. It performs many important functions in the body, It Regulating the balance of hormones in the body Filtering and removing bacteria, viruses, and other harmful substances from the blood. In certain dire circumstances, the outcome can unfortunately result in fatality. There exist numerous classifications of liver diseases, based on their causes or distinguishing characteristics. Some common categories of liver disease include Viral hepatitis, Autoimmune liver disease, Metabolic liver disease, Alcohol-related liver disease, Non-alcoholic fatty liver disease, Genetic liver disease, Drug-induced liver injury, Biliary tract disorders. Machine learning algorithms can help identify patterns and risk factors that may be difficult for humans to detect. With This clinician can enable early diagnosis of diseases, leading to better treatment outcomes and improved patient care. In this research work, different types of machine learning methods are implemented and compared in terms of performance metrics to identify whether a person effected or not. The algorithms used here for predicting liver patients are Random Forest classifier, K-nearest neighbor, XGBoost, Decision tree, Logistic Regression, support vector machine, Extra Trees Classifier. The experimental results showed that the accuracy of various machine learning models-Random Forest classifier-67.4%, K-nearest neighbor-54.8%, XGBoost-72%, Decision tree-65.1%, Logistic Regression-68.0%, support vector machine-65.1%, Extra Trees Classifier-70.2% after applying Synthetic Minority Over-sampling technique. © 2023 K. R. Makkena et al., licensed to EAI.

关键词： Decision trees

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A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand

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Chinese Journal of Electronics 2024年第6期33卷 1383-1398页

作者： Xueqing YAN Yongming LI Sanjiang LI School of Computer Science Shaanxi Normal University School of Mathematics and Statistics Shaanxi Normal University Centre for Quantum Software and Information Faculty of Engineering and Information TechnologyUniversity of Technology Sydney

The tile-based multiplayer game Mahjong is widely played in Asia and has also become increasingly popular worldwide. Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand. An important notion in Mahjong is the deficiency number(*** number in Japanese Mahjong) of a hand, which estimates how many tile changes are necessary to complete the hand into a winning hand. The deficiency number plays an essential role in major decision-making tasks such as selecting a tile to discard. This paper proposes a fast algorithm for computing the deficiency number of a Mahjong hand. Compared with the baseline algorithm, the new algorithm is usually 100 times faster and, more importantly,respects the agent's knowledge about available tiles. The algorithm can be used as a basic procedure in all Mahjong variants by both rule-based and machine learning-based Mahjong AI.

关键词： Machine learning algorithms Knowledge based systems Decision making Asia Games

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A telerobotic shared control architecture for learning and generalizing skills in unstructured environments

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IEEE Transactions on Automation science and engineering 2025年 22卷 15953-15965页

作者： Shao, Xingmao Xie, Lun University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Direct teleoperation of robots in unstructured environments by non-experts often leads to low efficiency and increased risk. To this end, this paper proposes a shared control architecture where the robot can generalize demonstrations based on variable environments (start, obstacles, and goal positions) and infer user intention online to assist tasks safely and efficiently. First, the complex task is decomposed into unit actions, where the cubic-quintic-cubic Bezier curve is utilized to resolve the limitation of the classical dynamic movement primitives (DMP) algorithm in generalizing via-points. Gaussian process regression (GPR) was implemented to generalize expert demonstrations to different environments, ensuring full generalizable trajectory in the subtask space. GPR further extrapolate simple demonstrations from a subspace to the entire task space, avoiding the demand for numerous original demonstrations. Then, the online evolution of DMP is optimized: 1) an adaptive temporal scaling system is developed to synchronize evolution with human operations;2) an intention prediction and expected evolution selection method based on operational input is proposed, achieving humanled operation guidance. Experiments validate the effectiveness of the generalization, obstacle avoidance, and operation assistance. Tests with non-experts reported the designed architecture enhances safety and efficiency, and reduces collisions. © 2004-2012 IEEE.

关键词： Gaussian distribution

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Secured Provisioning of Multimedia Contents in Cloud Platform Using Cryptographic Approaches 1

Secured Provisioning of Multimedia Contents in Cloud Platfor...

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1st International Conference on computer science and Emerging Technologies, CSET 2023

作者： Arul Mary, S.A. Sahaaya Nevgi, Akshaj Nair, Rhea Ranjith Sharukh Bhathena, Jehan Chaudhry, Angad Jessika, S. Sharon School of Computer Science and Engineering Vit India Computer Science and Engineering India Computer Science with Specialization in Data Science India

ISBN: (纸本)9798350341737

Cloud computing is a technology that allows the utilisation of a vast network of computers that are distributed and run in parallel with one another. The management of the multimedia files presents challenges and the significant of which is the prevalence of attackers and users with malicious intent. This article presents the conceptual foundation for a four-tiered data security paradigm that can be implemented in cloud computing. The proposed framework employs a DNA-computing-based password or secret key with a length of 1024 bits for the purpose of encrypting the user private multimedia file or correspondence. The DNABMS algorithm is applied in the cryptographic generation of digital identities as well as pseudorandom number generators. The results of the testing that was done on the model have validated both its reliability and its efficacy, demonstrating that the model that was developed is both reliable and effective. Experiments have demonstrated that the developed method is superior to other well-known existing strategies in terms of both effectiveness and efficiency. © 2023 IEEE.

关键词： Cryptography

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Ultra-Dense LEO Satellite-Aircraft Access and Service Management in Civil Aviation

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China Communications 2025年第1期22卷 277-292页

作者： Wang Yilei Ma Ting Liu Xiaoyu Gao Zhuxuan Zhou Haibo Shen Xuemin School of Electronic Science and Engineering Nanjing UniversityNanjing 210046China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China The Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada

With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet ***,the civil aviation communications have increased dramatically,especially for providing airborne Internet ***,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service ***,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight ***,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service ***,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated *** results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.

关键词： civil aviation deep reinforcement learning satellite-aircraft access service management ultra-dense LEO satellite network

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MLCC Lamination Alignment Inspection System using Deep Neural Network

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Journal of Institute of Control, Robotics and Systems 2022年第7期28卷 699-705页

作者： Jung, Jun-Uk Kim, Hyun-Jae Son, Sung-Bin Oh, Heung-Seon School of Computer Science and Engineering Korea Republic of

The Multi-Layer Ceramic Capacitor (MLCC) lamination inspection process is an essential step in determining whether the dielectric in an MLCC image, which allows a constant flow of current, is well stacked or not at an early stage. Nevertheless, in the conventional inspection process, the inspector manually measures the margin ratio of the dielectric in the MLCC image and then conducts a simple visual inspection. This process is time and human resources consuming. Additionally, it causes noise in the determined result because it depends on each inspector’s subjective judgment. In this paper, we propose an MLCC lamination alignment inspection system using a deep learning-based segmentation model to solve this issue. Using various experiments, we demonstrated that the proposed system can help inspectors and improve work efficiency. © ICROS 2022.

关键词： Object detection

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Multi-Channel Multi-Frame Integration Detection Method for Weak Target with Coherent MIMO Radar

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6518-6536页

作者： Gao, Longji Li, Xiaolong Wang, Mingxing Sun, Zhi Cui, Guolong Yeo, Tat Soon University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China National University of Singapore Department of Electrical and Computer Engineering Singapore119077 Singapore

Coherent multiple-input multiple-output (MIMO) radar could significantly improve the weak moving target detection ability by accumulating multi-channel and multi-frame echo signal. However, due to the target motion and the geometric configuration differences of each node relative to the target, the multi-channel multi-frame accumulation of target echoes by coherent MIMO radar faces several difficulties, i.e., range migration (RM) and Doppler frequency migration (DFM) issues within each channel, spatial variations of target signal envelope and phase across different channels, time variations of target signal envelope and phase across different frames. To tackle these problems, two contributions are made in this paper. Firstly, by applying the generalized Radon Fourier transform (GRFT) to correct the RM/DFM and obtain the signal integration within each channel, we propose a multi-channel accumulation algorithm to compensate the spatial variations of target signal and realize the target energy accumulation across different channels, which takes full advantage of the GRFT output characteristics and the radar system topological structure. Secondly, a multi-frame accumulation algorithm is developed to compensate the time variations of target signal and achieve the further integration of target energy distributed in different frames. We also provide explicit expressions and analysis for performance metrics, including the output response, SNR improvement performance. The proposed method's effectiveness is confirmed through simulation experiments. © 1965-2011 IEEE.

关键词： Topology

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Enhancing Reliability, Security, and Scalability of Serverless Computing for Cloud Backup in Power Grids During Emergency Situations

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SN computer science 2025年第4期6卷 1-17页

作者： Bilgaiyan, Saurabh Aryan, Apurv Nayak, Nikhil Kumar Mishra, Priyanka School of Computer Science Engineering KIIT Deemed to be University Odisha Bhubaneswar 751024 India

The present study is to explore and experiment with the challenges and limitations of using serverless computing for cloud backup power and to propose solutions to improve its reliability, security, and scalability. Serverless computing offers several advantages for emergencies, including scalability, reliability, and cost-effectiveness. However, challenges that need to be addressed include cold boot delays, resource limitations, lack of infrastructure control, security concerns, monitoring and debugging issues, and vendor lock-in. To address these challenges, this research explores techniques to reduce cold boot delays, optimize resource allocation, implement effective security measures, ensure vendor independence, and improve monitoring and debugging capabilities. The findings and conclusions drawn from this study will provide valuable insight into ensuring platform suitability for emergencies, ultimately leading to more efficient and effective emergency response. This research begins by providing an overview of serverless computing and its benefits and limitations in emergencies. It then addresses the specific challenges that need to be addressed when using serverless computing to provide backup power to cloud-based power grids. These challenges are analyzed in detail, including their root causes and potential impact on emergency response. The findings guide emergency response organizations, cloud service providers, and other stakeholders to effectively use serverless computing as a backup power source for cloud-based power grids while addressing the specific challenges and limitations of emergencies. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud computing CPU utilization PMU Power grid Serverless

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Optimizing Job Search: A Multi-Model Approach with DRNN-BES, Collaborative Filtering, and LLM-NER Resume Analysis 3

Optimizing Job Search: A Multi-Model Approach with DRNN-BES,...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Jayakumar, Naveenkumar Patil, Netra Pokal, Roshankumar S. Dev, Rudra Pratap Nair, Sidharth Vellore Institute of Technology School of Computer Science and Engineering Vellore India College of Engineering Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350354348

This research introduces an innovative job recommendation platform designed to optimize the job search process through advanced technology integration. The platform features a sophisticated Resume Analyzer leveraging NLP and ML to meticulously extract candidate information. Coupled with a Recommendation System that employs mixture models such as DRNN-BES and collaborative filtering, it delivers personalized job suggestions tailored to individual profiles. Additionally, the system utilizes state-of-the-art web scraping technologies to continuously update its database with diverse and current job listings from various online sources, ensuring a wide range of opportunities are accessible. This integration of technologies not only refines job-matching precision but also significantly improves the efficiency of connecting job seekers with appropriate opportunities. Initial experimental results underscore the enhanced effectiveness and accuracy of our recommendation system, particularly when incorporating user behavioral data into the collaborative filtering algorithm. This paper demonstrates the transformative potential of integrating AI into job seeking, offering a scalable, unbiased, and time-efficient approach to modern employment challenges. © 2024 IEEE.

关键词： Collaborative filtering

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Air quality and dust level monitoring systems in hospitals using IoT

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Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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