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Advances in Computing Research on science engineering and Technology (ACROSET), International Conference on

作者： Almas Jafar Shaikh Sakshi Bharat Takbhate Shailesh Gawai Altaf Mainoddin Limbore Vaidehi Avinash Patil Department of Computer Science and Business System JSPM's Rajarshi Shahu College of Engineering Tathawade India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

The main characteristics of the healthcare platform that this study suggests for rural areas are User ID or image-based recognition, Options for consultation, Disease Prediction, Integration with Aasha Workers, Technical considerations and Project Objectives. User ID or image-based recognition are used for a secure login process. Options for Consultation is to select from medical chatbots or video consultations with physicians. Disease Prediction is to make recommendations for possible disease diagnosis, real-time image analysis (such as reviewing uploaded photos) and machine learning are used. Risk assessment: Machine learning algorithms evaluate risks for different health disorders based on user-provided data (age, medical history, etc.). Integration with Aasha Workers is a platform establishes a connection with Aasha workers, who can provide medication as required following consultations. Technical considerations are a platform can be built using web development frameworks such as Flask or Django. Strong privacy and data security protocols are necessary. Project Objectives: Expand access to healthcare in remote locations. Give patients knowledge and tools for self- evaluation to empower them. Improve interaction and cooperation between medical professionals, patients, and Asha employees.

关键词： Image recognition Medical services Web conferencing Streaming media Chatbots Real-time systems Skin Medical diagnostic imaging Monitoring Diseases

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Grammar Error Correction in Indonesian Sports News: Comparing the Performance of Pre-Trained T5 and BART Models

Grammar Error Correction in Indonesian Sports News: Comparin...

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2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Adnan, Muh Prakasa, Esa Yuyun Umar, Najirah Nasrullah Sadli, Adi Anatasya, A. Edeth Fuari Hazriani Razak, Mashur Handayani University of Makassar Dept. of informatics engineering Makassar Indonesia Research center for data and information science National Research and Innovation Agency Bandung Indonesia Handayani University Makassar Dept. of computer system Makassar Indonesia

ISBN: (纸本)9798331508579

This research compares the performance of two pre-trained models based on the transformer, namely text-to-text transfer transformer (T5) and bidirectional and auto-regressive transformer (BART), through a fine-tuned for correcting grammar in Indonesian sports news texts. We used vennify/t5-base-grammar-correction and onionLad/grammar-correction-bart-base, was trained using a dataset of 10,075 sentences from Indonesian language sports news sites. We tested the model performance using bilingual evaluation understudy (BLEU), google's language evaluation understanding (GLEU), training loss, and validation loss. The results show that BART was more efficient in learning data patterns and has better generalization ability, supported by BLEU and GLEU scores of 0.8560 and 0.9655. Meanwhile, T5 maintains simpler sentence structures, supported by BLEU and GLEU scores of 0.8120 and 0.9564. BART excels at handling more complex contexts, making it more effective at accurately correcting errors in dynamic sports news. Both models have their respective advantages, with BART being better at correcting detailed grammatical errors, while T5 offers consistent correction of more simple sentence structures. © 2024 IEEE.

关键词： Error correction

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DUCK: A Drone-Urban Cyber-Defense Framework Based on Pareto-Optimal Deontic Logic Agents 37

DUCK: A Drone-Urban Cyber-Defense Framework Based on Pareto-...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Deb, Tonmoay Dix, Jürgen Jeong, Mingi Molinaro, Cristian Pugliese, Andrea Li, Alberto Quattrini Santos, Eugene Subrahmanian, V.S. Yang, Shanchieh Zhang, Youzhi Department of Computer Science Northwestern University EvanstonIL United States Department of Computer Science Dartmouth College HanoverNH United States Department of Informatics Modeling Electronics and System Engineering University of Calabria Rende Italy Department of Informatics Technical University of Clausthal Clausthal Germany Department of Computer Engineering Rochester Institute of Technology RochesterNY United States CAIR Hong Kong Institute of Science and Innovation Hong Kong

ISBN: (纸本)9781577358800

Drone based terrorist attacks are increasing daily. It is not expected to be long before drones are used to carry out terror attacks in urban areas. We have developed the DUCK multiagent testbed that security agencies can use to simulate drone-based attacks by diverse actors and develop a combination of surveillance camera, drone, and cyber defenses against them. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Terrorism

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Scavenger: Better Space-Time Trade-Offs for Key-Value Separated LSM-trees 40

Scavenger: Better Space-Time Trade-Offs for Key-Value Separa...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhang, Jianshun Wang, Fang Qiu, Sheng Wang, Yi Ou, Jiaxin Huang, Junxun Li, Baoquan Fang, Peng Feng, Dan School of Computer Science and Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China China Shenzhen Huazhong University of Science and Technology Research Institute China Bytefrance Inc.

ISBN: (纸本)9798350317152

Key-Value Stores (KVS) implemented with log-structured merge-tree (LSM-tree) have gained widespread ac-ceptance in storage systems. Nonetheless, a significant challenge arises in the form of high write amplification due to the compaction process. While KV-separated LSM-trees successfully tackle this issue, they also bring about substantial space am-plification problems, a concern that cannot be overlooked in cost-sensitive scenarios. Garbage collection (GC) holds significant promise for space amplification reduction, yet existing GC strategies often fall short in optimization performance, lacking thorough consideration of workload characteristics. Additionally, current KV-separated LSM-trees also ignore the adverse effect of the space amplification in the index LSM-tree. In this paper, we systematically analyze the sources of space amplification of KV-separated LSM-trees and introduce Scavenger, which achieves a better trade-off between performance and space amplification. Scavenger initially proposes an I/O-efficient garbage collection scheme to reduce I/O overhead and incorporates a space-aware compaction strategy based on compensated size to minimize the space amplification of index LSM-trees. Extensive experiments show that Scavenger significantly improves write performance and achieves lower space amplification than other KV-separated LSM-trees (including BlobDB, Titan, and TerarkDB). © 2024 IEEE.

关键词： Economic and social effects

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Effective Heart Attack Prediction method using Machine Learning Algorithm

Effective Heart Attack Prediction method using Machine Learn...

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International Conference on Communications and Signal Processing

作者： M.G. Chitra Ramya Govindaraj School of Computer Science Engineering and Information System (SCORE) Vellore Institute of Technology Vellore TN India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In today’s era development cardiovascular disease is the most common disease that cause a heart attack. So, diagnosing heart disease patients on time is the most challenging task in the medical field. Due to expensive treatment the poor people prohibit to take over heart disease treatment. The cardiologist’s doctors generally predict the patient’s heart attack with the help of a scrutinized patient report, electrocardiography, blood tests, computer report based on the patient’s previous database before seriousness of patient diseases. This study aims to analyze different machine learning (ML) algorithms that are currently available for predicting heart attacks, including decision trees, Random Forest, XGBoost, K-Nearest Neighbors, Support Vector Machines, and Logistic Regression. Finally, we obtained the best algorithm from this existing work based on accuracy, precision and recall.

关键词： Support vector machines Heart Machine learning algorithms Signal processing algorithms Cardiac arrest Predictive models Prediction algorithms

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Q-learning Based Meta-Heuristics for Scheduling Bi-Objective Surgery Problems with Setup Time

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Complex system Modeling and Simulation 2024年第4期4卷 321-338页

作者： Ruixue Zhang Hui Yu Adam Slowik Kaizhou Gao School of Computer Science Liaocheng UniversityLiaocheng 252000China Macao Institute of System Engineering Macao University of Science and TechnologyMacao 999078China the Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin 75-453Poland

Since the increasing demand for surgeries in hospitals,the surgery scheduling problems have attracted extensive *** study focuses on solving a surgery scheduling problem with setup *** a mathematical model is created to minimize the maximum completion time(makespan)of all surgeres and patient waiting time,*** time by the fatigue effect is included in the surgery time,which is caused by doctors’long working ***,four mate-heuristics are optimized to address the relevant *** novel strategies are designed to improve the quality of the initial *** improve the convergence of the algorithms,seven local search operators are proposed based on the characteristics of the surgery scheduling ***,Q-learning is used to dynamically choose the optimal local search operator for the current state in each ***,by comparing the experimental results of 30 instances,the *** based local search strategy's effectiveness is *** all the compared algorithms,the improved artificial bee colony(ABC)with Q-leaming based local search has the best competiiveness.

关键词： surgery scheduling meta-heuristics Q-earning makespan

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Scaffolding Process-Aware Information systems with the AKIP Platform 1

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18th International Conference on Web Information systems and Technologies, WEBIST 2022

作者： Telemaco Neto, Ulisses Oliveira, Toacy Pillat, Raquel Alencar, Paulo Cowan, Don Melo, Glaucia David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada System Engineering and Computing Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9783031430886

ISBN: (纸本)9783031430879

Business Process Automation (BPA) refers to the automation of business processes through technology, with the goal of improving efficiency, reducing errors, and increasing productivity by eliminating manual and repetitive tasks. Over the past few years, the number of BPA platforms has increased, including both open-source and proprietary solutions. However, some challenges and limitations still exist related to the adoption of these solutions, such as vendor lock-in, limited UI/UX, limited integration, outdated technology stack, and lack of support for non-process features. To address these issues, this paper presents the AKIP platform, an open-source framework for developing process-aware information systems (PAISs) using code generation techniques. AKIP generates functional process-aware web applications from BPMN business process models, making it the only known software tool capable of generating fully functional process-aware web applications. To evaluate the effectiveness of the AKIP platform, a case study was conducted in the industry and six business processes automated into a process-oriented web application were analyzed in this paper. The study showed that the AKIP platform was able to generate a functional web application while supporting the automation of business processes with different model sizes and complexities. Furthermore, this study showed that even a team of professionals with little experience was able to produce such results by using the AKIP platform. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Automation

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Machine Learning Techniques with LowDimensional Feature Extraction for Improving the Generalizability of Cardiac Arrhythmia

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-10页

作者： Nurmaini, Siti Tutuko, Bambang Rachmatullah, Muhammad Naufal Darmawahyuni, Annisa Firdaus, Firdaus A Professor of Computer Engineering Intelligent System Research Group Universitas Sriwijaya Palembang30139 Indonesia A lecturer of Computer Engineering Department Faculty of Computer Science Universitas Sriwijaya Palembang30139 Indonesia A research assistant of Intelligent System Research Group Universitas Sriwijaya Palembang30139 Indonesia

Automatic heartbeat classification is an important stage in identifying cardiac arrhythmia. Several machine learning (ML) techniques have been proposed to perform this, but they produce an accuracy result of below 99%. In this study, a deep neural network (DNN) structure is applied to improve ML performance. The feature selection method is based on the combination of discrete wavelet transform (DWT) and principal component analysis (PCA). To avoid computational complexity, the components of PCA are derived by low-dimensional DWT coefficients. The results show that the proposed ML model achieves good performance, producing 99.76% accuracy, 91.80% sensitivity, 99.78% specificity, 93.02% precision, and 92.31% F1-score. To benchmark the proposed model, the support vector machine (SVM) and random forest (RF) techniques are used as the baseline models. The DNNs are 2.3% more sensitive than SVM, while the RF fails to classify the ECG heartbeat. Four datasets are used to analyze the robustness and generalization performance of the proposed model: MIT-BIH, SVDB, MITDB, and IncartDB. All testing results produce satisfying performance. The proposed ML model offers a potential solution to improve the generalizability of a DNN-based model in different cardiac datasets for classifying tasks. © 2021. All Rights Reserved.

关键词： Principal component analysis

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RGraph: Asynchronous graph processing based on asymmetry of remote direct memory access

RGraph: Asynchronous graph processing based on asymmetry of ...

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作者： Chen, Hanhua Yuan, Jie Jin, Hai Wang, Yonghui Wu, Sijie Jiang, Zhihao National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

The scale of real-world graphs is constantly growing. To deal with large-scale graphs, distributed graph processing has attracted much research efforts. Existing distributed graph processing systems are commonly built on traditional TCP/IP communication stack, which leads to network bottleneck because of low bandwidth and heavy kernel stack operations. Meanwhile, in real power-law graphs, the average number of mirror vertices after graph partitioning is very large, resulting in significant communication overhead among nodes. The emerging high-performance Remote Direct Memory Access (RDMA) network has the features of low latency, high bandwidth, and low CPU overhead, which brings new opportunities for distributed graph processing systems. Existing RDMA-assisted graph processing systems focus on synchronous execution, which imposes barriers between consecutive iterations. Synchronous execution transfers bulk data among nodes and thus only needs a small number of network transfers. However, synchronous execution is usually less efficient than asynchronous execution because of bulk synchronization. Asynchronous execution accelerates graph processing by eliminating barriers, which in turn requires to transfer a large amount of small size data. In this paper, we propose RGraph, an RDMA-assisted asynchronous distributed graph processing system. RGraph distributes edges into two parts to isolate master and mirror vertices. RGraph exploits the asymmetry of RDMA to accelerate the one-to-many communication between master and mirror vertices. We implement RGraph on top of PowerGraph and conduct comprehensive experiments with large-scale real graphs to evaluate its performance. Results show that compared to existing designs, RGraph reduces the execution time by up to 81%. © 2021 John Wiley & Sons Ltd.

关键词： Mirrors

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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

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computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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