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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Rajkumar, M. Madhan, E.S. Karthikeyan, D. Gopal, Mohanraj Nithish, G. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore Campus Tamil Nadu India Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore632014 India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore Campus Tamil Nadu India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Tamil Nadu Vellore632104 India Vellore Institute of Technology Vellore Campus Tamil Nadu India

ISBN: (纸本)9798350389609

Ensuring the well-being of foetuses during pregnancy is crucial for preventing child and maternal mortality, aligning with the United Nations' Sustainable Development Goals. Utilizing a dataset of 2126 records from Cardiotocography (CTG) exams, obtained from the Kaggle repository, the study employs machine learning ensemble techniques, specifically stacking with 10x10 validations, to increase the classification of fetal health's precision and dependability. To address class imbalance, the dataset is resampled to 150%, and filter-based feature selection using the chi-squared statistical test is applied. Base models, including Decision Trees (C4.5), Random Forest (100 trees), Logistic Regression, and Sequential Minimal Optimization (SMO), are utilized. With the help of the Weka Machine Learning software tool, the ensemble model, after hyper parameter tuning and employing diverse techniques, with 98.68% accuracy, the model effectively classifies fetal health, indicating robust performance across various evaluation metrics. © 2024 IEEE.

关键词： Electronic health record

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A deep Q-learning model for sequential task offloading in edge AI systems

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Intelligent and Converged Networks 2024年第3期5卷 207-221页

作者： Dong Liu Shiheng Gu Xinyu Fan Xu Zheng Department of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 610000China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 515100China Department of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610000China

Currently,edge Artificial Intelligence(AI)systems have significantly facilitated the functionalities of intelligent devices such as smartphones and smart cars,and supported diverse applications and *** fundamental supports come from continuous data analysis and computation over these *** the resource constraints of terminal devices,multi-layer edge artificial intelligence systems improve the overall computing power of the system by scheduling computing tasks to edge and cloud servers for *** efforts tend to ignore the nature of strong pipelined characteristics of processing tasks in edge AI systems,such as the encryption,decryption and consensus algorithm supporting the implementation of Blockchain ***,this paper proposes a new pipelined task scheduling algorithm(referred to as PTS-RDQN),which utilizes the system representation ability of deep reinforcement learning and integrates multiple dimensional information to achieve global task ***,a co-optimization strategy based on Rainbow Deep Q-Learning(RainbowDQN)is proposed to allocate computation tasks for mobile devices,edge and cloud servers,which is able to comprehensively consider the balance of task turnaround time,link quality,and other factors,thus effectively improving system performance and user *** addition,a task scheduling strategy based on PTS-RDQN is proposed,which is capable of realizing dynamic task allocation according to device *** results based on many simulation experiments show that the proposed method can effectively improve the resource utilization,and provide an effective task scheduling strategy for the edge computing system with cloud-edge-end architecture.

关键词： edge computing task scheduling reinforcement learning Rainbow Deep Q-Learning(RainbowDQN)

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Intrusion Detection Mechanism Using Deep Learning 5

Intrusion Detection Mechanism Using Deep Learning

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Ananthi, P. Nirmaladevi, K. Naveen Kumar, S. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Madanapalle Institute of Technology & Science Department of Computer Applications India M.Sc Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798331518097

In today's connected and data-driven world, networks and digital systems need to be protected from malicious attacks. The effectiveness of conventional Intrusion Detection systems (IDS) in recognizing and impeding novel threats and intricate methodologies has its limits. This study introduces a new method for improving network security by using deep learning techniques in the design and development of an intrusion detection system (IDS). The suggested Intrusion Detection System uses deep learning techniques, specifically Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs), to identify and react to known and new security threats. Data can be taught to be encoded into a lower-dimensional representation and then decoded back into its original form using autoencoders (AEs). Autoencoders are a cutting-edge intrusion detection system that can lower false positives and raise overall detection accuracy because of their capacity to automatically learn from and adjust to the ever-changing threat landscape. They enhance computer networks capacity to spot irregularities and possible security threats. With the deep learning model obtaining a high accuracy of 99% in real-world threat detection, the results show that deep learning is effective in developing intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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ARCH-COMP23 Repeatability Evaluation Report 10th

ARCH-COMP23 Repeatability Evaluation Report

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10th International Workshop on Applied Verification of Continuous and Hybrid systems, ARCH 2023

作者： Johnson, Taylor T. Vanderbilt University Department of Computer Science Institute for Software Integrated Systems NashvilleTN United States

The repeatability evaluation for the 7th International Competition on Verifying Continuous and Hybrid systems (ARCH-COMP’23) is summarized in this report. The competition took place as part of the workshop Applied Verification for Continuous and Hybrid systems (ARCH) in 2023, affiliated with the 2023 Cyber-Physical systems and Internet of-Things Week (CPS-IoT Week). In its seventh edition, tools submitted artifacts through a new automated evaluation system and were synchronized with a Git repository for the repeatability evaluation and archiving, which were applied to solve benchmark instances through different competition categories. Due to procedural changes in execution through the automated system, fewer participants than in past iterations participated in the repeatability evaluation this year. The process was generally to submit scripts to automatically install and execute the tools in containerized virtual environments (specifically Docker files to execute within Docker containers, along with execution scripts). With the automated evaluation system, most participating categories presented performance evaluation information from this common execution platform. © 2023, EasyChair. All rights reserved.

关键词： Hybrid systems

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Framework for Effective Utilization of Distributed Scrum in software Projects

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computer systems science & engineering 2023年第1期44卷 407-422页

作者： Basit Shahzad Wardah Naeem Awan Fazal-e-Amin Ahsanullah Abro Muhammad Shoaib Sultan Alyahya Department of Software Engineering National University of Modern LanguagesIslamabad44000Pakistan Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science Sukkur IBA UniversitySindh65200Pakistan Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

There is an emerging interest in using agile methodologies in Global software Development(GSD)to get the mutual benefits of both *** is currently admired by many development teams as an agile most known meth-odology and considered adequate for collocated *** the same time,stake-holders in GSD are dispersed by geographical,temporal,and socio-cultural *** to the controversial nature of Scrum and GSD,many significant challenges arise that might restrict the use of Scrum in *** conducted a Sys-tematic Literature Review(SLR)by following Kitchenham guidelines to identify the challenges that limit the use of Scrum in GSD and to explore the mitigation strategies adopted by practitioners to resolve the *** validate our reviewfindings,we conducted an industrial survey of 305 *** results of our study are consolidated into a research *** framework represents current best practices and recommendations to mitigate the identified distributed scrum challenges and is validated byfive experts of distributed *** of the expert review were found supportive,reflecting that the framework will help the stakeholders deliver sustainable products by effectively mitigating the identified challenges.

关键词： Global software development distributed Scrum sustainable development challenges mitigation strategies

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A Web-Based Vaccine Distribution System for Covid-19 Using Vaxallot 4th

A Web-Based Vaccine Distribution System for Covid-19 Using V...

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4th EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2023

作者： Valarmathi, B. Srinivasa Gupta, N. Prakash, G. BarathyKolappan, A. Padmavathy, N. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Database Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Systems Engineer Tata Consultancy Services Tamil Nadu Chennai India Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram India

ISBN: (纸本)9783031488900

Vaxallot seeks to implement a system to distribute vaccines across high-risk groups accounting for various parameters and prove to be superior to what conventional systems are capable of today. It is a Python flask-based tool backed by infrastructure and data resources from the Covid India central repository;all it needs is a single channel input and a single parameter of the value produced, and the algorithm will take care of the rest. Since it’s Python-based and has an active integration with google sheets, live value updating could be possible for the real-time output of the distribution. The novelty of the proposed mechanism is the unique priority index, a score that accounts for an array of factors associated with the pandemic and is computed for regions in question here;this makes way for better distribution of vaccines. The application has an exclusive segment centered on handling excess units, if any. Moreover, since the application is developed to suit the needs of dynamic demographics, any region can roll out this application for purposes they desire to serve the masses. Since it isn’t bound by a coronavirus, it can be used by the healthcare industry as they deem fit. © 2024, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Coronavirus

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Predicting the Air Pollution Level and Creating an Awareness Platform 5

Predicting the Air Pollution Level and Creating an Awareness...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Nuha, M.N. Wishvajith, G.K.N.P. Caldera, Amitha Weerasinghe, N.T. Weeratunga, G.S. Lokuliyana, Shashika Sri Lanka Institute of Infromation Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Infromation Technology Department of Computer Systems & Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study proposes a comprehensive method to reduce the problems posed by global air pollution. To measure pollution levels and raise public awareness, the research uses a comprehensive strategy that incorporates image processing, time series analysis, chatbot, and facemask recognition. Time series analysis is used to build prediction models by recognizing historical patterns, and image processing techniques are used to examine cloud pictures for precise pollution predictions. Chatbots provide real time communication and may educate the public on environmental facts, health risks, and safety procedures. Additionally, it has a facemask identification system that monitors and promotes the use of masks. Importantly this not only encourages users to take action to reduce pollution but also utilizes predictive models to help mitigate pollution's consequences. These components result in improved public health, environmental conservation, and sustainability. The outcomes reflect significant advantages, with evidence of accuracy and its positive impact on both awareness and proactive measures to combat air pollution. © 2023 IEEE.

关键词： Time series analysis

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Light field depth estimation:A comprehensive survey from principles to future

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High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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An attention-based channel estimation network with residual mechanism for 5G OFDM systems 4

An attention-based channel estimation network with residual ...

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4th International Conference on Electronic Communication, computer science and Technology, ECCST 2024

作者： Jiang, Jinjing Huang, Weipeng Zhang, Xiao Yang, Kun Peng, Tong School of Information Engineering Zhejiang Ocean University China School of Computer Science and Software Engineering Shenzhen Institute of Information Technology China

Due to the significant increase in data transmission speed and gradual increase in Doppler frequency shift, channel estimation accuracy has become one of the most prioritized considerations in many cases. Specifically, we propose an attention-based channel estimation network with a residual mechanism, named TRCE-Net. In our design, the use of the multi-head attention mechanism at the input of the encoder is to obtain richer feature details. A linear series decoder module is implemented by leveraging multiple residual networks with skip connections. This novel design is capable of improving the channel estimation accuracy after deepening the network while effectively preventing degradation. To lessen the number of parameters that the network needs to learn, we introduce the bi-linear interpolation mechanism to replace the traditional up-sampling layer to mitigate both computational overhead and model complexity. According to the simulation results, our proposed TRCE-Net is superior to other state-of-the-art methods with regard to estimation accuracy and robustness to different maximum Doppler shifts, such as ResNet and InterpolateNet. © Published under licence by IOP Publishing Ltd.

关键词： Orthogonal frequency division multiplexing

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An Empirical Comparison of Machine and Deep Learning Algorithms for Predicting Maternal Health Risk 5th

An Empirical Comparison of Machine and Deep Learning Algorit...

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5th EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2024

作者： N, Srinivasa Gupta Valarmathi, B. Kavitha, A. Gopini, Sai Bhuvan Seela, Hari Kumar Department of Manufacturing Engineering School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Mathematics School of Advanced of Sciences Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9783031770777

Maternal health is among the greatest challenges in the world, especially in rural areas as there lack medical practitioners, they do not have easily accessible publics clinics and transport is difficult. Therefore, high rates of maternal as well as infant morbidity and mortalities are recorded. This research utilizes Artificial Intelligence (AI) with machine learning algorithms to forecast and address maternal health hazards right at their onset stage. The current research utilizes the concept of AI along with many Machine Learning (ML) methods like the Ensemble Learning Model (ELM), Random Forest (RF), K-Nearest Neighbour (KNN), Decision-Tree (DT), XG-Boost (XGB), Cat Boost (CB), and Gradient Boosting (GB), along with Synthetic Minority Over-sampling Technique (SMOTE) algorithm used for dealing with the problem class imbalance within the data set. SMOTE algorithm is utilized for the dataset balancing process. The handling system involves refining data preprocessing with the help of feature engineering and robust data cleaning which makes sure that anomalies do not erode the reliability of the predictive model. The existing methods [1] used RF (90%), DT (87%), XGB (85%), CB (86%), and GB (81%) algorithms and were compared with the accuracies of the proposed models like Logistic Regression (LR), Ensemble Learning Bagging (ELB), Ensemble Learning Stacking (ELS), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The existing methods used only imbalance dataset. The accuracies of the proposed models with using SMOTE algorithm (balanced dataset) are LR (61.33%), KNN (81%), ELB (92.33%), ELS (90.66%) CNN (40.67%), RNN (59.67%), LSTM (54%), GRU (56%) respectively. Among these methods, ELB achieved 92.33% of accuracy with using SMOTE algorithm using imbalanced dataset. Whereas the accuracies of the proposed models without using SMOTE algorithm (imbalanced dataset) are LR (66.09%), KNN (68.47%)

关键词： Convolutional neural networks

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