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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Sandoval, Jorge Ignacio Céspedes, Sandra González, Agustín Torreblanca, Diego Bugueño-Córdova, Ignacio University of Chile Department of Electrical Engineering Santiago Chile Concordia University Department of Computer Science & Software Eng. Montreal Canada

ISBN: (纸本)9798350356717

The introduction of the next generations' mobile communications, 5 G -Advance and 6G (5G-A/6G), promises boosting data throughput to new dimensions, achieving submillisecond latency, and providing wider coverage. Based on this promise, a great number of previously infeasible high-throughput, real-time, and IoT-based applications, such as high-resolution face recognition and extended reality, are being developed for deployment over 5G-A/6G networks. Such demanding applications assume that ample bandwidth will be available through the utilization of a high-frequency spectrum. However, at high frequencies, radio channels are susceptible to sudden changes in the surrounding conditions, generating highly fluctuating scenarios that directly impact the performance of upperlayer protocols and services. For applications that operate under end-to-end congestion control algorithm (CCA) (e.g., TCP- and QUIC-based applications), extreme fluctuations may generate unwanted behaviors that hurt the throughput and possibly favor non-CCA traffic with unfair results in bandwidth distribution. This paper thoroughly investigates the impact of fluctuating radio access channels on 5G-A/6G networks. We analyze the performance of various congestion control algorithms, including CUBIC, High-Speed, and BBR, as well as non-CCA traffic, under such conditions. Our evaluation, conducted through realistic simulations, examines the network's ability to maintain desired service levels amidst fluctuations. Furthermore, we explore the potential of state-of-the-art active queue management and buffer management policies at the gNB to mitigate the negative effects of these fluctuations and enhance overall network performance. © 2024 IEEE.

关键词： 5G mobile communication systems

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Intelligent Daycare: Enhancing Child Safety with IoT and Machine Learning Innovations 9

Intelligent Daycare: Enhancing Child Safety with IoT and Mac...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Prathyanga, Amisha Shyaminda, Pasindu Chamikara, Pasan Lakshan, Sachin Thelijjagoda, Samantha Kasthurirathna, Dharshana Sri Lanka Institute of Information Technology Department of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350377972

Child safety and well-being in daycare settings are important, thus it is vital that the persons in charge be attentive to them. This research tries to solve the problem by introducing a smart and detailed monitoring system that utilizes new technology to enhance safety and support caretakers. The system has various innovative features, such as child missing detection, behavior and facial expression analysis, cry analysis and categorization, and fall detection. Using high-resolution cameras and on-device Edge AI capabilities, it ensures real-time surveillance of children's activities while protecting privacy. The approach integrates behavior and facial expression analysis to assess emotional states, cry recognition to discover childcare needs, as well as a useful fall detection system that can tell the difference between falls and other movements. All these parts synergize to hand over the necessary information to the caregivers on time and in a way that they can act upon. The quantitative experiments in real-time childcare centers show high accuracy through all the components, attesting to the system's reliability and durability. This research has important significance in improving the safety and the frequency response in daycare centers because it gives caregivers automated tools to quickly address the needs of children. While promising, the system has drawbacks, such as potential biases in data models and a reliance on ideal environmental circumstances. Future work intends to improve flexibility across varied daycare situations, improve accuracy through larger datasets, and investigate ethical frameworks for wider application. © 2024 IEEE.

关键词： Fall detection

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CRAYM: Neural Field Optimization via Camera RAY Matching 38

CRAYM: Neural Field Optimization via Camera RAY Matching

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Lin, Liqiang Wu, Wenpeng Fu, Chi-Wing Zhang, Hao Huang, Hui College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Computing Science Simon Fraser University Canada

We introduce camera ray matching (CRAYM) into the joint optimization of camera poses and neural fields from multi-view images. The optimized field, referred to as a feature volume, can be "probed" by the camera rays for novel view synthesis (NVS) and 3D geometry reconstruction. One key reason for matching camera rays, instead of pixels as in prior works, is that the camera rays can be parameterized by the feature volume to carry both geometric and photometric information. Multi-view consistencies involving the camera rays and scene rendering can be naturally integrated into the joint optimization and network training, to impose physically meaningful constraints to improve the final quality of both the geometric reconstruction and photorealistic rendering. We formulate our per-ray optimization and matched ray coherence by focusing on camera rays passing through keypoints in the input images to elevate both the efficiency and accuracy of scene correspondences. Accumulated ray features along the feature volume provide a means to discount the coherence constraint amid erroneous ray matching. We demonstrate the effectiveness of CRAYM for both NVS and geometry reconstruction, over dense- or sparse-view settings, with qualitative and quantitative comparisons to state-of-the-art alternatives. © 2024 Neural information processing systems foundation. All rights reserved.

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Forecasting Mental Stress Using Machine Learning Algorithms

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computers, Materials & Continua 2022年第9期72卷 4945-4966页

作者： Elias Hossain Abdulwahab Alazeb Naif Almudawi Sultan Almakdi Mohammed Alshehri M.Gazi Golam Faruque Wahidur Rahman Department of Software Engineering Daffodil UniversityDhakaBangladesh Collage of Computer Science and Information System University of NajranNajranSaudi Arabia Department of Computer Science and Engineering Khwaja Yunus Ali UniversitySirajganj 6751Bangladesh

Depression is a crippling affliction and affects millions of individuals around the *** general,the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts,which results in lower costs and improved patient ***,this strategy can necessitate a lot of buy-in from a large number of people,as well as additional training and logistical ***,utilizing the machine learning algorithms,patients with depression based on information generally present in a medical file were analyzed and *** methodology of this proposed study is divided into six parts:Proposed Research Architecture(PRA),Data Pre-processing Approach(DPA),Research Hypothesis Testing(RHT),Concentrated Algorithm Pipeline(CAP),Loss Optimization Stratagem(LOS),and Model Deployment Architecture(MDA).The Null Hypothesis and Alternative Hypothesis are applied to test the *** addition,Ensemble Learning Approach(ELA)and Frequent Model Retraining(FMR)have been utilized for optimizing the loss ***,the Features Importance Interpretation is also delineated in this *** forecasts could help individuals connect with expert mental health specialists more quickly and *** to the findings,71%of people with depression and 80%of those who do not have depression can be appropriately *** study obtained 91%and 92%accuracy through the Random Forest(RF)and Extra Tree *** after applying the Receiver operating characteristic(ROC)curve,79%accuracy was found on top of RF,81%found on Extra Tree,and 82%recorded for the eXtreme Gradient Boosting(XGBoost)***,several factors are identified in terms of predicting depression through statistical data *** the additional effort is needed to develop a more accurate model,this model can be adjustable in the healthcare sector for diagnosing depression.

关键词： Depression analysis mental health machine learning model deployment XGBoost ROC-AUC

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A One-Time Encryption Consensus Algorithm Based on (t, n) Threshold Secret Sharing

A One-Time Encryption Consensus Algorithm Based on (t, n) Th...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Fanchang Yadav, Amit Khan, Asif Chengdu Neusoft University Department Of Software Engineering Chengdu China College Of Engineering It And Environment Charles Darwin University NT Australia Integral University Department Of Computer Application Lucknow India

ISBN: (纸本)9798350371567

A one-time encryption consensus algorithm based on (t, n) threshold secret sharing is proposed to address the performance and security issues of consensus mechanisms in blockchain applications. The theoretical basis of this algorithm is the chaos algorithm, finite state automata, secure multi-party computation, and lagrange interpolating formula in cryptography. After analyzing the security of the threshold secret sharing scheme, it can resist various attacks and achieve a one-time encryption security level, It also has the characteristics of key dynamism and nodes management flexibility, ensuring the strong security of the proposed consensus algorithm. © 2024 IEEE.

关键词： Cryptography

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Investigation of Single and Multiple Mutations Prediction Using Binary Classification Approach

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Intelligent Automation & Soft Computing 2023年第4期36卷 1189-1203页

作者： T.Edwin Ponraj J.Charles Department of Computer Applications Noorul Islam Centre for Higher EducationKumaracoil629180India Department of Software Engineering Noorul Islam Centre for Higher EducationKumaracoil629180India

The mutation is a critical element in determining the proteins’stability,becoming a core element in portraying the effects of a drug in the pharmaceutical *** wet laboratory tests to provide a better perspective on protein mutations is expensive and time-intensive since there are so many potential muta-tions,computational approaches that can reliably anticipate the consequences of amino acid mutations are *** work presents a robust methodology to analyze and identify the effects of mutation on a single protein ***,the context in a collection of words is determined using a knowledge graph for feature selection *** proposed prediction is based on an easier and sim-pler logistic regression inferred binary classiﬁcation *** approach can able to obtain a classiﬁcation accuracy(AUC)Area Under the Curve of 87%when randomly validated against experimental energy ***,for each cross-fold validation,the precision,recall,and F-Score are *** results support the validity of our strategy since it performs the vast majority of prior studies in this domain.

关键词： Proteins data science mutation analysis random forest neighbor proteins single and double mutations

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Residual Convolutional Graph Neural Network with Subgraph Attention Pooling

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Tsinghua Science and Technology 2022年第4期27卷 653-663页

作者： Yutai Duan Jianming Wang Haoran Ma Yukuan Sun Information and Communication Engineering Department Tiangong UniversityTianjin 300387China Computer Science Department Tiangong UniversityTianjin 300387China Software Engineering Department Tiangong UniversityTianjin 300387China Center for Engineering Intership and Training Tiangong UniversityTianjin 300387China

The pooling operation is used in graph classification tasks to leverage hierarchical structures preserved in data and reduce computational ***,pooling shrinkage discards graph details,and existing pooling methods may lead to the loss of key classification *** this work,we propose a residual convolutional graph neural network to tackle the problem of key classification features ***,our contributions are threefold:(1)Different from existing methods,we propose a new strategy to calculate sorting values and verify their importance for graph *** strategy does not only use features of simple nodes but also their neighbors for the accurate evaluation of its importance.(2)We design a new graph convolutional layer architecture with the residual *** feeding discarded features back into the network architecture,we reduce the probability of losing critical features for graph classification.(3)We propose a new method for graph-level *** messages for each node are aggregated separately,and then different attention levels are assigned to each node and merged into a graph-level representation to retain structural and critical information for *** experimental results show that our method leads to state-of-the-art results on multiple graph classification benchmarks.

关键词： graph neural network graph pooling information loss

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Storage and Querying of Spatiotemporal Data in Blockchain

Storage and Querying of Spatiotemporal Data in Blockchain

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

ISBN: (纸本)9798350371567

A storage structure C-L Matrix based on linked lists and matrices is proposed to address the issue of spatiotemporal data not fully utilizing its temporal and spatial attributes in blockchain, and efficient queries are implemented on this storage structure to meet the needs of fast on chain and large-scale transaction data queries in blockchain applications. It is also seamlessly integrated with the security features of blockchain, such as decentralization, tamper resistance, and traceability. After research analysis and comparison, C-L Matrix has significant advantages over F-L Tree in terms of spatial and temporal complexity;The C-L Matrix also solves cross domain, spatial data distribution, and temporal data chain problems, which can well support the application and management of spatiotemporal data in blockchain. © 2024 IEEE.

关键词： Blockchain

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Enhancing software Cost Estimation Using Feature Selection and Machine Learning Techniques

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computers, Materials & Continua 2024年第12期81卷 4603-4624页

作者： Fizza Mansoor Muhammad Affan Alim Muhammad Taha Jilani Muhammad Monsoor Alam Mazliham Mohd Su’ud Department of Software Engineering FAST-National University of Computer&Emerging SciencesKarachi75030Pakistan Faculty of Engineering Science and Technology IQRA UniversityKarachi72500Pakistan Department of Computer Science Bahria UnivesityKarachi74800Pakistan Faculty of Computing Riphah International UniversityIslamabad44600Pakistan Faculty of Computing and Informatics Multimedia University(MMU)Cyberjaya63100SelangorMalaysia

software cost estimation is a crucial aspect of software project management,significantly impacting productivity and *** research investigates the impact of various feature selection techniques on software cost estimation accuracy using the CoCoMo NASA dataset,which comprises data from 93 unique software projects with 24 *** applying multiple machine learning algorithms alongside three feature selection methods,this study aims to reduce data redundancy and enhance model *** findings reveal that the principal component analysis(PCA)-based feature selection technique achieved the highest performance,underscoring the importance of optimal feature selection in improving software cost estimation *** is demonstrated that our proposed method outperforms the existing method while achieving the highest precision,accuracy,and recall rates.

关键词： Machine learning software cost estimation PCA hyper parameter feature selection

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Programming Education for Young People using the Falling-Puzzle Game, 'Puyo Puyo' 14

Programming Education for Young People using the Falling-Puz...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Saito, Daisuke Tian, Ruochen Washizaki, Hironori Fukazawa, Yoshiaki Waseda University Global Software Engineering Laboratory Tokyo Japan Waseda University Department of Computer Science and Engineering Tokyo Japan

ISBN: (纸本)9798350399431

This study utilizes the game rules of a falling-puzzle game, developed as a consumer-oriented digital game, in programming education for young people. When digital games are used in programming education, they are often designed specifically for that purpose. In this study, we focused on the game rules of the consumer falling-object puzzle game, 'Puyo Puyo.' Learning impact was investigated on 23 Japanese elementary, junior high, and senior high school students. The results show that the concepts of branching and arraying in programming were taught effectively;thus, consumer digital game rules can be used as a case study for programming education. © 2023 IEEE.

关键词： E-learning

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