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1-Complex s, t Hamiltonian Paths: Structure and Reconfiguration in Rectangular Grids

Journal of Graph Algorithms and Applications 2023年第4期27卷 281-327页

作者： Nishat, Rahnuma Islam Srinivasan, Venkatesh Whitesides, Sue Department of Computer Science Mathematics Physics and Statistics University of British Columbia KelownaBC Canada Department of Computer Science University of Victoria VictoriaBC Canada

We give a complete structure theorem for 1-complex s, t Hamiltonian paths in rectangular grid graphs. We use the structure theorem to design an algorithm to reconfigure one such path into any other in linear time, mak... 详细信息

关键词： Hamiltonians

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Prediction in Multivariate Time Series Data using Generative Adversarial Network 15

Prediction in Multivariate Time Series Data using Generative...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Medhi, Shrabani Kashyap, Samudra Das, Nitul Girijananda Chowdhury University Department of Computer Science and Engineering Guwahati India Girijananda Chowdhury Institute of Management and Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331300579

Air pollution is an issue of great concern. PM2.5 is the most dangerous pollutant out of all the pollutants. A large number of missing values is present in multivariate pollution data. This makes the prediction of PM2.5 concentration very difficult. Traditional approaches to deal with missing values includes mean imputation, median imputation, case deletion, matrix factorization-based imputation, case deletion, etc. All these methods are not capable of modelling the temporal dependencies and complex distributed nature of multivariate pollution data. We have dealt with missing values by mean imputation, median imputation, Generative Adversarial Network imputation and Conditional Tabular Generative Adversarial Network. We have used Artificial Neural Network to predict PM2.5 concentration. The results reveal that for time series data, imputation by median model has performed waste. No significance improvement is seen in prediction of PM2.5 concentration using GAN and CTGAN model if we compare it with imputation by mean model. For time series data, more research work needs to be done with GAN and its variants so that better results can be achieved. © Grenze Scientific Society, 2024.

关键词： Generative adversarial networks

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A novel approach for the enhancement of payload capacity in pixel value differencing image steganography schemes

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International Journal of Information technology (Singapore) 2024年第8期16卷 4779-4786页

作者： Nisha, C.D. Monoth, Thomas Department of Computer Science Mary Matha Arts and Science College Kannur University

Image steganography is the technique of masking secret information inside a carrier medium without disclosing the presence of the secret. Image steganography considers images to be carriers. Pixel Value Differencing (PVD) steganography is a spatial domain image steganographic technique in which secret messages are transformed into a sequence of bits, which are then concealed in the difference value of pixel intensities. The evaluation of an image steganography system typically hinges upon three pivotal metrics: hiding capacity, imperceptibility, and robustness. In this study, we aim to enhance the payload capacity of the system by modifying the pixel differences between each pair of pixels using the entropy value of the cover image. The findings from our experiments suggest a marked enhancement in payload capacity compared to the traditional PVD process, while maintaining satisfactory visual quality. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Entropy Image steganography Payload capacity Pixel value differencing Spatial domain

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Malaria Detection Using Deep Learning Techniques 4

Malaria Detection Using Deep Learning Techniques

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Singh, Bhumi Dhanda, Namrata Verma, Rajat Amity University Uttar Pradesh Department of Computer Science and Engineering Lucknow India Pranveer Singh Institute of Technology Department of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350364729

Malaria, a deadly illness caused by Plasmodium parasites is usually diagnosed by a microscopic blood test. However, this manual process consumes an extended period and is liable to mistakes made by people. To address these challenges, models are trained on a comprehensive dataset containing either malaria-infected or uninfected blood samples. This study examines the effectiveness of various models of deep learning, particularly Convolutional Neural Networks (CNN), ResNet50, VGGNet19, and Inception V3, for the detection of malaria from blood smear images. The dataset is carefully curated to ensure a balanced representation of both classes, improving the ability of the Models can generalise successfully to new, previously unknown data. The ResNet50 is recognised for its deep residual learning framework, which has an accuracy of 94%, which has the highest accuracy. VGGNet19, known for its simplicity and depth, and Inception V3, known for its efficient multi-scale feature extraction, were all compared to a standard CNN. The results showed that these advanced architectures significantly outperformed traditional methods, providing a robust and reliable approach to malaria detection. This study mainly focuses on deep learning models to revolutionize the diagnostic process, making it faster, more accurate, and easier to use, especially in resource-constrained settings. © 2024 IEEE.

关键词： Convolutional neural networks

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Multi-Agent Deep Reinforcement Learning With Trajectory Prediction for Task Migration-Assisted Computation Offloading

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5839-5856页

作者： Zhang, Xinyi Wang, Chunyang Zhu, Yanmin Cao, Jian Liu, Tong Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China East China Normal University School of Data Science and Engineering Shanghai200050 China Shanghai University School of Computer Engineering and Science Shanghai200444 China

Multi-access edge computing has become an effective paradigm to provide offloading services for computation-intensive and delay-sensitive tasks on vehicles. However, high mobility of vehicles usually incurs spatio-temporal load-imbalances among edge servers. Therefore, task migration is employed to maintain dynamic workload balancing by transmitting excessive tasks from overloaded to underloaded servers. Recent studies adopt deep reinforcement learning approaches to generate offloading and migration decisions based on current observations of systems. However, we argue that the migration direction is highly dependent on vehicular movements, and task migration towards the wrong direction could lead to additional delays. Therefore, we emphasize the importance of guiding task migration via exploring prospective trajectories of vehicles. We propose a Mobility-Aware Cooperative Multi-Agent (MCMA) deep reinforcement learning approach to make vehicle-by-vehicle decisions in multi-edge computation offloading scenarios. A two-stage decision framework is designed to solve the joint optimization problem of computation offloading and resource allocation. Additionally, an Informer-based multi-step vehicular trajectory prediction module is incorporated to enhance the capability of forecasting vehicular movements. Extensive experiments and analysis are conducted on synthetic and realistic scenarios, showing that our approach consistently outperforms both heuristic and DRL-based methods. The simulation scenarios and source codes are publicly available here. © 2002-2012 IEEE.

关键词： Computation offloading

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A Research Methodology Course in a Game Development Curriculum

A Research Methodology Course in a Game Development Curricul...

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45th Annual Conference on European Association for computer Graphics, EUROGRAPHICS 2024

作者： Hu, Yan Sundstedt, Veronica Goswami, Prashant Blekinge Institute of Technology Department of Computer Science Sweden

ISBN: (纸本)9783038682387

Research methodology courses can often be considered part of a computer science curriculum. These basic or advanced-level courses are taught in terms of traditional research methods. This paper presents and discusses a research methodology course curriculum for students studying programs focusing on digital game development (more specifically, focusing on game engineering). Our research methodology course prepares students for their upcoming thesis by encouraging a research-oriented approach. This is done by exploring new research areas in game engineering as a basis for research analysis and by applying research methods practically in a smaller project. This paper presents the course structure, assignments, and lessons learned. Together with existing literature, it demonstrates important aspects to consider in teaching and learning game research methodologies. The course evaluation found that the students appreciated the interactive lectures, close staff supervision, and detailed feedback on the scientific writing process. © 2024 The Authors.

关键词： Game design

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A Review on Resource Provisioning Technique's in Cloud Environment 15

A Review on Resource Provisioning Technique's in Cloud Envir...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Ranjana, C. Anusha Bamini, A.M. Chitra, R. Division of Computer Science and Engineering Karunya Institute of Technology and Science India

ISBN: (纸本)9798331300579

Resource provisioning is the method of making sure that the cloud service can be efficiently resourced to the customers as demand increases. Resource provisioning is the procedure of selecting, deploying, and managing software like load balancers and database server management systems and hardware sources such as CPU, storage, and networks to guarantee utility performance. To do this in powerful manner, a CSP could want to take sure measures to effectively deliver on its SLAs. As an instance, the CSP may have procedures in place to feature servers or storage as call for increases. It goals to make sure that an organization can seamlessly get right of entry to the specified sources in an optimized and efficient manner. The general aim of aid provisioning is to permit the packages to utilize computational strength, garage, and offerings. Cloud aid provisioning requires the CSP to scale and manage consumer needs seamlessly the steps in resource provisioning are record Infrastructure, Configuring and booting, Provisioning API and Provisioning VMs. This paper suggests an evaluation between provisioning techniques utilized in cloud. The useful resource provisioning was formerly done with the aid of considering carrier level goals and with numerous scheduling methods Here we evaluate distinct strategies used in present day cloud computing environment. © Grenze Scientific Society, 2024.

关键词： Quality of service

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Exploring New Convolutional Neural Network Architectures for Satellite Image Categorization: A Comparative Study 18

Exploring New Convolutional Neural Network Architectures for...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Jain, Aryan Ghosh, Debjani Kumar, Prashant Kumar, Vimal School of Computer Science Engineering and Technology India

ISBN: (纸本)9798350391725

With deep learning, satellite images can accurately identify land cover elements such as annual crops, forests, herbaceous vegetation, pastures, permanent crops, and rivers. Deep learning has become a valuable tool in this regard. In this field, convolutional neural networks (CNNs) have shown great promise, with pre-trained models such as DenseNet121, EfficientNetB0, VGG16, and InceptionV3 showing encouraging outcomes. To outperform earlier CNN models, this research explores the possibilities of new CNN designs for satellite image categorization. We suggest a comparative study where these more recent architectures are refined using a dataset of satellite images. The results are shown together with a description of how well the above-mentioned CNN models work for this task. The performance is assessed based on accuracy and a discussion of the model's suitability for this task is included with the result. © 2024 IEEE.

关键词： Convolutional neural networks

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Score-Level Fusion Approach for Traffic Flow Prediction in Intelligent Transportation System 6

Score-Level Fusion Approach for Traffic Flow Prediction in I...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Axcellin, T. Kamalakkannan, S. Department of Computer Science Vel’s Institute of Science Technology and Advanced Studies Tamil Nadu Chennai India

ISBN: (纸本)9798331522667

Traffic flow prediction (TFP) for intelligent transportation system is essential for effective transportation planning and city development. Conventional models often have difficulty with the complex nature of traffic data, which includes nonlinear patterns, spatial dependencies, and external influences such as weather or road conditions. Prediction accuracy may be significantly increased by utilizing an ensembling strategy, which combines the capabilities of many models. The goal of the study is to improve TFP accuracy by integrating the results of different machine learning (ML) models. The study uses Random Forest(RF) Naïve Bayes(NB), K-Nearest Neighbor(KNN), Extreme Gradient Boosting(XGBoost) and Support vector machine(SVM) as prediction models and based on the prediction probabilities and the score level fusion is performed with RF and XGBoost ensembling. The new fusion procedure uses a weighted average technique in which weights are assigned dynamically depending on individual model performance criteria including accuracy, precision, recall, and F1-score. Higher-performing algorithms are given greater significance in the final forecast, resulting in a dynamic and data-driven assembly strategy. The findings show that the efficiency of the ensemble of RF-XGBoost is higher compared to the contrasted methods. The model also uses feature selection approaches like mutual information to find the most significant traffic-related characteristics, lowering computational cost and increasing forecast efficiency. © 2025 IEEE.

关键词： Support vector machines

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International Journal of Information and Communication technology 2024年第2期24卷 145-155页

作者： Wang, Kaiku Huang, Congkuan Yang, Yang Department of Computer Science and Technology Beijing University of Aeronautics and Astronautics Beijing100191 China Department of Electrical Engineering and Automation Hefei University of Technology Hefei230009 China

Aiming at the problem of low precision and large cleaning error in multi-link similar data mining, a multi-link similar data mining cleaning method based on Bayesian algorithm is proposed in this paper. On the basis of extracting multi-link data, the data is preprocessed and the similarity between the data is calculated. The data with high similarity is input into the Bayesian network, and the data cleaning process is completed according to the maximum likelihood value of the data. Experimental results show that the mining accuracy of the proposed method for similar data can reach 98.51%, and the cleaning error is about 1.22%, indicating that the proposed method can more effectively complete the mining and cleaning of similar data in multi-links. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Directed graphs

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