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Generation of High-Quality Relevant Judgments through Document Similarity and Document Pooling for the Evaluation of information Retrieval Systems 14

Generation of High-Quality Relevant Judgments through Docume...

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14th International Conference on Software, Knowledge, information Management and Applications, SKIMA 2022

作者： Joseph, Minnu Helen Ravana, Sri Devi University of Malaya Faculty of Computer Science & Information Technology Department of Information Systems Malaysia Faculty of Computer Science Engineering & Technology Malaysia

ISBN: (纸本)9781665493345

The information Retrieval System Evaluation have carried out through Cranfield-paradigm in which the test collections provide the foundation of the evaluation process. The test collections consist of document corpus, topics, and a set of relevance judgements. The relevant judgements are the documents which retrieved from the test collections based on the topics. The precision of the evaluation process is based on the number of relevant documents in the relevant judgement list called qrels. This paper presents a study on how methodologies like pooling and document similarity helps to generate more relevant documents into the relevance judgments set in order to increase the accuracy of the evaluation process. The initial results have shown that combination of pooling with document similarity performs better compared to base clustering or classification. © 2022 IEEE.

关键词： information retrieval systems

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On Vertex-Edge-Degree Topological Descriptors for Certain Crystal Networks

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computer Systems Science & engineering 2022年第3期40卷 835-850页

作者： Sadia Husain Fouad A.Abolaban Ali Ahmad Muhammad Ahsan Asim Yasir Ahmad College of Computer Sciences and Information Technology Jazan UniversitySaudi Arabia King Abdulaziz University College of EngineeringNuclear Engineering DepartmentJeddah21589Saudi Arabia

Due to the combinatorial nature of graphs they are used easily in pure sciences and social *** dynamical arrangement of vertices and their associated edges make them flexible(like liquid)to attain the shape of any physical structure or phenomenon *** the field of ICT they are used to reflect distributed component and communication among *** chemistry is another interesting domain of applied mathematics that endeavors to display the structure of compounds that are formed in result of chemical *** area attracts the researchers due to its applications in theoretical and organic *** also inspires the mathematicians due to involvement of mathematical *** or irregular bonding ability of molecules and their formation of chemical compounds can be analyzed using atomic valences(vertex degrees).Pictorial representation of these compounds helps in identifying their properties by computing different graph invariants that is really considered as an application of graph *** paper reflects the work on topological indices such as ev-degree Zagreb index,the first ve-degree Zagrebindex,the first ve-degree Zagrebindex,the second ve-degree Zagreb index,ve-degree Randic index,the ev-degree Randic index,the ve-degree atom-bond connectivity index,the ve-degree geometric-arithmetic index,the ve-degree harmonic index and the ve-degree sum-connectivity index for crystal structural networks namely,bismuth tri-iodide and lead *** this article we have determine the exact values of ve-degree and ev-degree based topological descriptors for crystal networks.

关键词： ev-degree ve-degree topological indices crystal networks

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LSTM-XGBoost: An Ensemble Model for Blood Demand Distribution Forecasting – A Case Study in Zakho City, Kurdistan Region, Iraq

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Operations Research Forum 2025年第1期6卷 1-22页

作者： Zebari, Rizgar R. Zebari, Gheyath M. Al-zebari, Adel Mohammed, Marwan Aziz Department of Computer Science College of Science Knowledge University Erbil 44001 Iraq Department of Information Technology Technical College of Informatics Akre University for Applied Sciences Kurdistan Akre Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil 44001 Iraq

A safe and adequate blood supply is essential for healthcare systems to function effectively. Accurately forecasting blood demand plays a key role in efficient inventory management and resource allocation. Traditional forecasting methods, like moving averages and ARIMA models, often fall short due to the complexity of factors influencing blood demand. This study introduces an innovative hybrid ensemble model that combines Long Short-Term Memory (LSTM) networks with XGBoost, harnessing their combined strengths to enhance forecasting accuracy. By analyzing blood donation data from the Zakho Blood Bank (January 1, 2015—July 22, 2022), the model outperforms individual LSTM and XGBoost models, excelling in metrics such as Mean Square Error (MSE) and Mean Absolute Error (MAE). These findings underscore the potential of advanced machine learning techniques to improve healthcare supply chain management and ensure the timely availability of critical blood supplies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Blood banking Blood demand forecasting Healthcare supply chain LSTM network Machine learning XGBoost

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Capacity Evaluation of Network Coding for Wireless Systems in Heterogeneous Wireless Networks

Capacity Evaluation of Network Coding for Wireless Systems i...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Mandal, Sanjeev Kumar Fauidar, Pramod Kumar Jain, Aaditya Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Mechanical Engineering Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

Network coding is rising as a powerful approach that can grow the throughput of wireless networks. This approach has been the difficulty of numerous studies and has spread new possibilities for Wi-Fi networks. In this paper, we compare the capacity of community coding in a heterogeneous Wi-Fi system. The machine includes users with distinct requirements, radio configurations, and visitor patterns. The analysis is finished using Shannon's mathematical model and Monte Carlo simulations. The consequences show that the ability of community coding relies upon channel conditions, user load, and diversity. We additionally discuss the capacity for improving the potential performance of community coding in heterogeneous wireless structures. Sooner or later, we show that network coding can be applied without problems within modern wireless structures and conclude with the feasibility of using network coding to enhance wireless networks' overall performance. Network coding has been proposed as an effective device for improving the overall performance of Wi-Fi verbal exchange structures. Lately, its potential has been investigated for heterogeneous wireless networks. This text reviews current studies on the potential assessment of community coding for wireless structures in heterogeneous wireless networks. We talk about the theoretical and sensible fashions for figuring out the network coding capacity in distinct eventualities, which includes static networks, dynamic networks, and multi-hop eventualities. Then, we offer some every day demanding situations within the implementation of community coding across various wireless networks. ultimately, we gift a few promising destiny guidelines for comparing the capability of community coding in heterogeneous networks. The discussion allows us to benefit from insights into the capability assessment and the development of future wireless networks. © 2024 IEEE.

关键词： channel networks numerous patterns

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Reseed Skipping of Linear Congruential Generator for Multi-level Image Steganography Security of Financial Data

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SN computer Science 2023年第2期4卷 124页

作者： Danso, Juliana Mantebea Missah, Yaw Marfo Gyamfi, Enoch Opanin Dankwa, Stephen Kwabena, Sarpong Department of Computer Science Kwame Nkrumah University of Science and Technology (KNUST) Kumasi Ghana School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Cyber Security and Computer Engineering Technology School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Upper East Region Navrongo Ghana School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

This paper proposed a multi-level image steganography model that integrates with image and plaintext encryption schemes. The model starts by partitioning the cover-image pixel positions into two regions of respective purposes, one is reserved to hold the data, and through the implementation of the dynamic difference expansion principle, the other sloughs some parts to the reserved region in responsive to the size of the data to embed. Continuously reseeding and initializing the parameter values of a Linear Congruential Generator (LCG), generate a set of random pixel positions as members of the reserved region. Before data embedding, the region reserved to hold the data as well as the data bits to embed, are then encrypted using AES with respective keys. Encryption keys are randomly generated using a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG), the Fortuna algorithm. Multiplex security nature of using a randomization generator to reserve image region for encryption and encrypting plaintext data before performing steganography still ensures stability in computational complexities better than a logarithmic O(n log n). A visual inspection by evaluators who made 3000 choices to correctly differentiate stego images from its cover-images shows that only 47 choices were correct, 1884 choices were wrong, while 1069 choices were uncertain. An experiment shows an average Embedding Rates (ERs) of 0.00109 bpp and an average PSNR of 72. This concludes, as our integrated steganography and image encryption which implements the dynamic difference expansion principle, outputs encrypted stego mages that can deceive the naked human eyes into identifying them as rather cover-images, its embedding rate is faster (i.e. ER < 0.1), suitably good, and ensures better imperceptibility of cover-image alterations. When comparing these experimental results with existing methods, our proposed model shows significant competitiveness. We even show real-life scenarios on how this

关键词： AES encryption scheme Dynamic difference expansion principle Linear congruential generator Pseudo-random number generator Reserving room before encryption Steganography

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MSMVAN: Multi Step Multi Variate Deep Attention Network for Renewable Energy Forecast

MSMVAN: Multi Step Multi Variate Deep Attention Network for ...

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作者： Varshini, U. Shivani Sri Sree, R. Praneetha Perumal, Murukessan Srinivas, M. Subramanyam, R.B.V. The Department of Computer Science and Engineering National Institute of Technology Telangana Warangal506004 India The Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Kurnool Kurnool518007 India

The importance of renewable energy in our everyday lives cannot be overstated, specifically highlighting solar and wind energies as two crucial sustainable power sources. Nevertheless, it is crucial to acknowledge that the amount of energy derived from renewable sources is subjected to variability, being influenced by environmental conditions. To ensure efficient management of energy load demands in diverse applications and enhance the performance of storage grids, precise energy production forecasting is vital. However, existing models doesn’t recognise the important variables for forecasting and fails in the presence of noisy or missing data. To address these issues, we proposed a MultiStep Multi Variate Deep Attention Network (MSMVAN) model with masked attention and dilated Fourier convolutions. The proposed approach utilizes a sequential model to analyze non-linear relationships in time series data. Further, dilated fourier convolutions are incorporated to capture multistep spatial and cross correlations, while attention mechanisms are employed to identify significant variables. As a result, the model acquires improved vector representations, leading to enhanced energy forecasting power. We evaluated our model on three standard datasets and from the experimental results we conclude that our proposed model outperforms the state-of-the-art models in forecasting solar and wind energy. © 2023 IEEE.

关键词： Time series analysis

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Interior Scene Intelligent Generated for Smart Homes Based on GAN-ID Algorithms

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 1046-1055页

作者： Zhang, Fu-Quan Ma, Lin-Juan Zhou, Jun-Xian Wang, Run-Hong Zhang, Guang-Zhen College of Computer and Control Engineering Minjiang University Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing Information System Minjiang University Fuzhou China Fujian Province University Fuzhou China School of Computer Science and Technology Beijing Institute of Technology Beijing China Minjiang University Fuzhou China Information Media School of Tianjin Technician Institute of Mechanical & Electrical Technology Tianjin China

The purpose of this paper is to present a new GAN-ID algorithm to gener-ate interior scene intelligently based on Artificial Intelligence of Things (AIoT). Although great progress has been made when GAN algorithm applied in smart homes and smart cities, the issue of the trade-off between the accuracy and the efficiency in interior scene has been overlooked. A number of experiments were performed to check the performance and it is indicated that the results of the proposed GAN-ID has great advantages in model sample generation than other common methods when generating interior scene intelligently, which is similar to state-of-the-art. It is meaningful when it can be applied in smart cities and smart homes filed based on AIoT in the future. © 2024, J. Network Intell. All rights reserved.

关键词： Automation

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Combining Entropy Optimization and Sobel Operator for Medical Image Fusion

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computer Systems Science & engineering 2023年第1期44卷 535-544页

作者： Nguyen Tu Trung Tran Thi Ngan Tran Manh Tuan To Huu Nguyen Faculty of Computer Science and Engineering Thuyloi University175 Tay SonDong DaHanoi010000Vietnam University of Information and Communication Technology Thai Nguyen UniversityThai Nguyen240000Vietnam

Fusing medical images is a topic of interest in processing medical *** is achieved to through fusing information from multimodality images for the purpose of increasing the clinical diagnosis *** fusion aims to improve the image quality and preserve the specific *** methods of medical image fusion generally use knowledge in many differentfields such as clinical medicine,computer vision,digital imaging,machine learning,pattern recognition to fuse different medical *** are two main approaches in fusing image,including spatial domain approach and transform domain *** paper proposes a new algorithm to fusion multimodal *** algorithm is based on Entropy optimization and the Sobel *** transform is used to split the input images into components over the low and high frequency ***,two fusion rules are used for obtaining the fusing *** rule,based on the Sobel operator,is used for high frequency *** second rule,based on Entropy optimization by using Particle Swarm Optimization(PSO)algorithm,is used for low frequency *** algorithm is implemented on the images related to central nervous system *** experimental results of the paper show that the proposed algorithm is better than some recent methods in term of brightness level,the contrast,the entropy,the gradient and visual informationfidelity for fusion(VIFF),Feature Mutual information(FMI)indices.

关键词： Medical image fusion wavelet entropy optimization PSO Sobel operator

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Enhanced Text-to-Image Synthesis Using Generative Adversarial Networks and Skip-Thought Vectors for Improved Visual and Semantic Accuracy

Enhanced Text-to-Image Synthesis Using Generative Adversaria...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Monisha, D. Keerthana, D.R. Manoj Kumar, M.V. Prashanth, B.S. Sneha, H.R. Puneeth, B.H. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India

ISBN: (数字)9798350360660

ISBN: (纸本)9798350360660

Creating realistic images from textual descriptions is a major challenge in computer vision and natural language processing. Traditional methods often fail to accurately translate text into visual representations, resulting in images that lack detail and context. This study addresses this issue by combining Generative Adversarial Networks (GANs) with Skip-Thought *** proposed approach involves three key stages. First, textual descriptions are embedded using Skip-Thought Vectors, which utilize a Recurrent Neural Network (RNN) encoder with Gated Recurrent Unit (GRU) activation to capture semantic nuances. Next, low-resolution images are generated using GANs, providing a basic visual structure. Finally, these images are refined to high resolution to ensure they are detailed and contextually *** method was validated using the Caltech-UCSD Birds 200 (CUB-200) dataset, showing notable improvements in both visual quality and semantic accuracy over previous methods. This approach enables the generation of detailed and contextually relevant images from text, with potential applications in design, storytelling, e-commerce, and *** automating the generation of high-quality images from text, this research enhances user experience and opens new possibilities for creative and practical uses, marking a significant advancement in the integration of computer vision and natural language processing. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhancing Tumor Detection through ANFIS-Based Clustering in Biomedical Imaging 2

Enhancing Tumor Detection through ANFIS-Based Clustering in ...

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2nd International Conference on Computational Intelligence and Network Systems, CINS 2024

作者： Sri, Sweta P. Lakshay, Morwal Suguna, M. Department of Information Technology Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai603110 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India

ISBN: (纸本)9798331504106

In Biomedicine, computer software is applied to the medical image for content analysis by MRI scanning for the program. Preprocessing, removal of noise, processing images in order to detect objects and analysis of the images for tumor assessment in terms of shape and volume form part of the MRI based brain tumor detection process. The author of this paper presents a method employing ANFIS models with clustering in order to determine the number of clusters necessary for identifying brain and lymph node tumors. ANFIS improves classification as well as cancer diagnosis and therapy by automating selection and refinement of treated clusters. ©2024 IEEE.

关键词： Medical image processing

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