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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Singh, Veenu Kumar, Vijay Singh, V.B. Amity University Amity Institute of Information Technology Uttar Pradesh Noida India Amity University Aias Department of Applied Mathematics Uttar Pradesh Noida India Jawaharlal Nehru University School of Computer and System Sciences New Delhi India

ISBN: (纸本)9789380544519

To thrive in the competitive market, organizations are opting to release their products early, followed by an ongoing testing process to eliminate any remaining bugs through the delivery of patches. Software patches refer to small programs introduced to the users to rectify faults, introduce new features, or enhance existing functionality. After going through the literature, it is observed that researchers have worked on testing-effort based SRGM, but none have addressed the aspect of reliability and cost in the context of determining the optimal time for software release and optimal time to stop software testing while patching is considered. To bridge this gap, we have introduced a testing effort based SRGM implementing patching, with the objective of making the software cost-effective and reliable. The numerical illustration has been done using Statistical Package for Social sciences. and Genetic Algorithm (GA). © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Generalized Modified Weibull (GMW) Genetic Algorithm (GA) Reliability Software Reliability Growth Model (SRGM) Statistical Package for Social sciences (SPSS) Testing Cost

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Convergence analysis of a Legendre spectral collocation method for nonlinear Fredholm integral equations in multidimensions

Convergence analysis of a Legendre spectral collocation meth...

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作者： Zaky, Mahmoud A. Hendy, Ahmed S. Department of Applied Mathematics National Research Centre Cairo Egypt Department of Mathematics Faculty of Science Benha University Benha Egypt Department of Computational Mathematics and Computer Science Institute of Natural Sciences and Mathematics Ural Federal University Yekaterinburg Russia

It is a very challenging task to solve a nonlinear integral equation in multidimensions. The main purpose of this paper is to develop and analyze a spectral collocation method for a class of nonlinear Fredholm integral equations of the second kind in multidimensions. The proposed spectral collocation method is based on a multivariate Legendre approximation in the frequency space. It is shown by both theory and numerics that the proposed spectral collocation scheme not only significantly reduces the number of degrees of freedom but also produces very accurate approximations. © 2020 John Wiley & Sons, Ltd.

关键词： Fredholm integral equations

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Enhancing Microgrid Stability: Optimal BESS Sizing via Modified PSO for Frequency Control and Cost Efficiency 7

Enhancing Microgrid Stability: Optimal BESS Sizing via Modif...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Singh, S. Vikram Revathi, V. Babu, B Sankara Bhavana, Munugapati Singh, Navdeep Nagamani, G. Amity University Amity School of Engineeringh and Technology Uttar Pradesgh Greater Noida India New Horizon College of Engineering Department of Applied Sciences Bangalore India Griet Department of Computer Science and Engineering Bachupally Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

When constructing a Battery Energy Storage System (BESS) to be incorporated in a microgrid, the main objective is to minimise the risk of microgrid stability and system failure. An improper or careless sizing of the BESS may lead to system losses, excessive capacity, and high installation costs. Consequently, this study suggest a novel technique for managing the frequency of the independent microgrid using an improved particle swarm optimisation (PSO), which aims to find the optimum size of the BESS while minimising its total cost. Our research focuses on finding the most cost-effective way to deploy BESS in microgrids for a period of 15 years. We will also recommend the optimal measurements of BESS to minimise microgrid volatility and system failure in the case of utility grid outage. We will use the PSO approach for frequency regulation. The next step is to examine a standard microgrid and evaluate BESS in comparison to other existing storage systems in terms of their economic performance. By comparing the optimum size of BESS- modified PSO method to both the conventional size of BESS and the ideal size of BESS-based empirical approach, the findings show that the latter may improve the dynamic system effectiveness. In a typical microgrid, the polysulfide-chromium BESS is predicted to be cheaper throughout the 15 years of deployment compared to the chrome redox BESS, when evaluating the financial health of the two storage techniques. Findings indicate that microgrid planning flexibility, grid security, and power system stability may all be greatly enhanced using the suggested PSO method-based frequency management. Simultaneously, it is able to meet the needs for frequency control economically. © 2024 IEEE.

关键词： Cost effectiveness

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Label Few Classify Many: N-Shot GAN for Abnormality Screening Using Chest X-Rays

Label Few Classify Many: N-Shot GAN for Abnormality Screeni...

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6th International Conference on Computational Intelligence in Pattern Recognition, CIPR 2024

作者： Pandey, Priyam Singh, Satish Kumar Santosh, K.C. Applied Artificial Intelligence Research Lab Department of Computer Science University of South Dakota VermillionSD57069 United States Computer Vision and Biometrics Lab Indian Institute of Information Technology Allahabad Prayagraj211015 India

ISBN: (纸本)9789819780921

Typical machine learning models typically demand a substantial volume of data for effective training, ensuring optimal performance during testing. However, these models often fail to specify the extent of data required—how big data is big enough to begin with? The "label few, classify many" paradigm is interesting in such a scenario where data is limited, and this holds true for healthcare informatics like others. To address this challenge, we explore the application of n-shot GANs in generating synthetic images, particularly in the context of chest X-rays for detecting COVID-19. Starting with one-shot GAN, our findings reveal that training on just 160 healthy chest X-rays achieves outstanding results, with maximum AUCs of 97.2 and 97.4 from ROC and PR curves on a test dataset comprising approximately 3600 chest X-rays. These results demonstrate comparable performance with state-of-the-art approaches in the field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： COVID-19

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Crow Search Optimization to Identify Adversary Nodes in Wireless Networks 2

Crow Search Optimization to Identify Adversary Nodes in Wire...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Rajaram, Gnanajeyaraman Thangaraj, T. Stephen Packialatha, A. Deepa, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Applied Machine Learning Tamil Nadu Chennai India Paavai Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

Adversary node detection systems have become a vital component due to the malware attacks. These systems are very important for network security due to internet's widespread development and increased accessibility to global data systems. This paper presents a Crow Search Optimization Algorithm (CSOA) to detect the adversary node in a wireless network. The Rivest, Shamir, Adleman (RSA) algorithm transmits secure data at first. It uses the public key to encrypt the data and the private key to decrypt the data and the adversary nodes can't access or modify the real data. One of the significant problems is when an unknown attack occurs on the network due to the high volume of data while the detection's accuracy and false alarm rate decline. The proposed system aims to improve accuracy by identifying the adversary nodes using CSOA algorithm. From the results, it is observed that the CSOA mechanism reduces the loss ratio and improves throughput of the network. © 2023 IEEE.

关键词： Optimization

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Corrections to “Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review”

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IEEE Access 2025年 13卷 50556-50558页

作者： Oriane Pierrès Markus Christen Felix M. Schmitt-Koopmann Alireza Darvishy Institute of Computer Science Zurich University of Applied Sciences School of Engineering Winterthur Switzerland Institute of Biomedical Ethics and History of Medicine (IBME) University of Zurich Zurich Switzerland

Presents corrections to the paper, (Corrections to “Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review”).

关键词： Reviews Artificial intelligence

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer Science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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Blockchain-Assisted Secure Fine-Grained Searchable Encryption for a Cloud-Based Healthcare Cyber-Physical System

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IEEE/CAA Journal of Automatica Sinica 2021年第12期8卷 1877-1890页

作者： Mamta Brij B.Gupta Kuan-Ching Li Victor C.M.Leun Kostas E.Psannis Shingo Yamaguchi IEEE the Department of Computer Engineering National Institute of Technology the Asia University the Macquarie University the Department of Computer Science and Information Engineering Providence University the Computer Science and Software Engineering Shenzhen University the University of British Columbia the Department of Applied Informatics University of Macedonia the Graduate School of Sciences and Technology for Innovation Yamaguchi University

The concept of sharing of personal health data over cloud storage in a healthcare-cyber physical system has become popular in recent times as it improves access *** privacy of health data can only be preserved by keeping it in an encrypted form,but it affects usability and flexibility in terms of effective ***-based searchable encryption(ABSE)has proven its worth by providing fine-grained searching capabilities in the shared cloud ***,it is not practical to apply this scheme to the devices with limited resources and storage capacity because a typical ABSE involves serious *** a healthcare cloud-based cyber-physical system(CCPS),the data is often collected by resource-constraint devices;therefore,here also,we cannot directly apply ABSE *** the proposed work,the inherent computational cost of the ABSE scheme is managed by executing the computationally intensive tasks of a typical ABSE scheme on the blockchain ***,it makes the proposed scheme suitable for online storage and retrieval of personal health data in a typical *** the assistance of blockchain technology,the proposed scheme offers two main ***,it is free from a trusted authority,which makes it genuinely decentralized and free from a single point of ***,it is computationally efficient because the computational load is now distributed among the consensus nodes in the blockchain ***,the task of initializing the system,which is considered the most computationally intensive,and the task of partial search token generation,which is considered as the most frequent operation,is now the responsibility of the consensus *** eliminates the need of the trusted authority and reduces the burden of data users,***,in comparison to existing decentralized fine-grained searchable encryption schemes,the proposed scheme has achieved a significant reduction in storage and computational cost for the secret

关键词： Cloud-based cyber-physical systems(CCPS) data encryption healthcare information search and retrieval keyword search public-key cryptosystems searchable encryption

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Automating Prediction of Roller Mills Performance in a Concentrator Using a Predictive Model by an Artificial Neural Network

Automating Prediction of Roller Mills Performance in a Conce...

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2023 International Russian Automation Conference, RusAutoCon 2023

作者： Ivashchuk, Orest D. Igrunova, Svetlana V. Nesterova, Elena V. Belgorod State National Research University Institute of Engineering Technology and Natural Sciences Dept of Applied Mathematics and Computer Modeling Belgorod Russia Belgorod State National Research University Institute of Engineering Technology and Natural Sciences Dept of Information and Robotic Systems Belgorod Russia

ISBN: (纸本)9798350345551

In this article, the authors propose to consider a multicomponent approach to evaluating the efficiency of roller mills in an enrichment plant, based on the use of a set of pre-built various mathematical models. The multicomponent approach makes it possible to predict the volume of finished concentrate output reliably during the period of the factory's transition to the use of ore with changed characteristics. Automation of forecasting will make it possible to analyze the effect of the physical and chemical characteristics of the feedstock adequately on the productivity of the main mills of the processing plant and plan the volumes of the concentrate. During the study, a database was created, the array size of which was 432x28: 432 is the number of daily data, and 28 is the total number of factors. Clustering methods were implemented by the software of the MATLAB R2021a application package, which made it possible to divide the generated database into clusters. As a result, the homogeneity of the data array connected by the same type of links was proved and, accordingly, the confirmation of the validity of combining the arrays of sections No. 1, 2 and 3 in order to form a single database for further processing and building a predictive model. Using the parallel use of the Statistica and Matlab packages, an optimal regression model was obtained. Artificial neural networks were formed with a different number of hidden layers. © 2023 IEEE.

关键词： Database systems

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SwinIA: Self-Supervised Blind-Spot Image Denoising Without Convolutions

SwinIA: Self-Supervised Blind-Spot Image Denoising Without C...

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IEEE Workshop on Applications of computer vision (WACV)

作者： Mikhail Papkov Pavel Chizhov Leopold Parts Institute of Computer Science University of Tartu Estonia CAIRO Technical University of Applied Sciences Würzburg-Schweinfurt Germany

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Self-supervised image denoising implies restoring the signal from a noisy image without access to the ground truth. State-of-the-art solutions for this task rely on predicting masked pixels with a fully-convolutional neural network. This most often requires multiple forward passes, information about the noise model, or intricate regularization functions. In this paper, we propose a Swin Transformer-based Image Autoencoder (SwinIA), the first fully-transformer architecture for self-supervised denoising. The flexibility of the attention mechanism helps to fulfill the blind-spot property that convolutional counterparts normally approximate. SwinIA can be trained end-to-end with a simple mean squared error loss without masking and does not require any prior knowledge about clean data or noise distribution. Simple to use, SwinIA establishes the state of the art on several common benchmarks.

关键词： Convolution Noise Noise reduction computer architecture Benchmark testing Transformers Robustness Noise measurement Tuning Image denoising

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