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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Tariq, Maria Al-Khasawneh, Mahmoud Ahmad Irtaza, Ghulam Fiaz, Muhammad Safi, Waseem Asif, Rabia National College of Business Administration and Economics Department of Computer Science Lahore Pakistan Skyline University College School of Information Technology Sharjah1797 United Arab Emirates University of Education Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Higher Institute for Applied Sciences and Technology-HIAST Damascus Syria Applied Science Research Center Applied Science Private University Amman11937 Jordan

ISBN: (纸本)9798350335644

Pneumonia causes a high rate of disease and death in infants. This condition impacts the lungs' tiny air sacs and necessitates quick Identification and therapy. Manually, it may take more time to diagnose this disease in children. Machine learning techniques are gaining traction in medical Diagnosis because they can classify data accurately. Using accurate feature selection algorithms to minimize the dimension of datasets is critical to the accuracy of classification algorithms. SVM is a computer algorithm applied to many biological applications with great success. The automated classification of microarray gene expression profiles is a widespread use of support vector machines (SVMs) in biomedicine. This algorithm is used to diagnose pneumonia in children more efficiently and quickly. The proposed intelligent system to diagnose pneumonia in children helps patients diagnose and treat this disease in time. This method would enable specialists to achieve quicker and more precise results, allowing them to provide the best possible treatment. The proposed method for detecting pneumonia in children has a 98.40 percent accuracy rate. © 2023 IEEE.

关键词： Support vector machines

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An Energy-Efficient Optimal Routing Model for Mobile Ad Hoc Network Using Mine Blast Algorithm

An Energy-Efficient Optimal Routing Model for Mobile Ad Hoc ...

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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Goswami, Subhrananda Mondal, Sukumar Johardar, Subhankar Das, Chandan Bikash Research Centre of Natural and Applied Sciences Raja Narendra Lal Khan Women's College Department of Computer Science India Raja Narendra Lal Khan Women's College Department of Mathematics India Haldia Institute of Technology Department of Computer Science India Tamralipta Mahavidyalaya Department of Mathematics India

ISBN: (纸本)9798350339369

The collection of wireless mobile nodes dynamically in a temporary network is termed a Mobile Ad Hoc Network (MANET). Here, energy consumption is an important complication in MANET because the mobile node doesn't offer a permanent power supply to the network, and they highly depend on batteries. The lifetime of the batteries is exhausted quickly as the nodes are traveled and change their positions frequently in the MANET network. The key factor for MANET is efficient energy to improve the network lifetime. Moreover, the efficacies of MANET are increased by different criteria like residual energy and link stability, which are always required to optimize. The major goal of multi-objective optimization issues is to reduce the energy consumption rate and also enhance the lifetime of the network. Based on the battery-dependent nodes, the efficacy of MANET is reduced. So, it is more need to select multiple optimal node disjoint paths among the destination and source to transfer the data. Further, multi-objective functions are utilized to choose the optimal path that offers the optimum outcome regarding traffic load, hop and energy consumption. So, efficient routing protocols for energy efficiency improvement in MANET are developed by utilizing the Mine Blast Algorithm (MBA). All the sensor nodes of the MANET are taken for experimentation purposes. The shortest paths in the MANET model are optimized by implementing the MBA. The parameters optimized by MBA are link cost, path loss, consumption of energy, an energy that is still available in the battery, throughput, packet energy cost, end-to-delay, routing overhead ratio, and packet delivery ratio to evaluate the overall efficiency of the network. Parametric studies of the proposed MBA are done. The developed energy-efficient MBA-MANET systems are compared with various existing techniques to verify the improved energy efficiency of the developed MBA-MANET model. © 2023 IEEE.

关键词： Sensor nodes

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Finding discriminative filters for specific degradations in blind super-resolution 21

Finding discriminative filters for specific degradations in ...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Liangbin Xie Xintao Wang Chao Dong Zhongang Qi Ying Shan Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences and ARC Lab Tencent PCG ARC Lab Tencent PCG Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences and Shanghai AI Laboratory Shanghai China

ISBN: (纸本)9781713845393

Recent blind super-resolution (SR) methods typically consist of two branches, one for degradation prediction and the other for conditional restoration. However, our experiments show that a one-branch network can achieve comparable performance to the two-branch scheme. Then we wonder: how can one-branch networks automatically learn to distinguish degradations? To find the answer, we propose a new diagnostic tool – Filter Attribution method based on Integral Gradient (FAIG). Unlike previous integral gradient methods, our FAIG aims at finding the most discriminative filters instead of input pixels/features for degradation removal in blind SR networks. With the discovered filters, we further develop a simple yet effective method to predict the degradation of an input image. Based on FAIG, we show that, in one-branch blind SR networks, 1) we are able to find a very small number of (1%) discriminative filters for each specific degradation; 2) The weights, locations and connections of the discovered filters are all important to determine the specific network function. 3) The task of degradation prediction can be implicitly realized by these discriminative filters without explicit supervised learning. Our findings can not only help us better understand network behaviors inside one-branch blind SR networks, but also provide guidance on designing more efficient architectures and diagnosing networks for blind SR.

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A lightweight identity based generalized signcryption scheme for secure communication in standard model

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International Journal of Information Technology (Singapore) 2024年第2期16卷 1221-1229页

作者： Singh, Tej Kumar, Mukesh Gelmecha, Demissie Jabir Thakur, Ganesh Kumar Tyagi, Vikas Sori, Worku Jifara Department of Applied Sciences ABES Engineering College UP Ghaziabad India Institute of Applied Sciences & Humanities GLA University UP Mathura India Department of Electronics and Communication Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia Department of CSE IMS Engineering College UP Ghaziabad India Department of Computer Science School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia

The implementation of secure communication in the Industrial Internet of Things (IIoT) has made and it is a significant security concern. Identity-based generalized signcryption scheme is one among the cryptographic tools available that offers a secure way out to meet the security needs of IIoT. Our proposal is a lightweight scheme, an efficient identity-based generalized signcryption that works as an encryption signature, or signcryption scheme using only one algorithm in a standard model. This scheme is appropriate for storage-limited environments like wireless sensor networks and smart cards due to its lightweight nature. This means that it can be implemented in a range of IIoT applications where resources are limited, without compromising on security or efficiency. © The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Bilinear Pairing Generalized signcryption Identity-based cryptography Signcryption

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Effect of E-Learning on Public Health and Environment During COVID-19 Lockdown

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Big Data Mining and Analytics 2021年第2期4卷 104-115页

作者： Avani Agarwal Sahil Sharma Vijay Kumar Manjit Kaur Department of Computer Science Thapar Institute of Engineering and TechnologyPatiala 147001India Department of Computer Science and Engineering National Institute of TechnologyHamirpurHimachal Pradesh 177005India Department of Computer Science Engineering School of Engineering and Applied SciencesBennett UniversityGreater Noida 201310India

E-learning is the most promising venture in the entire world. During the COVID-19 lockdown, e-learning is successfully providing potential information to the students and researchers. In developing nations like India, with limited resources, e-learning tools and platforms provide a chance to make education available to middle and low income households. This paper gives insights about three different online services, namely Google Classroom,Zoom, and Microsoft Teams being used by three different educational institutions. We aim to analyze the efficiency and acceptability of e-learning tools among Indian students during the COVID-19 lockdown. The paper also aims to evaluate the impact of e-learning on the environment and public health during COVID-19 lockdown. It is found that e-learning has potential to reduce carbon emissions, which has beneficial impact on the environment. However, the mental health is impacted as e-learning may lead to self-isolation and reduction in academic achievements that may lead to anxiety and mental depression. Due to usage of electronic devices for learning, the eyes and neck muscles may be put in strain, having deleterious effects on physical health.

关键词： e-learning environment health COVID-19

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Comparison of Machine Learning Algorithm for Stock Price Prediction Using Sentiment Analysis

Comparison of Machine Learning Algorithm for Stock Price Pre...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： Anuradha Yenkikar C. Narendra Babu Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bangalore Karnataka India Pune Institute of Computer Technology Pune India

Forecasting companies' stock market prices are one the interesting topics for analysts and researchers. Although a company's stock price can be unpredictable, long-term forecasts can help but it is dependent on many factors such as the company's business model, change in leadership, and investors' mood. It has been found to be insufficient to predict stock values just on the basis of historical data or textual information. Previous research in sentiment analysis have shown a strong correlation between the movement of stock prices and the publication of news stories. At different levels, a number of sentiment analysis research have been attempted utilizing methods. In this paper, we made a comparison of various Machine Learning methods on five datasets of financial news related to the company and domains in which the company. Encouraging results are obtained using 13 models i.e., Linear Regression, Ridge Regression, Lasso Regression, Random Forest, Naive Bayes, Logistic Regression, LSTM, ARIMA, Logistic Regression, Support Vector Machines, Decision Tree, Boosted Tree, and ensemble method which depict polarity of news articles being positive or negative and the accuracies are 93.90%, 92.31 %, 92.27%, 85.44%, 84.65%, 84.65%, 94.73%, 90.13%, 82%, 83%, 72%, 70%, 95.11 % respectively.

关键词： Support vector machines Sentiment analysis Neural networks Companies Predictive models Prediction algorithms Stock markets

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Policy gradient methods in the presence of symmetries and state abstractions

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 3434-3490页

作者： Prakash Panangaden Sahand Rezaei-Shoshtari Rosie Zhao David Meger Doina Precup School of Computer Science McGill University and Mila - Quebec AI Institute Montreal QC Canada School of Engineering and Applied Sciences Harvard University Cambridge MA School of Computer Science McGill University and Mila - Quebec AI Institute and DeepMind Montreal QC Canada

Reinforcement learning (RL) on high-dimensional and complex problems relies on abstraction for improved efficiency and generalization. In this paper, we study abstraction in the continuous-control setting, and extend the definition of Markov decision process (MDP) homomorphisms to the setting of continuous state and action spaces. We derive a policy gradient theorem on the abstract MDP for both stochastic and deterministic policies. Our policy gradient results allow for leveraging approximate symmetries of the environment for policy optimization. Based on these theorems, we propose a family of actor-critic algorithms that are able to learn the policy and the MDP homomorphism map simultaneously, using the lax bisimulation metric. Finally, we introduce a series of environments with continuous symmetries to further demonstrate the ability of our algorithm for action abstraction in the presence of such symmetries. We demonstrate the effectiveness of our method on our environments, as well as on challenging visual control tasks from the DeepMind Control Suite. Our method's ability to utilize MDP homomorphisms for representation learning leads to improved performance, and the visualizations of the latent space clearly demonstrate the structure of the learned abstraction.

关键词： reinforcement learning policy optimization abstraction symmetry representation learning

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Human-centered User Interfaces for Automated Driving – (Un-)exploited Potentials 17

Human-centered User Interfaces for Automated Driving – (Un-...

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17th International Conference on Wirtschaftsinformatik, WI 2022

作者： Driesen-Micklitz, Tim Fellmann, Michael Röcker, Carsten University of Rostock Institute of Computer Science Rostock Germany Fraunhofer IOSB-INA Lemgo Germany TH OWL University of Applied Sciences Lemgo Germany

Designing user interfaces for (highly) automated driving is a complex task since users vary considerably regarding their needs and preferences. Therefore, a one-size-fits-all approach will not be sufficient for designing these interfaces. Thus, in this paper we aim to identify unexploited potentials in this area. We do so by performing a systematic literature review. Our contributions are 1) a systematization of human-centered user interface design for automated driving in four key aspects, 2) the research intensity per aspect, 3) the unexploited potential within each aspect and 4) the potentials of the relations between them. Concretely, current research lacks frameworks supporting the customization of the named interfaces based on user characteristics. Among others, personalization of displayed information shows unexploited potentials for acceptance and usability. Thus, we recommend future research to focus on human-centricity accounting for individual needs instead of the interface itself. © 2022 17th International Conference on Wirtschaftsinformatik, WI 2022. All rights reserved.

关键词： User interfaces

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Rock-Paper-Scissors Image Classification Using Transfer Learning 2

Rock-Paper-Scissors Image Classification Using Transfer Lear...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ahmed, Fahad Khan, Wasim Ahmad Iqbal, Muhammad Ahmad Abazeed, Ashraf Riad Alrababah, Hamza Khan, Muhammad Farhan National College of Business Administration & Economics Department of Computer Science Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan Department of Computer Science Islamabad Pakistan Abu Dhabi Vocational Education and Training Institute ADVETI United Arab Emirates University City Sharjah School of Information Technology Skyline University College Sharjah1797 United Arab Emirates University of Health Sciences Department of Forensic Sciences Lahore Pakistan

ISBN: (纸本)9798350335644

Games are a genre of deliberate mental or physical activities which can be done alone or in groups for the purpose of enjoyment and recreation. Every game has its unique collection of participants, movements, and tactics, in addition to its own set of principles and guidelines. Rock-Paper-Scissors is probably the most well-known game for determining a winner in the world. The rules are pretty straightforward. Rock, paper, or scissors are displayed by each player simultaneously. The rock defeats the scissors but is defeated by the paper. Scissors defeat paper but lose to rock and paper defeats rock but loses to scissors. Using hand images, this study proposed identifying images of hands playing rock-paper-scissors. Transfer learning is used in the proposed model to recognize and classify images of rock-paper-scissors. The dataset for this study was acquired from the Kaggle. There are 2188 images in the dataset, including 712 rock images, 726 paper images, and 750 scissor images. The dataset is divided into two parts: 80% is utilized for training the model, and 20% is utilized for model validation. The RPSICTL model evaluation results good validation accuracy of 99.54%. © 2023 IEEE.

关键词： Rocks

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Riesz-Laplace Wavelet Transform and PCNN Based Image Fusion

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Machine Graphics and vision 2023年第1期32卷 73-84页

作者： Sun, Shuifa Tang, Yongheng Mei, Zhoujunshen Yang, Min Tang, Tinglong Wu, Yirong Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University YiChang China College of Computer and Information Technology China Three Gorges University YiChang China College of Economics and Management China Three Gorges University YiChang China Institute of Advanced Studies in Humanities and Social Sciences Beijing Normal University Zhuhai China

Important information perceived by human vision comes from the low-level features of the image, which can be extracted by the Riesz transform. In this study, we propose a Riesz transform based approach to image fusion. The image to be fused is first decomposed using the Riesz transform. Then the image sequence obtained in the Riesz transform domain is subjected to the Laplacian wavelet transform based on the fractional Laplacian operators and the multi-harmonic splines. After Laplacian wavelet transform, the image representations have directional and multi-resolution characteristics. Finally, image fusion is performed, leveraging Riesz-Laplace wavelet analysis and the global coupling characteristics of pulse coupled neural network (PCNN). The proposed approach has been tested in several application scenarios, such as multi-focus imaging, medical imaging, remote sensing full-color imaging, and multispectral imaging. Compared with conventional methods, the proposed approach demonstrates superior performance on visual effects, contrast, clarity, and the overall efficiency. © 2023 Faculty of applied Informatics and Mathematics - WZIM, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： Image fusion

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