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Single Under-Water Image Enhancement Using the Modified Transmission Map and Background Light Estimation

International Conference on Machine Vision and Augmented Intelligence, MAI 2022

作者： Verma, Gunjan Kumar, Manoj Raikwar, Suresh Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura281406 India Department of Computer Engineering and Applications Thappar University Punjab Patiala147004 India

ISBN: (纸本)9789819901883

In under-water environment, sensors are used to explore marine resources, sea-bed, archaeology, and tracking. Due to that, vision sensor plays a vital role as it helps to obtain high-content information. The light attenuation and backscattering in under-water environment are dependent on the wavelength that leads to color distortion, low contrast, and blurry images. The under-water image formation model depends on mainly two factors: background light and transmission light. In this paper, a modified transmission light estimation method and a new attenuation coefficient (U-TMBL) are proposed that enhance the quality of under-water images. Also, novel background light estimation method is proposed. The performance of the proposed method is quantitatively and qualitatively analyzed. The quantitative comparison is based on NIQE, BRISQUE, and UIQM evaluation metrics. The quantitative and qualitative results demonstrate that the proposed U-TMBL overcomes the existing under-water image enhancement approaches. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Light transmission

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Role of ELMo Embedding in Detecting Fake News on Social Media 11

Role of ELMo Embedding in Detecting Fake News on Social Medi...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Garg, Sonal Sharma, Dilip Kumar GLA University Department of Computer Engineering and Applications India

ISBN: (纸本)9781665487320

The advancement of technology makes it difficult to distinguish between genuine and false information. Social media allow fake news to spread faster in comparison to conventional media. The propagation of such fake news creates a harmful effect on the public and society. It can also generate biased opinions and disturb the stability of the news ecosystem. To combat the rapid proliferation of fake news on online media, a holistic approach is required. In this paper, we used Elmo word embedding for counterfeit detection. For implementation, the Reuter dataset is used. In particular, This paper used different machine-learning models to evaluate their effectiveness. © 2022 IEEE.

关键词： Embeddings

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An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

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computer Modeling in engineering & Sciences 2024年第5期139卷 1521-1555页

作者： Abidhan Bardhan Raushan Kumar Singh Mohammed Alatiyyah Sulaiman Abdullah Alateyah Department of Civil Engineering National Institute of Technology PatnaPatnaIndia Department of Computer Engineering and Applications GLA UniversityMathuraIndia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Computer Science College of Science and ArtsQassim UniversityUnaizahSaudi Arabia

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented *** proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal *** EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was *** train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was *** on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive *** consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)*** outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

关键词： Metakaolin-contained cemented materials compressive strength extreme learning machine grey wolf optimizer swarm intelligence uncertainty analysis artificial intelligence

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Detection of Credit Card Fraud using Machine Learning Algorithms 11

Detection of Credit Card Fraud using Machine Learning Algori...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Singh, Akhilesh Kumar GLA University Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665487320

The use of online transactions in regular life has increased over the past ten years due to continuous development in technology and network connectivity. Online transactions are easy, simple, and user-friendly is the main reason for new users to constantly join the vast population benefitting them from the system. The main demerit of the online transaction system is a misuse of the system is defined as theft or one's credit card information which is used for self-profit without the permission of the cardholder. In this paper we have uses different ML algorithms such as regression, random forest classifier, and decision tree to control the heavily imbalanced dataset. Finally, in this research, we will calculate the accuracy, precision, recall, f1 score, and confusion matrix. © 2022 IEEE.

关键词： Decision trees

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A three-stage novel framework for efficient and automatic glaucoma classification from retinal fundus images

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Multimedia Tools and applications 2024年第37期83卷 85421-85481页

作者： Singh, Law Kumar Khanna, Munish Garg, Hitendra Singh, Rekha Iqbal, Md. Department of Computer Engineering and Applications GLA University Mathura India School of Computing Science and Engineering Galgotias University Greater Noida203201 India Department of Physics Uttar Pradesh Rajarshi Tandon Open University Uttar Pradesh Prayagraj India Department of Computer Science and Engineering Meerut Institute of Engineering and Technology Meerut India

glaucoma is one of the leading causes of visual impairment worldwide. If diagnosed too late, the disease can irreversibly cause severe damage to the optic nerve, resulting in permanent loss of central vision and blindness. Therefore, early diagnosis of the disease is critical. Recent advancements in machine learning techniques have greatly aided ophthalmologists in timely and efficient diagnosis through the use of automated systems. Training the machine learning models with the most informative features can significantly enhance their performance. However, selecting the most informative feature subset is a real challenge because there are 2n potential feature subsets for a dataset with n features, and the conventional feature selection techniques are also not very efficient. Thus, extracting relevant features from medical images and selecting the most informative is a challenging task. Additionally, a considerable field of study has evolved around the discovery and selection of highly influential features (characteristics) from a large number of features. Through the inclusion of the most informative features, this method has the potential to improve machine learning classifiers by enhancing their classification performance, reducing training and testing time, and lowering system diagnostic costs by incorporating the most informative features. This work aims in the same direction to propose a unique, novel, and highly efficient feature selection (FS) approach using the Whale Optimization Algorithm (WOA), the Grey Wolf Optimization Algorithm (GWO), and a hybridized version of these two metaheuristics. To the best of our knowledge, the use of these two algorithms and their amalgamated version for FS in human disease prediction, particularly glaucoma prediction, has been rare in the past. The objective is to create a highly influential subset of characteristics using this approach. The suggested FS strategy seeks to maximize classification accuracy while reducing the t

关键词： Image classification

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Fake News Classification via CNN 11

Fake News Classification via CNN

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Garg, Sonal Sharma, Dilip Kumar GLA University Department of Computer Engineering and Applications India

ISBN: (纸本)9781665487320

The use of digital media allows people to consume daily news, opinions and information covering a large variety of topics. The availability of social media allows users to easily share their own views and opinions, but this online platform is also responsible for the sudden increase rate of fake news. Fake news may create serious damage to society, including creating anger, hype and depression and, in some cases, lead to death also. A different approach has been proposed in the past to combat phonic news. This paper used CNN deep-learning classifiers for misleading news detection. The proposed approach uses the Reuter dataset to perform experiments. The proposed model obtained the highest of 93.64% accuracy, which is higher in comparison to existing methods. © 2022 IEEE.

关键词： Digital storage

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Conversational Interfaces for Health Consultation 11

Conversational Interfaces for Health Consultation

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Singh, Akhilesh Kumar GLA University Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665487320

What we do when we get ill or sick, standard practice is we go to a doctor or sometimes we search on the internet and take medicines without consulting any doctor. What if you want to consult a doctor but there is no clinic nearby you? So considering all the real-time problems, Here is the solution -Healthcare chat-bot services using flask. The adduce idea is to create a model that can understand your problem or diagnose the disease and recommend the medication for free or minimal charges and not only recommend but also keep a record/track of your health on daily basis and if needed book your appointments with the doctors immediately. You can relate it with a medication consultant /book in your hand. Not only in general situations but also in pandemic situations, such technology will reach its peak usage, where it will be not possible to visit a doctor frequently. © 2022 IEEE.

关键词： Bottles

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AI-Driven Solutions for Predictive Maintenance in Smart Transportation Systems

AI-Driven Solutions for Predictive Maintenance in Smart Tran...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Hundekari, Sheela Mittal, Tanusha Dutt, Amit Bhavana, Munugapati Kumar, Rakesh Nijhawan, Ginni School of Computer Applications Pimpri Chinchwad University Pune India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798331512965

Predictive maintenance: A game changer in infrastructure and vehicle management, and able to cut maintenance expenses and downtime of smart transportation systems. This study explores ways in which smart transportation systems might use AI in order to plan maintenance schedules and forecast equipment breakdowns. Predictive algorithms, that can detect potential problems before raising to expensive repair or system breakdown, use real time sensor data from infrastructure and vehicles. Decision trees and neural networks are two of these models. By allowing AI driven solutions, transportation systems may transition from a reactive to a proactive maintenance practice which may support efficiency, safety, and reliability. Unsupervised learning methods are used to do anomaly detection for real time operations, and we examine the patterns in previous maintenance data with supervised learning approaches. The use of AI for predictive maintenance is also being studied for use in smart cities, where transportation networks are becoming more interconnected by IoT. The paper also considers challenges on data integration as well as the importance of scaling the algorithms and guaranteeing cybersecurity in these systems. Based on our research, smart transportation systems could benefit from AI to increase operational efficiency, reduce the cost of material and labor to maintain and repair equipment systems, and decrease the rate of disruptions. © 2025 IEEE.

关键词： Predictive maintenance

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e-Health: A Modern Approach to Healthcare Delivery 14

e-Health: A Modern Approach to Healthcare Delivery

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Maithani, Ankit Pradhan, Rahul Dit University Dehradun Department of Computer Science & Engineering Dehradun India Gla University Mathura Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798350335095

e-Health had various attributes other than doctors, patients and medicine. Some of the components are health records, prescriptions, decision support system, online consultation videos, transcripts and telemedicine. Information technology is now integral and one of the core components of eHealth. Prescriptions generated from computer and Artificial intelligent expert systems, include diagnosis, basis of diagnosis, medicine, their side-effects and drug combination and their dosages. © 2023 IEEE.

关键词： Diagnosis

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Breast Cancer Prediction Using Advance Machine Learning Algorithms 8

Breast Cancer Prediction Using Advance Machine Learning Algo...

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8th International Conference on Advanced Computing and Communication Systems, ICACCS 2022

作者： Jain, Vinod Agrawal, Mayank Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665408165

Breast cancer is very common type of cancer now a day. It is observed in many of the women and responsible for many deaths in recent days. In this work the power of machine learning classifiers is applied in prediction of breast cancer. Three machine learning classifiers LR, KNN and Decision Tree are applied on a standard data set and their prediction accuracy is calculated. From the experiment it is concluded that the LR algorithms is best for breast cancer prediction. © 2022 IEEE.

关键词： Machine learning

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