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Bankruptcy Prediction using Emperor Penguin Optimizer with Deep Learning Model on Qualitative Dataset

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Bankruptcy Prediction using Emperor Penguin Optimizer with D...

International Conference on Smart Systems and Inventive technology (ICSSIT)

作者： Vijay Abrol Priyanka Singh R Subhashini A.Sasi Kumar Kamlesh Singh B. Raja Mannar Department of Computer Science Uttam Group of Institutions Agra Uttar Pradesh India Department of Technical Education Sgsj Government Polytechnic Bulandshahr Uttar Pradesh India Department of Information Technology Sona College of Technology Salem Tamil Nadu India Department of Cloud Technology & Data Science (Mentor-IT – inurture Education Solutions Pvt LtdBangalore) Institute of Engineering & Technology Srinivas UniversitySrinivas NagarMukka SurathkalMangalore Dakshina Kannada District Karnataka State India Department of Computer Science and Engineering Graphic Era Hill UniversityDehradun Campus Dehradun India Department of Commerce Sri Venkateswara University Tirupati Andhra Pradesh India

Bankruptcy prediction and credit risk study are amongstthe most important problems in the domain of financial and accounting decision making. Evolving an effectual Classification Rule Induction (CRD) structure for bankruptcy prediction and credit risk examination at suitable time is vital to avoid the business communities being bankrupt. Classical statistical approaches and Artificial Intelligence (AD) play an important role in bankruptcy prediction and credit risks. Most previous research works can be executed on quantitative approaches but some studies are presented on qualitative approaches for improvising the efficiency of bankruptcy prediction techniques. This study focuses on the development of Bankruptcy Prediction using Emperor Penguin optimizer with Deep Learning (BP-EPODL) model on Qualitative Dataset. The projected BP-EPODL technique identifies the existence of bankruptcy or not. Here, the BP-EPODL technique exploits EPO algorithm for the choice of optimum features. For bankruptcy prediction, the BP-EPODL technique applies Long Short Term Memory (LS TM) model. At last, Adam optimizer is applied as a hyperparameter tuning technique for the IS TM model. The experimental validation of the BP-EPODL technique is tested on benchmark qualitative dataset. The obtained values inferred that the BP-EPODLtechnique has shown effectual results over other models.

关键词： Deep learning Decision making Predictive models Benchmark testing Prediction algorithms Artificial intelligence Bankruptcy

Fitness Dependent Optimizer for IoT Healthcare using Adapted Parameters: A Case Study Implementation

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arXiv 2022年

作者： Aladdin, Aso M. Abdullah, Jaza M. Salih, Kazhan Othman Mohammed Rashid, Tarik A. Sagban, Rafid Alsaddon, Abeer Bacanin, Nebojsa Chhabra, Amit Vimal, S. Banerjee, Indradip Department of Applied Computer College of Medical and Applied Sciences Charmo University KRG Sulaymaniyah Iraq Department of Information Systems Engineering Erbil Technical Engineering Colleg Erbil Polytechnic University KRG Erbil Iraq Information Technology College of Commerce University of Sulaimani KRG Sulaymaniyah Iraq Project Management Department College of Commerce University of Sulaimani KRG Sulaimani46001 Iraq Computer Science and Engineering Department University of Kurdistan Hewler KRG Erbil Iraq Department of Computer Technology Engineering Technical Engineering College Al-Ayen University Thi-Qar 64001 Iraq Information Technology College University of Babylon Hilla Iraq Sydney Australia Singidunum University Danijelova 32 Belgrade11000 Serbia Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Dept of CSE Ramco Institute of Technology Tamilnadu Rajapalayam India Department of Computer Science University Institute of Technology The University of Burdwan Burdwan India

This discusses a case study on Fitness Dependent Optimizer or so-called FDO and adapting its parameters to the Internet of Things (IoT) healthcare. The reproductive way is sparked by the bee swarm and the collaborative decision-making of FDO. As opposed to the honey bee or artificial bee colony algorithms, this algorithm has no connection to them. In FDO, the search agent's position is updated using speed or velocity, but it's done differently. It creates weights based on the fitness function value of the problem, which assists lead the agents through the exploration and exploitation processes. Other algorithms are evaluated and compared to FDO as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) in the original work. The key current algorithms-The Salp-Swarm Algorithms (SSA), Dragonfly Algorithm (DA), and Whale Optimization Algorithm (WOA) have been evaluated against FDO in terms of their results. Using these FDO experimental findings, we may conclude that FDO outperforms the other techniques stated. There are two primary goals for this chapter: first, the implementation of FDO will be shown step-by-step so that readers can better comprehend the algorithm method and apply FDO to solve real-world applications quickly. The second issue deals with how to tweak the FDO settings to make the meta-heuristic evolutionary algorithm better in the IoT health service system at evaluating big quantities of information. Ultimately, the target of this chapter's enhancement is to adapt the IoT healthcare framework based on FDO to spawn effective IoT healthcare applications for reasoning out real-world optimization, aggregation, prediction, segmentation, and other technological problems. © 2022, CC BY.

关键词： Internet of things

SAARSNet: A Deep Neural Network for COVID-19 Cases Diagnosis 3

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SAARSNet: A Deep Neural Network for COVID-19 Cases Diagnosis

3rd International Conference on Advanced science and engineering, ICOASE 2020

作者： Abdulla, Hawre Kh. Ahmed, Zanear Sh. Surameery, Nigar M. Shafiq Rashid, Rasber Dh. Salih, Shadman Q. Technical College of Informatics Sulaimani Polytechnic University Database Technology Department Sulaimani Kurdistan Region Iraq Erbil Technology College Erbil Polytechnic University Information Technology Department Erbil Kurdistan Region Iraq College of Engineering University of Garmian Kalar Civil Engineering Department Sulaimani Kurdistan Region Iraq College of Science Salahaddin University Computer Science and It Department Erbil Kurdistan Region Iraq

ISBN: (纸本)9781665415798

The global spread of the COVID-19 is a continuously evolving situation and it is still a major risk on the health of people around the world. A huge number of people are infected by this deadly virus and the number is still getting increased day by day. At this time, no specific vaccines or treatments of COVID-19 are found. Numerous ways are offered to detect COVID-19 such as swab test, CDC and RT-PCR tests. All of them can detect corona virus in different ways but they are not recommended by the reason of their limited availability, inaccurate results, high false-negative rate predicates, high cost and time consuming. Hence, medical radiography and computer Tomography (CT) images were suggested as the next best alternative of RT -PCR and other tests for detecting Covid-19 cases. Recent studies found that patients with COVID-19 cases are present abnormalities in chest X-Ray images. Motivated by this, many researchers propose deep learning systems for COVID-19 detection. Although, these developed AI systems have shown quite promising results in terms of accuracy, they are closed source and unavailable to the research community. Therefore, in the present work, we introduced a deep convolutional neural network design (SAARSNet) designed to detect COVID-19 cases from chest X-Ray images. 1292 X-Ray images have been used to train and test the proposed model. the images have been collected from two open-source datasets. The input images are progressively resized into (220 by 150 by 3) in order to decrease the training time of the system and improve the performance of the SAARSNet architecture. Furthermore, we also investigate how SAARSNet makes predictions under three different scenarios with the aim of distinguishing COVID-19 class from both Normal and Abnormal classes as well as gaining deeper perceptions into critical factors related to COVID-19 cases. We also used the confusion metrics for evaluating the performance of SAARSNet CNN in an attempt to measure the true and

关键词： COVID-19

Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

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arXiv 2022年

作者： Rahman, Chnoor M. Rashid, Tarik A. Ahmed, Aram Mahmood Mirjalili, Seyedali Applied Computer Department College of Medicals and Applied Sciences Charmo University Sulaimany Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Department of Information Technology Technical College of Informatics Sulaimani Polytechnic University Sulaimany Iraq Department of Information Technology College of Science and Technology University of Human Development Sulaimany Iraq Centre for Artificial Intelligence Research and Optimization Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of

In this work, a new multiobjective optimization algorithm called multiobjective learner performance-based behavior algorithm is proposed. The proposed algorithm is based on the process of transferring students from high school to college. The proposed technique produces a set of non-dominated solutions. To judge the ability and efficacy of the proposed multiobjective algorithm, it is evaluated against a group of benchmarks and five real-world engineering optimization problems. Additionally, to evaluate the proposed technique quantitatively, several most widely used metrics are applied. Moreover, the results are confirmed statistically. The proposed work is then compared with three multiobjective algorithms, which are MOWCA, NSGA-II, and MODA. Similar to the proposed technique, the other algorithms in the literature were run against the benchmarks, and the real-world engineering problems utilized in the paper. The algorithms are compared with each other employing descriptive, tabular, and graphical demonstrations. The results proved the ability of the proposed work in providing a set of non-dominated solutions, and that the algorithm outperformed the other participated algorithms in most of the cases. © 2022, CC BY-NC-SA.

关键词： Multiobjective optimization

Metagenomic Characterization of Biomethane Transformation by Lipid-Catalyzed Anaerobic Fermentation of Lignite

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SSRN

SSRN 2022年

作者： Guo, Hongyu Song, Bo Xia, Dapin Deng, Ze Cai, Yidong Wang, Yongjun Su, Xianbo Zhao, Weizhong School of Energy Science and Engineering Henan Polytechnic University Jiaozuo454000 China Mining Research Institute of Henan Polytechnic University Jiaozuo454000 China Institute of Petroleum Exploration and Development Beijing100083 China School of Energy Resources China University of Geosciences Beijing100083 China College of Computer Science and Technology Henan Polytechnic University Jiaozuo454000 China Department of Environmental Engineering Technical University of Denmark LyngbyDK-2800 Denmark Henan Polytechnic University China

The present study aims at using lipid in a novel way to improve the efficiency of methane production from lignite anaerobic digestion. The obtained results showed an increase by 3.13 times of the cumulative biomethane content of lignite anaerobic fermentation, when 1.8 g lipid was added. The gene expression of functional metabolic enzymes was also found to be enhanced during the anaerobic fermentation. Moreover, the enzymes related to fatty acid degradation such as long-chain Acyl-CoA synthetase and Acyl-CoA dehydrogenase were increased by 1.72 and 10.48 times, respectively, which consequently, accelerated the conversion of fatty acid. Furthermore, the addition of lipid enhanced the carbon dioxide trophic and acetic acid trophic metabolic pathways. Hence, the addition of lipids was argued to promote the production of methane from lignite anaerobic fermentation, which provided a new insight for the conversion and utilization of lipid waste. © 2022, The Authors. All rights reserved.

关键词： Fatty acids

Retraction Note: Flow monitoring system and abnormal log traffic mode detection based on artificial intelligence

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Optical and Quantum Electronics 2024年第10期56卷 1-1页

作者： Cao, Jinghua Pan, Bo Zou, Xiang School of Computer and Information Engineering Guangdong Songshan Polytechnic College Shaoguan China School of Computer and Information Engineering Guangdong Information Vocational and Technical College Shaoguan China School of Mathematics and Statistics Lingnan Normal University Zhanjiang China Neusoft Education Technology Group IDC Dalian China

Semantic Prior Distillation with Vision Foundation Model for Enhanced Rapid Bone Scintigraphy Image Restoration

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arXiv 2025年

作者： Liang, Pengchen Shi, Leijun Yao, Huiping Pu, Bin Chen, Jianguo Zhao, Lei Huang, Haishan Chen, Zhuangzhuang Xu, Zhaozhao Xu, Lite Chang, Qing Li, Yiwei Department of Nuclear Medicine Shanghai Children’s Hospital School of Medicine Shanghai Jiao Tong University Shanghai200062 China School of Microelectronics Shanghai University Shanghai201800 China Department of Ophthalmology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai201800 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology 999077 Hong Kong School of Software Engineering Sun Yat-sen University Guangdong Province Zhuhai519000 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Computer Science and Technology Henan Polytechnic University Henan Jiaozuo454000 China Department Shanghai Key Laboratory of Gastric Neoplasms Department of Surgery Shanghai Institute of Digestive Surgery Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai201800 China

Rapid bone scintigraphy is an essential tool for diagnosing skeletal diseases and tumor metastasis in pediatric patients, as it reduces scan time and minimizes patient discomfort. However, rapid scans often result in poor image quality, potentially affecting diagnosis due to reduced resolution and detail, which make it challenging to identify and evaluate finer anatomical structures. To address this issue, we propose the first application of SAM-based semantic priors for medical image restoration, leveraging the Segment Anything Model (SAM) to enhance rapid bone scintigraphy images in pediatric populations. Our method comprises two cascaded networks, fIR1 and fIR2, augmented by three key modules: a Semantic Prior Integration (SPI) module, a Semantic Knowledge Distillation (SKD) module, and a Semantic Consistency Module (SCM). The SPI and SKD modules incorporate domain-specific semantic information from a fine-tuned SAM, while the SCM maintains consistent semantic feature representation throughout the cascaded networks. In addition, we will release a novel Rapid Bone Scintigraphy dataset called RBS, the first dataset dedicated to rapid bone scintigraphy image restoration in pediatric patients. RBS consists of 137 pediatric patients aged between 0.5 and 16 years who underwent both standard and rapid bone scans. The dataset includes scans performed at 20 cm/min (standard) and 40 cm/min (rapid), representing a 2× acceleration. We conducted extensive experiments on both the publicly available endoscopic dataset and RBS. The results demonstrate that our method outperforms all existing methods across various metrics, including PSNR, SSIM, FID, and LPIPS. Copyright © 2025, The Authors. All rights reserved.

关键词： Pediatrics

A new method of vision-based seat belt detection

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A new method of vision-based seat belt detection

作者： Yang, Zhongming Xiong, Hui Cai, Zhaoquan Peng, Yu College of Computer Engineering Technical Guangdong Polytechnic of Science and Technology Zhuhai Guangdong China School of Software Engineering South China University of Technology Guangzhou Guangdong China Science and Technology Department Huizhou University Huizhou Guangdong China

In the traffic management system, it can greatly improve the management efficiency if the algorithm that monitors automatically detects whether the driver fastens the seat belt;however, currently prevalent detecting methods cannot achieve satisfactory results in aspects of the detecting rate, the image quality requirement and the colour difference between seat belt and the surrounding environment. We propose a method of seat belt detection based on visual positioning. The algorithm locates the window according to the licence plate position and the contour statistics obtained from the gradient. The face detection is used to adjust and determine the seat belt detection area in the window. Finally, the method of seat belt detection based on the connected area is used to detect whether the seat belt is fastened. Experiments show that the successful rate of the proposed method is much higher than other existing methods, and satisfactory results are obtained. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Face recognition

Heart Attack Classification System using Neural Network Trained with Particle Swarm Optimization

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arXiv 2022年

作者： Amin, Askandar H. Ahmed, Botan K. Maaroof, Bestan B. Rashid, Tarik A. Technical College of Informatics Sulaimani Polytechnic University KRG Sulaimani Iraq Administration and Economics University of Sulaimani KRG Sulaimani Iraq IT Department College of Commerce University of Sulaimani KRG Sulaimani Iraq School of Information Technology Fanshawe College London Canada Computer Science and Engineering Dept. University of Kurdistan Hewler KRG Erbil Iraq

The prior detection of a heart attack could lead to the saving of one’s life. Putting specific criteria into a system that provides an early warning of an imminent attack will be advantageous to a better prevention plan for an upcoming heart attack. Some studies have been conducted for this purpose, but yet the goal has not been reached to prevent a patient from getting such a disease. In this paper, Neural Network trained with Particle Swarm Optimization (PSONN) is used to analyze the input criteria and enhance heart attack anticipation. A real and novel dataset that has been recorded on the disease is used. After preprocessing the data, the features are fed into the system. As a result, the outcomes from PSONN have been evaluated against those from other algorithms. Decision Tree, Random Forest, Neural network trained with Backpropagation (BPNN), and Naive Bayes were among those employed. Then the results of 100%, 99.2424%, 99.2323%, 81.3131%, and 66.4141% are produced concerning the mentioned algorithms, which show that PSONN has recorded the highest accuracy rate among all other tested algorithms. © 2022, CC BY.

关键词： Swarm intelligence