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4th International Conference on Inventive Research in Computing applications, ICIRCA 2022

作者： Bhardwaj, Vivek Rahul, K. Venkata Kumar, Mukesh Lamba, Vikas School of Computer Science and Engineering Lovely Professional University Jalandhar144411 India School of Computer Application Lovely Professional University Punjab Phagwara India Chitkara University Institute of Engineering & Technology Chitkara University Punjab India

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

As vast amounts of data are generated every day in many organizations, health, education, transport, business, marketing, communication, etc. All the data cannot be used for every analysis and processing as it contains some redundant data, outliers, some corrupted data, missing values and so on. To gain the knowledge-based and predictive data from this data, Data science plays a crucial role. Data science is a process of analysis and differentiating historical data to evaluate patterns or predict the occurrence of a future event using its robust algorithms. Many Organizations nowadays realized the importance of Data science and started implementing it in their strategic analysis and planning processes. This research study analyses the historical data of some stock market companies between certain time periods and perform analysis by using a data science algorithm and check the accuracy of predictions. Finally, the prediction results are compared with the accuracy of other algorithms and the best possible algorithm will be selected. © 2022 IEEE.

关键词： Forecasting

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Reinforcement Learning for Predictive Modeling and Management of Rare Genetic Disorders in Pediatric Healthcare 3

Reinforcement Learning for Predictive Modeling and Managemen...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Chaudhary, Pavan Inzimam Hannah Jessie Rani, R. Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India Electrical and Electronics Engineering Faculty of Engineering and Technology Karnataka Bangalore India

ISBN: (纸本)9798350343274

The difficulties caused by rare genetic abnormalities in pediatric healthcare highlight the need for advances in predictive modeling and treatment choices. An innovative method is discussed in the article 'Reinforcement Learning for Predictive Modeling and Management of Rare Genetic Disorders in Pediatric Healthcare.' Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Actor-Critical are the three RL algorithms used in the suggested approach. To determine the most effective treatment for children with uncommon genetic illnesses, we used these algorithms to create a prediction model. To improve healthcare quality, we carefully evaluate the suggested approach alongside six alternative methods with overlapping but distinct names. We also take accuracy and precision into account while making our assessments. The suggested strategy, which we refer to as the 'Novel Genetic Predictor (NGP),' excels in every metric we tested. The results show a considerable improvement in the detection of rare genetic disorders in children. NGP also demonstrates an enhanced ability to make precise predictions, which opens the path for treatment alternatives that are both patient-specific and more effective. This research not only shows the promise of RL in pediatric healthcare, but also emphasizes the need for tailored care for children with rare genetic disorders. The results show that NGP is better than other methods, and they provide doctors a tool they may use to better care for their patients. © 2023 IEEE.

关键词： Reinforcement learning

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A Data Commercialization Evaluation Model Based on Fuzzy Multi-attribute Group Decision Making 23

A Data Commercialization Evaluation Model Based on Fuzzy Mul...

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Proceedings of the 2023 4th International Conference on Big Data Economy and Information Management

作者： Bin Zhao Li Cao Yilong Gao Peng Zhai School of Information Science and Engineering Linyi University China School of Mathematics and Computer Application Technology Jining University China

ISBN: (纸本)9798400716669

The core purpose of data commoditization transformation is to lower the threshold of data utilization and facilitate data analysis and mining at the user end. In this paper, we propose a data commoditization degree evaluation model for data trading websites. First, the evaluation index system of data commercialization is constructed, combining the abstract indexes reflecting data commonality and industry-specific data commercialization standards to assess the degree of data commercialization; second, the fuzzy multi-attribute group decision-making method is utilized to conduct fuzzy evaluation of the indexes in response to the issue of user evaluation preference; and the fuzzy function corresponding to the fuzzy evaluation is calculated based on the fuzzy structured element afterwards. The degree of superiority of the program is calculated by fuzzy compromise group decision-making method. Finally, the simplicity, convenience, and effectiveness of the data commoditization assessment model are demonstrated through a speech data case study.

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Impact of Glucocorticoid Toxicity Index on Immunosuppressive Management of Inflammatory Bowel Disease 5th

Impact of Glucocorticoid Toxicity Index on Immunosuppressive...

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5th International Conference on Data science, Machine Learning and applications, ICDSMLA 2023

作者： Yadav, Dhyan Chandra Sharma, Sunil Agarwal, Chhaya Mehta, Deepak Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Biotechnology Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Glucocorticoid toxicity index (GTI) is a useful degree proposed by using a meta-evaluation to assess the chance of systemic glucocorticoid exposure in sufferers laid low with inflammatory bowels illnesses (IBD). The GTI is computed based totally on cumulative glucocorticoid cumulative dose and duration within the first yr. After a prognosis of IBD. The contemporary take a look at objectives to check the literature to decide the utility of GTI in predicting the danger of glucocorticoid toxicity in IBD patients. A literature search turned into conducted to pick out research that evaluated the predictive price of GTI for toxicity in IBD patients. The retrieved articles suggest that cumulative glucocorticoid dose and length prior to three hundred and sixty five days of the diagnosis of IBD have been massive factors related to the prevalence of glucocorticoid toxicity in IBD sufferers. However, different next elements may additionally have an effect on the toxicity of glucocorticoid exposure in IBD patients, along with age, comorbidities, and genetic polymorphisms. Moreover, some of the research, the outcomes validated that GTI can be a valuable device for immunosuppressive management in IBD sufferers to be able to discover patients who're much more likely to revel in systemic toxicity at some stage in immunosuppressive treatment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cumulative Exposure Glucocorticoid

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Evaluating Glucocorticoid Toxicity Index as a Predictor of Intestinal Adhesion Formation After Abdominal Surgery 5th

Evaluating Glucocorticoid Toxicity Index as a Predictor of I...

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5th International Conference on Data science, Machine Learning and applications, ICDSMLA 2023

作者： Tyagi, Pankaj Kumar Beemkumar, N. Agarwal, Ankita Kaushik, Harshita Department of Biotechnology Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

Stomach surgical operation can cause damage to intestinal tissue and is associated with a better risk of adhesive intestinal ailment. The main symptom of the adhesive intestinal disorder is intestinal adhesion, a continual and probably life-threatening hassle. Glucocorticoid use at the time of abdominal surgical procedures has been related to an increase in the danger of adhesive intestinal sickness. In addition to recognizing this disorder’s threat and protecting factors, the existing examination evaluated the Glucocorticoid Toxicity Index (GTI) as a predictor of intestinal adhesion formation in patients’ present process of stomach surgical operation. A prospective discussion was conducted on 220 sufferers who underwent stomach surgical treatment at an urban coaching hospital. The GTI change was measured at the time of the surgical treatment, and the patients were accompanied for one year after their surgery. The consequences of the look showed that there had been a statistically significant affiliation between higher GTI and the occurrence of postoperative intestinal adhesion after a stomach surgical operation. Moreover, the facts confirmed that there had been an additive effect of other chance factors, together with the acute inflammatory procedure or impaired wound healing, on the hazard of adhesion formation while blended with GTI. Those results propose that GTI is a dependable predictor of intestinal adhesion formation after a belly operation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Transplantation (surgical)

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A Method of Constructing and Automatically Labeling Radio Frequency Signal Training Dataset for UAV 48

A Method of Constructing and Automatically Labeling Radio Fr...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Chao Ma, Ruipeng Si, Zheng Chi, Mingmin Fudan University School of Computer Science Shanghai Key Laboratory of Data Science China Zhengzhou Zhongke Institute of Integrated Circuit and System Application China Zhengzhou University School of Cyber Science and Engineering China Zhengzhou University Henan Institute of Advanced Technology China

ISBN: (纸本)9781728163277

The problem of signal detection and classification of multiple UAVs can be solved using object detection techniques in computer vision. However, this requires collecting and labeling a large amount of reliable raw data. Since the UAV signal dataset cannot be directly applied to object detection, we propose a method using time-frequency domain filtering and automatic labeling to construct a large-scale time-frequency spectrogram dataset. Experimental results show that the average recognition accuracies of image transmission signals and remote control signals under interference conditions are 97% and 82%, respectively, while the average errors of signal parameters are 0.93% and 5.57%. © 2023 IEEE.

关键词： Object detection

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FViT: Finetuned Vision Transformer for Improved Brain Tumor Classification

FViT: Finetuned Vision Transformer for Improved Brain Tumor ...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Chandra, Lalini Chandra, Tej Bahadur Rathy, Anirud Rai, Anushka National Institute of Technology Department of Computer Application Chhattisgarh Raipur India Bennett University School of Computer Science Engineering and Technology UP Greater Noida India

ISBN: (纸本)9798331530259

Timely detection and treatment of brain tumors (BT) remain a critical challenge due to their rapid growth and complex morphologies. Recent deep learning (DL) approaches and large vision models (LVMs) have promising potential in early and accurate diagnosis of BTs. However, the optimal tuning of hyperparameters is crucial for increasing the model's efficacy and robustness. Large vision models have significant potential for improving the accuracy of diagnosing BT using MRI images. Additionally, we evaluated and compared the performance of recent DL models ResNet50, VGG16, and Long Short-Term Memory (LSTM) using BT MRI dataset. Through rigorous experimentation, our results highlight the superior diagnostic capabilities of the FViT model with an accuracy (ACC) of 99.68% and an F1-score (FS) of 99.68% for BT classification (using training-validation set). This result is also verified with high ACC of 99.31% and FS of 99.31%using testing set. The study demonstrates how well FViT model performs in classifying BT accurately, showcasing its potential in clinical settings. These findings emphasize the critical role of fine-tuning LVM models in advancing BT diagnosis through brain MRI images. © 2025 IEEE.

关键词： Diagnostic radiography

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FViT: Finetuned Vision Transformer for Improved Brain Tumor Classification

FViT: Finetuned Vision Transformer for Improved Brain Tumor ...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Lalini Chandra Tej Bahadur Chandra Anirud Rathy Anushka Rai Department of Computer Application National Institute of Technology Raipur Chhattisgarh India School of Computer Science Engineering and Technology Bennett University Greater Noida UP India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

Timely detection and treatment of brain tumors (BT) remain a critical challenge due to their rapid growth and complex morphologies. Recent deep learning (DL) approaches and large vision models (LVMs) have promising potential in early and accurate diagnosis of BTs. However, the optimal tuning of hyperparameters is crucial for increasing the model’s efficacy and robustness. Large vision models have significant potential for improving the accuracy of diagnosing BT using MRI images. Additionally, we evaluated and compared the performance of recent DL models ResNet50, VGG16, and Long Short-Term Memory (LSTM) using BT MRI dataset. Through rigorous experimentation, our results highlight the superior diagnostic capabilities of the FViT model with an accuracy (ACC) of 99.68% and an F1-score (FS) of 99.68% for BT classification (using training-validation set). This result is also verified with high ACC of 99.31% and FS of 99.31%using testing set. The study demonstrates how well FViT model performs in classifying BT accurately, showcasing its potential in clinical settings. These findings emphasize the critical role of fine-tuning LVM models in advancing BT diagnosis through brain MRI images.

关键词： Deep learning Accuracy Magnetic resonance imaging Brain tumors Brain modeling Transformers Robustness Long short term memory Tuning Testing

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SS-WSSS: Small-Scale Weakly Supervised Semantic Segmentation for Histopathology Image

SS-WSSS: Small-Scale Weakly Supervised Semantic Segmentation...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Jiatai Han, Guoqiang Zhou, Jingxuan Shi, Zhenwei Liu, Zaiyi Han, Chu South China University of Technology Guangzhou China Shenzhen China South China University of Technology School of Computer Science and Engineering Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou China

ISBN: (纸本)9798350386226

Semantic segmentation for histopathology images is one of the fundamental tasks in computational pathology. Due to the high cost of pixel-level annotation acquisition, the weakly supervised semantic segmentation (WSSS) attempts to achieve information-intensive segmentation task for histopathology images to reduce the labeling effort of pathologists by leveraging image-level labels. However, traditional WSSS requires a large-scale training set with image-level labels, which still imposes considerable labeling costs on pathologists. To this end, this work proposes a Small-Scale Weakly Supervised Semantic Segmentation (SS-WSSS) approach to achieve the comparable performance only with small-scale weakly-labeled data to further reduce pathologist's labeling effort. Since histopathology images can easily generate massive unlabeled data, our SS-WSSS aims to learn with the unlabeled data to bridge the information gap. First, we propose a Single-to-Multi Prototype Similarity (S2M-PS) method to generate reliable pseudo-labels for unlabeled data by measuring the similarity between single-label prototypes and multi-label feature maps. Then, we introduce a Cross-Task CoTraining (CT2) method for pseudo-supervision of models with pseudo-labels self-refinement to avoid overfitting to noisy labels. We conduct the experiment on two public datasets to demonstrate the effectiveness of our SS-WSSS. In the experiment, our method achieves comparable performance with SOTA methods only using 30% labeled data. © 2024 IEEE.

关键词： Semantic Segmentation

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A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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