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Evaluation of CatBoost for Diabetes Prevention in Comparison to XGBoost: To avert mortality by developing an AI model capable of predicting the onset of diabetes

Evaluation of CatBoost for Diabetes Prevention in Comparison...

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Electronic Systems and Intelligent Computing (ICESIC), International Conference on

作者： G. Gifta Jerith S. Jagadeesh P. Ponmathi Jeba Kiruba C Mahesh S. Anantha Babu S. Samsudeen Shaffi Dept. of CSE (Artificial Intelligence and Machine Learning) School of Engineering Malla Reddy University Hyderabad India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Saveetha Engineering College Chennai India Department of Information Technology Panimalar Engineering College Chennai India Department of Computer Science and Engineering GITAM School of Technology GITAM University Bangalore India

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

Diabetes is a metabolic condition defined by consistently elevated blood glucose levels. Hyperglycemia is characterized by heightened hunger, thirst, and urination. Numerous consequences may arise from untreated diabetes. Diabetic ketoacidosis, hyperosmolarity, hyperglycemia, and even mortality may occur as acute consequences. Heart disease, stroke, chronic renal failure, foot ulcers, and eye impairment are some of the worst long-term consequences. A total of 463 million individuals were found to have diabetes in 2019, according to the worldwide diabetes organisation. Assuming predictions come true, that number will soar to 700 million by 2045, up 578 million by 2030. It has modern medical applications, allowing for the rapid and precise diagnosis of illness. Thus, we may use machine learning to avert mortality by developing an AI model capable of predicting the onset of diabetes; to determine which algorithm is most suited for this task, we will compare the CatBoost and XGBoost methods. In the end, the research compared two diabetes prediction models, XGBoost and CatBoost, using a variety of health indicators from the dataset. Using a real-world dataset obtained from Kaggle, our suggested system is trained and built using Python. Performance assessment criteria such as precision, recall, F1score and accuracy measurements are used to thoroughly analyse our methods. In comparison to XGBoost's 88.33% accuracy rate, 85.25 percent precision, 85.63 percent recall, and an F1 Score of 86.50. CatBoost's 91.08% percent accuracy, 85.25 percent precision, 85.63 percent recall, and 87.38 F1 Score. CatBoost marks it as the most successful ensemble approach.

关键词： Deep learning Heart Accuracy Machine learning algorithms Prevention and mitigation Mortality Medical services Predictive models Prediction algorithms Diabetes

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SwinIR Transformer Applied for Medical Image Super-Resolution

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Procedia computer Science 2022年 204卷 907-913页

作者： Muralikrishna Puttagunta Ravi Subban Nelson Kennedy Babu C Dept of Computer Science School of Engineering and Technology Pondicherry University India Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Super-resolution refers to artificially enhancing the resolution of a low-resolution (LR) image to get a high-resolution image (HR), which is an effective technique in image processing and computer vision. Depending on the modality used in medical image processing, various variables may affect the spatial resolution of an image. Increasing the resolution of medical images by super-resolution is essential for a more precise comprehension of the anatomy. Recently, several papers revealed that deep learning might be successfully used, resulting in state-of-the-art results for various practical medical image applications. This paper evaluated single image SR architectures, including SRGAN, BSRGAN, RealESRGAN, and SwinIR using medical images. The SwinIR considerably enhanced the peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) compared to other architectures.

关键词： Image super-resolution deep learning Swin Transformer

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ADAMAX-Based Optimization of Efficient Net V2 for NSFW Content Detection

ADAMAX-Based Optimization of Efficient Net V2 for NSFW Conte...

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Contemporary Computing and Communications (InC4), IEEE International Conference on

作者： Chayanika Arora Gaurav Raj Akshat Ajit Aditya Saxena Dept. of Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Greater Noida Greater Noida India

In recent years, the need for automatic detection of Not Safe for Work (NSFW) content on social media platforms has increased dramatically. In this study, we contrasted the presentation of five optimizers, namely ADAM, ADAMAX, ADAMW, SGD, and ADAGRAD, on the EfficientNet-V2M and V2L models for NSFW content detection. We used a dataset consisting of NSFW images for training and testing the models. The results show that the ADAM optimizer performed better than the other optimizers with an accuracy of 98.80% for training and that for testing is 95.60% for the EfficientNet-V2L model. However, all the optimizers performed reasonably well with same parameters. This study provides valuable insights into the selection of optimizers for NSFW content detection using deep learning models. The dataset used in the research consists of a large number of images with explicit and non-explicit content. The results were evaluated based on accuracy, precision, recall, F1-score, loss, and area under the curve (AUC). The findings indicate that ADAM, ADAMAX, and ADAMW outperform the other optimizers in terms of all evaluation metrics. With the easy availability of explicit content online, it has become a concern for society, especially for children who are easily influenced by such content on various platforms. Exposure to unfiltered and inappropriate content can have a negative impact on young minds. To safeguard against such content, the authors of this paper review and analyze different approaches for detecting and filtering pornographic and NSFW content. The objective of this paper is to provide a filtered and safe content environment for the community, especially for children and teenagers who are the most vulnerable.

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An Artificial Intelligence Approach in 5G New Radio Beam Enhancement

An Artificial Intelligence Approach in 5G New Radio Beam Enh...

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International Conference on Secure Cyber Computing and Communication (ICSCCC)

作者： Deepak Upadhyay Saksham Mittal Ayushi Jain Ravi Sharma Piyush Bagla Neha Tripathi Pallavi Tiwari Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science & Engineering Dept. National Institute of Technology Jalandhar Punjab India Dept. of Computer Science & Engineering Graphic Era (Deemed to be University) Dehradun Uttarakhand India

The paper presents a novel application of artificial neural networks in the context of 5G new radio beamforming. The research leverages the intricate mechanism of ANNs to demonstrate the efficacy of this approach in optimizing the beamforming process and improving the overall performance of the 5G new radio system. The utilization of ANNs allows for real-time adaptation and decision-making, thereby mitigating the limitations of conventional beamforming techniques. The results of the study are quite promising, indicating a substantial enhancement in the accuracy and efficiency of beamforming in 5G new radio. The proposed method is expected to have a significant impact on the development of advanced wireless communication systems.

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USDOT Tier-1 University Transportation Center for Advancing Cybersecurity Research and Education 1

USDOT Tier-1 University Transportation Center for Advancing ...

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1st IEEE International Conference on Mobility, Operations, Services and Technologies, MOST 2023

作者： Zhang, Yunpeng Jack Wang, Kailai Song, Lingguang Lendasse, Amaury Song, Houbing Herbert Han, Zhu Verma, Rakesh Silva, Arlei Rubio-Medrano, Carlos Li, Zhixia Richard Zhang, Guohui University of Houston Dept. of Information Science Technology HoustonTX United States University of Houston Dept. of Construction Management HoustonTX United States University of Maryland Baltimore County Dept. of Information Systems BaltimoreMD United States University of Houston Dept. of Electrical & Computer Engineering HoustonTX United States University of Houston Dept. of Computer Science HoustonTX United States Rice University Dept. of Computer Science HoustonTX United States Texas A&m University-Corpus Christi Dept. of Computer Science Corpus ChristiTX United States University of Cincinnati Dept. of Civil and Architectural Engineering and Construction Management CincinnatiOH United States University of Hawai'i at Mānoa Dept. Civil and Environmental Engineering HonoluluHI United States

ISBN: (纸本)9798350319958

The Transportation Cybersecurity Center for Advanced Research and Education (CYBER-CARE) is a US Department of Transportation (USDOT) Tier-1 University Transportation Center (UTC) funded in 2023. CYBER-CARE primarily focuses on the USDOT statutory research priority area of 'Reducing Transportation Cybersecurity Risks.' CYBER-CARE aims to establish a fundamental knowledge basis and explore advanced theory to mitigate the impacts of large-scale cyberattacks on transportation infrastructure and connected and automated vehicle (CAV) systems. The research projects at CYBER-CARE will develop conceptual frameworks, construct comprehensive datasets, explore novel analytical approaches, support the implementation of public policies and infrastructure investments, and build a high-quality industry workforce through education. All CYBER-CARE research projects can be organized into four thrusts: CAV cybersecurity, transportation data security, advanced traffic management system (ATMS) cybersecurity, and next-generation transportation cybersecurity systems. In addition, CYBER-CARE will accelerate industry collaborations, foster new technologies, and provide professionals with the skills and opportunities needed to become successful leaders in their fields. Notably, as CYBER-CARE will prioritize engagement with underrepresented minorities, these communities stand to benefit from professional development training in transportation cybersecurity. © 2023 IEEE.

关键词： technology transfer

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An Efficient Model for Early Detection of Maize Leaf Disease using Deep Learning Approaches

An Efficient Model for Early Detection of Maize Leaf Disease...

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Innovation in technology (ASIANCON), Asian Conference on

作者： Shanta Kallur Suman Yaligar Puneeth N. Thotad Dept. of Computer Science and Engineering K.L.E.Institute of Technology Hubballi India Affiliated to Visvesvaraya Technological University Belagavi Dept. of Master of Computer Applications K.L.E.Institute of Technology Hubballi India

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

Maize plays a fundamental role as a staple food for millions worldwide. However, the significant threat posed by various diseases to maize cultivation has led to substantial yield losses on a global scale. Early and accurate detection of these diseases is essential for prompt intervention and effective treatment strategies. In this regard, the development of advanced detection methods employing deep learning and image processing has gained attention, offering the potential to revolutionize disease identification and management practices. Accordingly, this research introduces a Deep Convolutional Neural Network model combined with Transfer Learning techniques, reducing the training time for the AI model. Furthermore, Data Augmentation practices are employed to improve accuracy by addressing dataset variability. The projected model achieves an impressive accuracy of 98.6%, crucial for mitigating current crop wastage resulting from undetected diseases. Enhancing accuracy further is essential for improving current crop yield conditions impacted by undetected diseases. Moreover, a GUI-based application model is developed for early detection of maize diseases, aiding farmers in their fields. This accuracy can be further increased by incorporating additional parameters. Traditional image recognition and Deep Learning approaches are progressively making their way into the field of plant disease recognition.

关键词： Deep learning Training Productivity Adaptation models Technological innovation Accuracy Image recognition Transfer learning Convolutional neural networks Diseases

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Advanced Underwater Image Quality Enhancement via Hybrid Super-Resolution Convolutional Neural Networks and Multi-Scale Retinex-Based Defogging Techniques

arXiv

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arXiv 2024年

作者： Gogireddy, Yugandhar Reddy Gogireddy, Jithendra Reddy Dept. Computational Intelligence SRM Institute of Science and Technology Chennai India Dept. Computer Science and Engineering SRM University AP India

The difficulties of underwater image degradation due to light scattering, absorption, and fog-like particles which lead to low resolution and poor visibility are discussed in this study report. We suggest a sophisticated hybrid strategy that combines Multi-Scale Retinex (MSR) defogging methods with Super-Resolution Convolutional Neural Networks (SRCNN) to address these problems. The Retinex algorithm mimics human visual perception to reduce uneven lighting and fogging, while the SRCNN component improves the spatial resolution of underwater photos.[11] Through the combination of these methods, we are able to enhance the clarity, contrast, and colour restoration of underwater images, offering a reliable way to improve image quality in difficult underwater conditions. The research conducts extensive experiments on real-world underwater datasets to further illustrate the efficacy of the suggested approach. In terms of sharpness, visibility, and feature retention, quantitative evaluation which use metrics like the Structural Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) demonstrates notable advances over conventional techniques.[41] In real-time underwater applications like marine exploration, underwater robotics, and autonomous underwater vehicles, where clear and high-resolution imaging is crucial for operational success, the combination of deep learning and conventional image processing techniques offers a computationally efficient framework with superior results. Copyright © 2024, The Authors. All rights reserved.

关键词： Underwater imaging

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Systematic Exploration of Dialogue Summarization Approaches for Reproducibility, Comparative Assessment, and Methodological Innovations for Advancing Natural Language Processing in Abstractive Summarization

arXiv

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arXiv 2024年

作者： Gogireddy, Yugandhar Reddy Gogireddy, Jithendra Reddy Dept.Computational Intelligence SRM Institute of Science and Technology Chennai India Dept.Computer Science and Engineering SRM University AP India

Reproducibility in scientific research, particularly within the realm of natural language processing (NLP), is essential for validating and verifying the robustness of experimental findings. This paper delves into the reproduction and evaluation of dialogue summarization models, focusing specifically on the discrepancies observed between original studies and our reproduction efforts. Dialogue summarization is a critical aspect of NLP, aiming to condense conversational content into concise and informative summaries, thus aiding in efficient information retrieval and decision-making processes. Our research involved a thorough examination of several dialogue summarization models using the AMI (Augmented Multi-party Interaction) dataset. The models assessed include Hierarchical Memory Networks (HMNet) and various versions of Pointer-Generator Networks (PGN), namely PGN(DKE), PGN(DRD), PGN(DTS), and PGN(DALL). The primary objective was to evaluate the informativeness and quality of the summaries generated by these models through human assessment, a method that introduces subjectivity and variability in the evaluation process. The analysis began with Dataset 1, where the sample standard deviation of 0.656 indicated a moderate dispersion of data points around the mean. In our reproduction experiment, the scores were overall inferior to those in the original study, mostly all below 3.0 versus the original scores, which were all above 3.0. Specifically, the gold standard scores in our analysis were significantly lower than those reported in the original study. In our experiment, we noticed a distinct trend that contrasts with the findings of Feng et al. (2021), in which the performance of HMNet did not exhibit substantial gains over the PGN models. he original study regarding AMI/Informativeness did not demonstrate effectiveness: the baseline HMNet performed the best. This raises the question of whether the selection of the AMI dataset is appropriate for the human evaluation

关键词： Natural language processing systems

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Capacity Planning of Access Point Deployment for Indoor Positioning System

Capacity Planning of Access Point Deployment for Indoor Posi...

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2021 International Conference on Power, Energy and Innovations, ICPEI 2021

作者： Thitinaruemit, Aphirak Sitjongsatapom, Suchada Prongnuch, Sethakarn Mahanakorn University of Technology The Electrical Engineering Graduate Program Faculty of Engineering and Technology Bangkok Thailand Faculty of Engineering and Technology Dept. of Electronic Engineering Bangkok Thailand Suan Sunandha Rajabhat University Faculty of Industrial Technology Dept. of Computer Engineering Bangkok Thailand

ISBN: (纸本)9781665402163

This paper present the network design and capacity planning of access point (AP) deployment for indoor positioning system. The estimation of WiFi distance is described briefly how to measure the signal strength of AP. Co-channel interference is a key for network design of access point installation for network capacity. The objective function for network capacity planning of AP deployment is to optimize the number of access point and location for indoor positioning system concerning with the minimized co-channel interference. Simulations are tested to install AP for lecture hall in university. Capacity planning design of wireless AP deployment is proposed for indoor position systems in terms of the received signal strength indications (RSSI). We assume the situation is supported for the crowded conference. The requirement of the results of wireless AP positioning in crowded conference rooms are placing the wireless AP in forms of overlap the signal in order to get the strong signal. Experimental results have been tested their received signal of WiFi APs with the 2.4GHz frequency using the receiver installed in laptops to check the signal. The empirical results shown that the strength of RSSI will be received a signal of -44 dBm at a distance of approximately 20 meters. © 2021 IEEE.

关键词： Signal receivers

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An Analysis of Security Protocols for Cloud Computing Algorithms in Mobile Ad Hoc Networks

An Analysis of Security Protocols for Cloud Computing Algori...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Abdul Sajid Mohammed Madhusudhan Dasari Sreeramulu Anuteja Reddy Neravetla Ketan Gupta N. Yuvaraj Dept of School of Computer and Information Sciences University of the Cumberlands Kentucky USA Dept of Cyber Security American Express USA Department of Information Technology University of The Cumberlands Kentucky USA Department of Information Technology University of The Cumberlands Williamsburg KY USA Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Cellular ad Hoc Networks (MANETs) have become increasingly popular in cell computing packages, inclusive of cellular computing, fitness care, automobile, and army packages. As such, safety protocols used in the transmission of records over MANETs are of essential importance for their hit usage. In this paper, we endorse an evaluation of safety protocols for cloud computing algorithms in MANETs. We first discuss the main safety necessities that have to be satisfied. At the same time, a cloud set of rules is adopted in a MANET and then provides a survey of different security protocols that can be used to relax the cloud algorithms for MANETs. We are aware of precise cloud algorithms for MANETs: Opportunistic Routing and Cooperative Caching. We look at the safety of these cloud algorithms under three one-of-a-kind security protocols—SSL/TLS, IPSec, and 2FA—and examine the effect of these protection protocols on their execution. Finally, we summarize our findings and offer guidelines for the adoption of security protocols in MANETs for cloud-based total computing algorithms.

关键词： Surveys Cloud computing Protocols Tunneling Routing Security Protection Mobile computing Mobile ad hoc networks Sports

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