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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Sharma, Himanshu Wasim, Javed Sharma, Pankaj Mangalayatan University Computer Science & Engineering Uttar Pradesh India Mangalayatan University Department of Computer Engineering and Applications Uttar Pradesh India Eshan College of Engineering Computer Science & Engineering Mathura India

ISBN: (纸本)9798350307757

The fundus image is a crucial diagnostic tool for diseases like diabetic retinopathy (DR) because it catches a retina, a back of an eye. This issue is most prevalent among diabetics and a leading contributor to blindness, particularly in young adults and those engaged in the workforce. Detecting DR in its early stages is crucial in reducing the likelihood of blindness in patients. An ophthalmologist performed DR screening and looked for lesions on the fundus imaging. Intelligent systems have the ability to make early identification of DR more efficient, especially with the recent advancements in machine learning in healthcare. This study compares and contrasts many methods for detecting DR in fundus images. This study lays out methods for improving upon pre-trained architectures such that they can correctly classify fundus images, allowing for early detection and prediction of diabetes. A concatenate model is presented in this comparative study as a method for classifying fundus images using ML and deep/transfer learning. Various model architectures are used, including SVM, Gradient Boosting, Efficient Net, Efficient-Net B4 and B7, MLP, CNN, ResNet, VGG-19, and DNN, among others. The models' accuracy, F1-score, recall, and precision are measured in experimental findings that compare them. Our strategies outperform a single model with results ranging from 84% to 96% accuracy, respectively. They found that our ML- DL-TL methods work well for automated DR classification employing fundus image data in this experiment. © 2024 IEEE.

关键词： Intelligent systems

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GD-STFA: Gradient Descent Sea Turtle Foraging Algorithm enabled Deep Q Network for Diabetic Retinopathy Detection

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Multimedia Tools and applications 2024年第18期83卷 53817-53836页

作者： Sabeena, A.S. Jeyakumar, M.K. Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Kumaracoil - 629180 Tamilnadu Thuckalay India Department of Computer Applications Noorul Islam Centre for Higher Education Kumaracoil - 629180 Tamilnadu Thuckalay India

Diabetic Retinopathy (DR) refers to a retinal disease that happens due to high glucose levels in the blood, and it leads to blindness globally. In order to delay or avoid vision loss and degradation, early detection, and treatment are necessary. Hence, the conception of an automated technique for precise DR recognition is necessary. Thus, a Deep Q network (DQN) with a Gradient Descent-Sea Turtle Foraging Algorithm (GD-STFA) is proposed that inherits Gradient Descent (GD) in Sea Turtle Foraging Algorithm (STFA), to detect DR from images. This method involves various phases, like pre-processing, segmentation, as well as DR detection. Here, the DR image quality is enhanced in the pre-processing phase using a median filter. After that, the segmentation is done by utilizing Swin-UNet and it is trained using the proposed GD-STFA algorithm. Finally, the DQN trained with GD-STFA finds if a person has DR or normal from his retinal images. The experimentation is performed in detail and validated on the Indian Diabetic Retinopathy Image Dataset benchmark dataset. Ultimately, the experimental outcomes demonstrate that the model is better than traditional techniques regarding optimal accuracy of 0.98, sensitivity of 0.984 and specificity of 0.971. Also, regarding the segmentation accuracy, the proposed model is around 0.906 accurate with a Dice coefficient of 0.963. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2023.

关键词： Median filters

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Artificial intelligence-inspired comprehensive framework for desertification prediction

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International Journal of Cognitive Computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification Internet of things Artificial neural network

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Application of XGBoost algorithm and grid search hyperparameter tuning to study health effects among individuals in the industrial area

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Multimedia Tools and applications 2025年 1-44页

作者： Johnson, Susymary Perumalsamy, Deepalakshmi Department of Computer Applications Kalasalingam Academy of Research and Education Virudhunagar District Tamil Nadu Krishnankoil626126 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar District Tamil Nadu Krishnankoil626126 India

This research focuses on utilizing data-driven machine learning techniques to study the impact of air pollution on the health of residents in industrial regions. The objective is to develop an efficient machine learning model that can accurately identify communities with a higher prevalence of health issues and diseases. A population-based survey was conducted in four geographical areas, collecting data on sociodemographic characteristics, health effects, perceived health status, and annoyance due to industries. The proposed model, XGBoost_BLR_GridCV, achieved high accuracy (ROC_AUC: 0.9215), precision of 92%, recall of 91% and f1-score of 91% in predicting the proximity of residents with chronic diseases. The results highlight the significant association between living in high proximity to industrial regions and increased rates of asthma, respiratory illness, and heart-related health problems. Comparative analysis with other tree-based models further demonstrates the effectiveness of the proposed model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Forestry

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Improving recommendations utilizing users’ demographic information

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Quality and Quantity 2024年第6期58卷 5559-5575页

作者： Dey, Avick Kumar Dutta Pramanik, Pijush Kanti Singh, Pradeep Kumar Choudhury, Prasenjit Department of Computer Applications DSMS College West Bengal Durgapur India School of Computer Science & amp Engineering Galgotias University Greater Noida India Department of Computer Applications & amp Technology Galgotias University Uttar Pradesh Greater Noida India Department of Computer Science & amp Engineering National Institute of Technology Durgapur West Bengal India

The exponential increase in digital data has increased the amount of available online information. This complicates the user’s decision-making. Most online merchants and service providers utilize recommendation systems to solve this problem and meet customer needs. The traditional collaborative filtering based approach faces enormous challenges in providing potential personalized recommendation results. The demographic information of users may improve personalized recommendation results. This research proposes an improved recommendation approach based on users’ demographic information. Compared with traditional collaborative filtering-based approaches, this approach provides improved results. The experimental results show the enhanced prediction accuracy of the proposed approach and significantly lower errors when experimenting with the MovieLens dataset. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Collaborative filtering Demographic information MAE Recommendation system RMSE

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Maximizing Data Privacy: Empirical Analysis of Multi-Keyword Data Search and Privacy Protection over Cloud Computing Paradigm 1

Maximizing Data Privacy: Empirical Analysis of Multi-Keyword...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Prakash, V.S. Karthikeyan, N. Kanagaraj, A. Shriidhar, P.J. Viiayakumar, P. Kristu Jayanti College Department of Computer Science Bengaluru India VMKV Engineering College Department of Computer Science Engineering Tamil Nadu Salem India Kongu Engineering College Department of Computer Applications Tamilnadu Erode India

ISBN: (纸本)9798350361780

In the realm of cloud computing, querying on secured information has emerged as a technology of critical importance. These kinds of searches make it possible for the owner of the data to search through encrypted data that is kept in the cloud without revealing any pertinent information. Numerous strategies, the most of which concentrate on conjunctive and disjunctive keyword searches, have been presented by researchers in an effort to improve the quality of the search experience. On the other hand, a disjunction will provide an excessive number of results, but a conjunction of all the keywords could only produce a small number of results. It is tough to customize the relevance of the keywords given the present schemes, which makes it difficult to acquire the results that are required. For the purpose of resolving these issues, we offer a unique technique that we name the Secured Multiple Keyword Searching Scheme (SMKSS). This scheme allows for the search to be conducted with the user specifying the amount of keywords that are included in the search result. A cross-validation is performed between the proposed scheme and the traditional Ranked Keyword Searching Model (RKSM) in order to determine how effective the novel scheme is. This particular quantity, n, can be utilized to personalize the relevance of the term. Furthermore, as a consequence of this, the owner of the data might acquire the desired search results that include any n terms from a keyword collection. Not only does the proposed system allow the conventional disjunctive and conjunctive keyword searches, but it also supports these searches when n equals one or the size of the keyword collection, respectively. There is a possibility that the keyword will be critical. Initially, we provide a formal definition of its security, and then we demonstrate that the proposed scheme is safe against the adaptive selected keyword attack in the standard model, and that it is also capable of providing some degree of def

关键词： Data privacy

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Deep Learning for Plant Species Classification

Deep Learning for Plant Species Classification

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Kumar, M. Sunil Neeraja, Peddinti Gunamaneni, Anusha Nirmala, Ankireddy Vijaya Nikhil, Gobburu Department of Computer Science and Engineering AP Tirupati India Department of Computer Applications AP Tirupati India Sree Vidyanikethan Engineering College Department of Computer Applications AP Tirupati India

ISBN: (纸本)9798350329186

Plant species classification is a fundamental task in botany, agriculture, and environmental science, providing valuable insights into biodiversity, ecological systems, and agricultural practices. Botanists and the general public could benefit from an automated system for quickly recognizing plant species. The protection of biodiversity necessitates a better understanding of plant species identification [1]. Traditional methods of plant species identification relying on hand-crafted features can be complex and challenging, especially for non-specialists who have trouble remembering specific botanical words. A potential method has emerged for automatic plant species identification utilizing machine learning and deep learning techniques. Deep learning is effective in extracting features because it excels at giving more detailed information about images. This paper presents the implementation of Convolutional Neural Networks (CNNs), a deep learning-based approach that extracts features from leaf images and facilitates the classification of plant species. Deep learning methods, particularly CNNs, have demonstrated superior performance compared to handcrafted feature-based approaches. The ability of CNNs to automatically learn discriminative features from images has greatly improved the accuracy and efficiency of plant species classification. By leveraging deep learning methods, the proposed approach extracts features directly from leaf images, eliminating the need for manual feature engineering. The CNN architecture captures intricate patterns and structures present in the leaf images, enabling robust classification of plant species. The application of CNNs in plant species classification has yielded significant advancements in the field, providing a reliable tool for both experts and non-experts in identifying plant species. The implementation of CNNs facilitates the extraction of features from leaf images, leading to improved accuracy and efficiency. © 2023 IEEE.

关键词： Biodiversity

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Analyzing the Impact of COVID-19 on Liver Cancer: A Comprehensive Study using XGBoost Classifier and Feature Selection Techniques 2

Analyzing the Impact of COVID-19 on Liver Cancer: A Comprehe...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Nair, Rekha R. Babu, Tina Kishore, S. Vilashini, V. Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte University Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Karnataka Bengaluru India

Healthcare systems around the world have faced challenges due to the Coronavirus disease (COVID-19) epidemic, which has taken resources and attention away from long-term diseases like liver cancer. To identify the effect of COVID-19 on liver cancer, proposed an effective research work with Extreme Gradient Boosting (XGBoost) classifier. The identification of the most suitable feature is executed through the utilization of SelectKBest, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) techniques. A comparative analysis is carried out to ascertain the more precise feature selection, employing Decision Tree, K-Nearest Neighbors (KNN), CatBoost, LightGBM, and XGBoost algorithms. Further, the proposed work undertakes an analysis of the impact of COVID-19 on liver cancer, encompassing both the pandemic and pre-pandemic periods. In comparison to other feature selection techniques and models, SelectKBest feature selection with Xgboost classifier provided an accuracy of 0.92. The proposed research work provides valuable insights to healthcare professionals and policy makers, providing a better understanding of the challenges faced by liver cancer patients during the pandemic. By meticulously exploring feature selection techniques and employing advanced machine learning models, this study quantifiably demonstrates the effectiveness of the proposed methodology in addressing the impact of COVID-19 on liver cancer, contributing to a more comprehensive understanding of this critical issue. © 2024 Elsevier B.V.. All rights reserved.

关键词： Principal component analysis

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Microplastic Identification in Seawater using Generative Adversarial Networks

Microplastic Identification in Seawater using Generative Adv...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Rajasekar, M. Geetha, Angelina Hindustan Institute Of Technology And Science Department Of Computer Applications Chennai India Departmnet Of Computer Science And Engineering Hindustan Insitute Of Technology And Science Chennai India

ISBN: (纸本)9798350364828

In recent years, microplastics-plastic particles smaller than five millimeters-have become a significant environmental contaminant. These tiny plastic pieces have been found permeating aquatic ecosystems globally, posing a threat to marine life upon ingestion. However, identification and quantification of microplastics in seawater samples remains an arduous task. The small size and translucent nature of microplastics makes them difficult to distinguish from natural particulates in seawater under a microscope. This has hindered efforts to accurately monitor microplastic pollution levels in the marine environment. Our goal in this study was to use cutting edge deep learning techniques to create an automated image based system for detecting microplastics in seawater. We chose to employ a generative adversarial network (GAN) architecture for its proven capability in generating highly realistic synthetic images. The GAN model was trained on a dataset of microscope images of microplastics and natural particulates extracted from seawater samples. Coastal seawater samples were first filtered to isolate particulates of a size range containing microplastics. Density separation was then used to separate the plastic microparticles from denser natural particulates. We implemented a deep convolutional GAN with a generator network to produce synthetic microplastic images, and a discriminator network to differentiate real from synthetic images. Through iterative adversarial training, the two networks evolved to produce and accurately classify images of microplastics with a high degree of realism. Advanced image processing techniques were used to enhance the training data. After training, the GAN model was evaluated on an annotated testing dataset of images containing real-world microplastics and natural particulates extracted from seawater. Our proposed GAN-based technique achieved a microplastic classification accuracy of 92.5% on the test set, demonstrating its effectiveness. The

关键词： Convolutional neural networks

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Classification of Ultrasound Breast Cancer Image Using Tuning Up the Hyper-Parameter of Convolutional Neural Network

SSRN

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SSRN 2023年第7期17卷 264-269页

作者： Bansal, Satish Jadon, Rakesh S. Gupta, Sanjay Kumar Prestige Institute of Management & Research Gwalior India Department of Computer Engineering Madhav Institute of Technology and Science Gwalior India SOS in Computer Science & Applications Jiwaji University Gwalior India

Breast cancer in women is a significant public health concern worldwide, with many cases going undiagnosed until the advanced stages. Early detection is crucial for proper treatment and improved outcomes. There are some pre-trained models used by authors for the detection of breast tumor, but these models require extensive computation power due to their many layers and parameters. To address this issue, it is required to proposed convolutional neural network (CNN) model with fewer training parameters for classification of ultrasound images dataset to determine that a particular image is either benign or malignant. In this paper, CNN model is proposed with changes in some hyperparameters like the number of filters, filter size, batch normalization, learning rate, epoch, and batch size, to achieve better accuracy with less computational power. The proposed model was compared to other pre-trained models, including ResNet50, Efficient Net, and VGG16, using two databases (database A for training and validation data, and database B for testing data). Our proposed classifier outperformed than pre-trained classifiers in terms of accuracy. © 2023, The Authors. All rights reserved.

关键词： Neural network models

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