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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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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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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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An Automated Framework for the Segmentation of Skin Lesions Using Deep Learning

An Automated Framework for the Segmentation of Skin Lesions ...

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International Health Informatics Conference, IHIC 2023

作者： Garg, Anupam Kaur, Amrita Sunita Parashar, Anshu Computer Science and Engineering Department TIET Punjab Patiala147001 India Chandigarh India Computer Applications Department NIT Haryana Kurukshetra India

ISBN: (纸本)9789819771899

Skin cancer, a prevalent and potentially deadly condition resulting from factors such as UV rays exposure and ozone layer depletion, is a significant health concernworldwide, with approximately 5million cases reported annually. Its gravity lies in its ability to metastasize to the brain, causing complications like headaches, seizures, and paralysis. Early detection plays a pivotal role in mitigating mortality rates, yet subjective clinical assessments for lesion segmentation often fall short of the expectations of expert dermatologists. Biomedical image segmentation, which involves the automatic or semi-automatic delineation of boundaries in 2D images, encounters challenges due to variations in shape, location, size, and texture within medical images. Manual segmentation, a laborious and time-consuming endeavor, underscores the need for fully automated approaches. In this context, this paper introduces a UNet-based architecture enhanced by the pre-trained ResNet50 model. The comprehensive results showcase outstanding performance across five evaluation parameters for medical image segmentation. Notably, this GA-UNet approach surpasses traditional methods with an impressive accuracy rate of 98.7% and a Dice Similarity Coefficient of 89.6%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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A Network Optimized 3D Intercom Approach For UAV Communication Using 6G Network 1

A Network Optimized 3D Intercom Approach For UAV Communicati...

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1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems, iTech SECOM 2023

作者： Premalatha, N. Gayathri, P. Sangavi, N. Ramesh, K. Nandhakumar, R. Samraj, Andrews Pg and Research Department of Computer Science and Applications Namakkal India Mahendra College of Engineering Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798350371185

With such motivation, the routing optimization in 3D intercom facility is required when an UAV communication is focused. The ground stations control the autonomous UAVs such that it maintains a high-speed communication in remote areas. A swarm or a group of UAVs, on other hand entails a major issue when there exists an internetworking between them. Hence, the routing between the UAVs in the context of 3D intercom poses a design challenge. The Mobility of Unmanned Ariel Vehicle (UAV) enables the internetworking among them to form a cooperative cluster or swarm behavior. A three-dimensional (3D) intercommunication (intercom) is enabled as a preliminary feature in 6G network that supports high-speed communication. The combination of UAV at a greater altitude of various sizes and 3D intercom facility in 6G supports remote connections but at the cost of energy efficiency and high latency. In this paper, we develop a novel network-optimized 3D intercom (NO3DI) model to increase the connectivity between the UAVs with higher energy efficiency. This novel network-optimized model uses an intelligent Q-learning module to establish routing between the UAVs such that a reliable high-speed communication is maintained between the UAVs, which forms a strong intercommunication between the UAVs. Thus, a high-speed non-latent communication is established between the UAVs and the adoption of 6G networking maintains high data rate even in adhoc environments. The simulation is conducted in matlab to test the ability of internetwork among the UAVs under various UAV sizes and the Q-learning routing is tested in terms of packet delivery rate, latency and energy efficiency. The results of simulation shows that the proposed NO3DI achieves reduced energy consumption and maintains a reliable data rate with a high-speed packet delivery rate. Also, the latency is highly reduced as the Q-learning effectively routes the data packets by maintaining a high-speed communication between the UAVs. © 2023

关键词： Queueing networks

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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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GraphXAI: a survey of graph neural networks (GNNs) for explainable AI (XAI)

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Neural Computing and applications 2025年第17期37卷 10949-11000页

作者： Nandan, Mauparna Mitra, Soma De, Debashis Department of Computer Applications Techno Main Salt Lake West Bengal Kolkata India Department of Computational Science Brainware University West Bengal Barasat India Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology West Bengal West Bengal Kolkata India

Graphs find wide applications in numerous domains, ranging from simulating physical systems to learning molecular fingerprints, predicting protein interfaces, diagnosing diseases, etc. These applications encompass simulations in non-Euclidean space, in which a graph serves as an ideal representation, and are also an indispensable means of illustrating the connections and interdependencies among its various constituents. Graph neural networks (GNNs) are specific types of neural networks that are specifically built to handle data possessing a graph structure. They are highly effective at capturing intricate relationships among different entities. Nonetheless, their "black-box" characteristics pose difficulties in transparency, trust, and interpretability, especially in critical sectors like heath care, banking, and autonomous systems. Explainable artificial intelligence (XAI) has emerged to clarify these obscure decision-making processes, thus enhancing trust and accountability in AI systems. This survey paper delves into the intricate interplay between GNNs and XAI, including an exhaustive taxonomy of the various explainability methods designed for graph-structured data. It classifies the existing explainability methods into post hoc and self-interpretable models. The paper analyzes their practical applications in diversified fields, highlighting the significance of transparent GNNs in essential sectors such as fraud detection, drug development, and network security. The survey also delineates evaluation parameters for assessing explainability along with addressing persistent issues in scalability and fairness. The paper concludes by addressing prospective advancements in the subject, including the creation of innovative XAI methodologies tailored for GNN architectures, integration with federated learning, and utilization of these models in interdisciplinary fields. This study bridges the gap between GNNs and XAI, providing an essential resource for researchers and p

关键词： Graph neural networks

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An Analysis of Irrigation Management for Crops using Machine Learning Algorithms 1

An Analysis of Irrigation Management for Crops using Machine...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Thamilselvan, R. Natesan, P. Rajalaxmi, R.R. Prasanth, D. Nithish Swaminathan, S. Yuvapriya, R. Kongu Engineering College Computer Science and Engineering Perundurai Erode India Kongu Engineering College Department of Computer Applications Perundurai638 060 India

ISBN: (纸本)9798350372748

In every nation, agriculture has boosted the economy. Agriculture is currently dealing with a number of difficulties, such as irrigation and water management. Crop irrigation plays a crucial role in agricultural production by providing water to crops in regions where rainfall alone is insufficient to meet their needs. This abstract provides an overview of crop irrigation, its importance, and various irrigation methods used in agriculture. Irrigation is a critical component of agricultural production, and advancements in machine learning algorithms have the potential to optimize irrigation practices for crop cultivation. Additionally, machine learning has developed in several industries to improve quality. Machine learning algorithms like Random Forest, K-Nearest Neighbor, Support Vector Machine and Decision Tree are used to find the various results. Internet of Things also plays a vital role in irrigation by making use of the sensors. This review's investigation was conducted using a variety of machine learning algorithms. © 2024 IEEE.

关键词： Support vector machines

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Enriched Model of Intuitionistic Fuzzy Adaptive Noise Filtering on MR Brain Image

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SN computer science 2023年第2期4卷 166页

作者： Srinivasan, Preethi Saroj Gurunathan, Pradeep Department of Computer Science and Engineering Anna University Chennai India Department of Computer Applications A.V.C. College of Engineering Mayiladuthurai India

MRI is one of the major modality in assisting the medical experts to diagnose and treat different brain diseases such as cancer, epilepsy and stroke. Poor light illumination of MRI scanners, electronic interferences and radiofrequency emissions cause noise in MR images. Handling such noises in MR brain images is a challenging task. To overcome the issue, this paper aims to develop an enhanced model that has the ability to handle three different types of noises namely salt and pepper noise, speckle noise and Gaussian noise. The proposed enhanced intuitionistic fuzzy adaptive filter (IFAF) model removes noises in MR brain images by categorizing pixels into membership and non-membership grade. IFAF preserves the edges and detailed information of the images by adapting contrast enhancement histogram equalization. Conventional denoising models face uncertainties in distinguishing normal and noise affected pixel of an image. The performance of IFAF is compared over different noises and the results show IFAF provides better results than the other conventional noise filtering models. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Contrast enhancement Denoising Gaussian Intuitionistic fuzzy MRI Salt and pepper Speckle noise

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