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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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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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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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Empowering Early Detection: CervicalCareXG Revolutionizes Cervical type cancer Diagnostics 4

Empowering Early Detection: CervicalCareXG Revolutionizes Ce...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Verma, Shanti Suthar, Hiral Rami, Khyati L J University Department of Computer Applications Gujarat Ahmedabad India Silver Oak University Department of Computer Applications Gujarat India Sal Institute of Technology & Engineering Research Department of Computer Science Gujarat India

ISBN: (纸本)9798350354218

A XGBoost-Based System in Cervical type cancer Diagnostics are proposed in the current research work. Generally Cervical type of cancer will be a significant health concern worldwide, particularly in the regions with limited access on advanced healthcare facilities. On Early detection will plays a pivotal role on improving patient outcomes and by reducing mortality rates. In this study, the propose system termed CervicalCareXG, a novelistic system for cervical type cancer diagnostics depends on the XGBoost methodology. CervicalCareXG will utilizes a machine Learning-Driven approach to analyze patient data, also including demographic information, medical history, and cervical screening results, to Prediction the likelihood of cervical type cancer development. The XGBoost algorithm, known for its prediction accuracy and efficacy in handling complex health related datasets, is employed to train a predictive model with a capable of identifying high-risk individuals who may requires further diagnostic evaluation or preventive interventions. Through comprehensively evaluation using reality patient data, CervicalCareXG may demonstrates a promising performance in terms of sensitivity, specificity, and area under the receivers operating characteristic curve (AUC-ROC). Thus, it showcases its potential as a valuable tool for healthcare professionals on cervical type cancer screening programs. By harnessing the power of machine Learning-Driven and XGBoost, CervicalCareXG with an aim to contribute to the early detection and management of cervical type cancer, ultimately by improving patient outcomes and reducing the burden of this devastating disease. © 2024 IEEE.

关键词： Diseases

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Exploring AI Techniques for Yoga Image Classification: A Focus on Machine Learning and Deep Learning 1

Exploring AI Techniques for Yoga Image Classification: A Foc...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Duggal, Anjali Kumar, Satish Tripathi, Pooja Panjab University Department of Computer Science and Applications Chandigarh India Swami Sarvanand Giri Regional Centre Panjab University Department of Computer Science and Applications Hoshiarpur India Inderprastha Engineering College Aktu Department of Information Technology Lucknow India

ISBN: (纸本)9798331529246

Machine Learning (ML) has become essential for developing intelligent systems, providing critical Artificial Intelligence (AI) capabilities. ML involves teaching computers to solve problems by analyzing data, creating analytic models automatically, and performing associated tasks. Deep Learning (DL), a sophisticated branch of ML, utilizes neural networks to simulate AI and often surpasses traditional ML models and data analysis techniques, especially in applications like image classification. This study investigates ML and DL techniques with a particular focus on image classification applications. A novel experiment was conducted using the CNN and VGG16 model, with variations in batch size configurations to optimize yoga posture classification. The methodology involved a comprehensive review of existing approaches and experimental evaluation to assess the model's performance under different configurations. The experimental results demonstrated significant improvements in classification accuracy, achieving up to 86% in recognizing yoga postures. This represents a notable advancement in precision compared to existing methods, showcasing the effectiveness of DL models such as VGG16 in tackling complex image classification challenges. These findings underscore the potential of DL in achieving high accuracy in specialized domains like yoga posture recognition. The study provides a strong foundation for further research and application in related fields, highlighting the efficacy of advanced DL models in addressing domain-specific image classification problems. © 2025 IEEE.

关键词： Adversarial machine learning

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PrescienceMED: Leveraging Text Classification for Precise Disease Prediction 8

PreScienceMED: Leveraging Text Classification for Precise Di...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Prathwini Prathyakshini Nitte Department of Master of Computer Applications Karkala India Nitte Department of Information Science and Engineering Karkala India

ISBN: (数字)9798350350593

ISBN: (纸本)9798350350593

Emergent of new and modern technologies provide personalized solutions to health issues faced by the individuals. A chatbot using Natural language processing (NLP) is proposed which can detect relevant disease based on the user input. Different classification techniques such as Support vector Machine (SVM), Random Forest, Gaussian Naive Bayes and Decision Tree are used in detecting the relevant diseases with each disease containing 17 symptoms. Based on the symptoms the all the models can detect the diseases out of which Random Forest yields better accuracy score of 99.59%. Hence the proposed model can detect the diseases based on user queries. The study utilizes machine learning algorithms in order to automatically classify medical data based on the symptoms, reducing the reliance on manual analysis, and increasing efficiency. © 2024 IEEE.

关键词： Random forests

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Deep Learning Model-Based Region Measurement Analysis for Breast Cancer Classification 1

Deep Learning Model-Based Region Measurement Analysis for Br...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Meegada, Radhika Bhuyan, Hemanta Kumar Department of Computer Science Engineering Andhra Pradesh Guntur India Department of Information Technology and Computer Applications Andhra Pradesh Guntur India

ISBN: (纸本)9798331518943

The mortality of Breast cancer patients is issued based on lack of identification and treatment, and mammography is a useful tool for early screening. Deep learning-based computer-aided diagnosis (CAD) of mammography can help radiologists make more accurate and objective decisions. However, a lot of the current approaches rely on datasets that have been manually annotated for segmentation. Furthermore, several techniques are less interested in using ROI for image fusion models because of the high image sizes and modest lesion proportions. These flaws raise the application of the model's labour, cost, and computational overhead. Consequently, a network with regional scoring based on the deep location network (DLN) model is suggested. Utilizing pooling modules, DLN is considered a single feature extractor end-to-end mammography image classification approach that locates lesion sites without the need for training. Different deep learning models have been considered to test our proposed model. The experiments are demonstrated on publicly available IN breast and CBIS-DDSM datasets, and the proposed model fared well when compared to earlier cutting-edge techniques for classifying mammogram images. © 2024 IEEE.

关键词： Mammography

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Blockchain Breakthrough: A Review of its Role in Modern Technology 2

Blockchain Breakthrough: A Review of its Role in Modern Tech...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Asthana, Sameer Chaturvedi, Ravi Prakash Mishra, Annu Nayyar, Poorva Shaliyar, Md. Greater Noida Institute of Technology Department of Computer Science and Engineering Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350368109

This article summarizes the Blockchain technology along with the artificial intelligence. The cutting edge technology is described in a elaborated manner along with its advantages, disadvantages and its impact with the artificial intelligence. There are various sectors in which it is used and the outcomes are discussed. The proficiency of blockchain has proved its reliability and threshold in different domains. © 2024 IEEE.

关键词： artificial intelligence blockchain ledger security

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