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共 564 条记录，以下是41-50 订阅

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Deciphering Brain Tumor Diagnosis: A CNN-Based Deep learning Approach 4

Deciphering Brain Tumor Diagnosis: A CNN-Based Deep Learning...

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4th IEEE international conference on Advance computing and Innovative Technologies in Engineering, ICACITE 2024

作者： Tiwari, Pradeep Kumar Johri, Prashant Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350360165

A brain tumor is defined as the rapid growth of abnormal cells in the brain. Magnetic resonance imaging (MRI) is the best way to diagnose these tumors. Using MRI images, doctors can carefully identify and define specific areas of soft tissue growth to determine the presence of brain tumors. Today, brain cancer diagnosis uses machine learning and deep learning algorithms along with advances in artificial intelligence. Using these algorithms has many benefits, such as increasing the brain's focus, reducing errors, and providing more accurate results. these benefits increase the efficiency of the decision-making process and make it easier for the patient to choose the most appropriate treatment options. this article introduces the use of convolutional neural networks (CNN) to detect brain tumors and evaluate their consequences. the main goal is to use neural networks, especially deep learning techniques, to identify brain tumors. From the results obtained in the training and testing phase, the pre-trained model achieved an accuracy of 0.9664 and a loss of 0.0934. Obtained are indicators for the classification of glioma, meningioma, non-neoplastic and pituitary tumors. Precision, recall, and F1 scores for glioma were 0.9781, 0.9477, and 0.9627, respectively. In the absence of tumor, precision, recall, and F1 scores were 0.9790, 0.9943, and 0.9866, respectively. the precision, sensitivity and harmonic mean values of the pituitary gland are 0.9589, 0.9951 and 0.9767, respectively. After analyzing the dataset, it is clear that convolutional neural networks (CNN) are the most reliable method for predicting the presence of brain tumors.s © 2024 IEEE.

关键词： Convolutional neural networks

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Dynamic Spectrum Fusion: An Adaptive learning Approach for Hybrid NOMA/OMA in Evolving Wireless Networks 4

Dynamic Spectrum Fusion: An Adaptive Learning Approach for H...

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4th international conference on Advanced Research in computing, ICARC 2024

作者： Perera, Geethaka S. Senanayake, Dimantha Y. Basnayake, Vishaka Jayakody, Dushantha N.K. School of Engineering CTR Sri Lanka Lusofona University COPELABS Portugal

ISBN: (纸本)9798350384864

this study introduces an advanced adaptive learning algorithm to optimize the choice between Non-Orthogonal Multiple Access (NOMA) and Orthogonal Multiple Access (OMA) in wireless networks. Our approach utilizes a dual-node simulation environment to evaluate the performance of a novel hybrid NOMA-OMA scheme, especially under varying conditions of user mobility. A key introduction of our research is the implementation of a versatile, data-driven scoring system, designed to maximize system performance across diverse applications. To improve the decision-making process, we incorporated a range of machine learning techniques;Random Forest, Gradient Boosting machine (GBM), and Multilayer Perceptron. this comprehensive approach, blending deep learning and traditional algorithms, is pivotal in determining the most effective Multiple Access (MA) scheme in various spatial scenarios. the integration of reinforcement learning techniques enables our system to dynamically adapt to changing conditions, facilitating real-time decision-making. the result is a dynamic and adaptive Hybrid NOMA/OMA scheme, demonstrating a significant improvement over traditional systems. © 2024 IEEE.

关键词： Wireless networks

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Enhancing Security in software-Defined Networks: A machine learning-Driven Hybrid Intrusion Detection System with Optimized Feature Selection 4th

Enhancing Security in Software-Defined Networks: A Machine L...

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4th international conference on Advanced Network Technologies and Intelligent computing-ANTIC-Annual

作者： Logeswari, G. Roselind, J. Deepika Chakraborty, Sparsh Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9783031837890;9783031837906

software Defined Networking (SDN) has emerged as a revolutionary approach to network management offering enhanced flexibility and control. However with the rise of SDN networks have become increasingly susceptible to sophisticated cyber-attacks. Intrusion Detection Systems (IDS) play a vital role in safeguarding SDN infrastructures by identifying and mitigating potential threats in real-time. To address the evolving nature of these threats the proposed IDS model integrates advanced feature selection and classification techniques combining the strengths of Information Gain (IG), Genetic Algorithm (GA) and an ensemble of classifiers to improve detection accuracy and reduce computational complexity. the proposed IDS for SDN enhances detection accuracy by combining IG and GA (IG2A) in a hybrid feature selection process. IG ranks features based on their ability to distinguish between different classes while GA optimizes the selection of feature subsets through evolutionary techniques. this integrated IG2A approach selects the most relevant features to reduce computational complexity and improve classification accuracy. Following feature selection the system employs a soft voting-based Ensemble Classification (SEC) technique. the ensemble consists of four classifiers: Random Forest (RF) k-Nearest Neighbors (KNN) Naive Bayes (NB) and Support Vector machine (SVM). By combining the outputs of these classifiers using soft voting the model enhances prediction robustness and accuracy. Experimental results show that the IG2 A-SEC model outperforms traditional feature selection methods. the IG2 A-SEC achieved 96.31% accuracy 94.62% precision 92.56% recall and 93.57% F-measure demonstrating its superior performance on the InSDN dataset.

关键词： Intrusion detection system software-defined networks information gain genetic algorithm machine learning soft-voting ensemble classification

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A Review on Cosmetic Product Recommendation Using Deep learning 4

A Review on Cosmetic Product Recommendation Using Deep Learn...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Chouhan, Ruchika Barde, Snehlata Sage University Dept. of Computer Science Engineering Indore India

ISBN: (纸本)9798331515720

Cosmetic products play an important role in every one's life. Cosmetic products also called as makeup are substance that is applied on the body especially on face. the modern lifestyle can be demanding, leaving little time for self-care. However, it's crucial to prioritize self-care, including skincare, despite hectic schedules. Indeed, relying solely on best-seller products or in-store recommendations may not always be effective in determining compatibility between a skincare product and an individual's skin condition. this is because everyone's skin tone and type of the skin is unique, with varying sensitivities, concerns, and needs. Many people buy their goods online today, using various e-commerce sites. So, to choose the best skincare product during online purchasing is difficult, since it depends entirely on skin type and usage. the main aim of this paper is to survey and explores the existing recommendation systems designed to assist users in selecting cosmetics personalized to their unique skin type, by using advanced algorithms and machine learning. this study has reviewed various methods like face recognition, cosmetic product recognition, tag recommendation, facial skin image classification using convolution neural network, a new examples-rules guided deep neural network approach. through a comprehensive review of literature and existing research, this paper examines the methodologies, algorithms, and techniques employed in cosmetic recommendation systems. Key factors such as user preferences, skin type, skin concerns, and product attributes are analysed to understand how recommendation systems use this information to generate personalized recommendations. © 2024 IEEE.

关键词： Cosmetics

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machine learning Algorithms used in Crop Yield Prediction for Precision Agriculture 4

Machine Learning Algorithms used in Crop Yield Prediction fo...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Umamaheswari, R. Shanmugapriya, S. Vimala, P. Ganesan, R. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech High Tech Dr.Rangarajan Dr. Sagunthala Engineering College Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331515720

Agriculture is a crucial sector that plays a vital role in producing food for human consumption. However, predicting crop yield accurately can be a challenging task due to various natural factors, such as soil quality, climate, rainfall, and resource availability. In the past, farmers relied on their expertise and judgment to determine crop yield, but their traditional methods have become less effective due to the unpredictable nature of these factors. To address this challenge, researchers have developed various algorithms and models for predicting crop yield based on different parameters, such as soil quality, climate, and image analysis. these algorithms can help farmers make informed decisions about when and where to grow their crops, leading to improved productivity and higher yields. Among these algorithms, neural networks are showing great promise as they can analyze vast amounts of data and identify patterns that may not be immediately apparent to human observers. By training neural networks on historical crop yield data, researchers can develop models that accurately predict crop yield based on a variety of factors. Ensuring accurate crop yield prediction is critical to ensuring food security and preventing famine, particularly in developing countries such as India. As technology continues to advance, we can expect even more advanced algorithms and models for predicting crop yield that will help farmers make better decisions and ultimately lead to increased productivity and better outcomes for agriculture. © 2024 IEEE.

关键词： Agriculture Crop yield prediction (CYP) machine learning Neural Network (NN)

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Advanced Deep learning Techniques for Accurate Detection of Pathological Infant Cry Signals 4

Advanced Deep Learning Techniques for Accurate Detection of ...

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4th international conference on soft computing for Security Applications, ICSCSA 2024

作者： Sirisha, J. Sarath Chandra, B. Madhavi, B. thirupathamma, Boggula Lakshmi Vallabhapurapu, Tejaswi Penjarla, Naveen Kumar Prasad V.Potluri Siddhartha Institute of Technology Department of IT Vijayawada India Lakireddy Bali Reddy College of Engineering Department of IT Andhra Pradesh Mylavaram India SRKR Engineering College Department of IT Andhra Pradesh Bhimavaram India Lakireddy Bali Reddy College of Engineering Department of ECE Andhra Pradesh Mylavaram India Hyderabad Institute of Technology and Management Department of ECE Telangana India

ISBN: (纸本)9798331515720

the ability to predict these signals aids in the discovery of the cause of an infant's scream. the gaussian mixture model successfully identifies the essential inspiration and expiration widening. the sensitivity, accuracy and specificity of the system were evaluated to determine its performance. the proposed system's output resulted in a balanced accuracy of 92.31 %. the proposed approach has the greatest accuracy level of 95.31 %, highest specificity level of 94.58 %, and maximum sensitivity level of 93.5 %. the results of this investigation show that the suggested approach is more effective than existing strategies at identifying cry signals. To identify the cry signal, the proposed DCNN (Dense Convolutional Neural Network) architecture is simulated with a modified activation layer. In this second module depicts this approach focuses on the region, between pathologic and normal cry signals, there are considerable variations. the goal of this research is to introduce a deep learning technique like DRnet (combined dense net and resnet) is to classify the source of newborn babies crying. the audio and speech features are extracted by using MFCC and BFCC features. All the features are merging into a feature generation matrix. the accuracy, specificity, and sensitivity of the systems are computed to estimate its performance. the proposed technique achieves the accuracy of 96.4 %, precision 94 %, sensitivity of 95 % and specificity of 93 %. From this study, the proposed technique is very efficient to detect the pathological cry signals compared to the existing machine learning or deep learning techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Association Rules Based Feature Extraction for Deep learning Classification 4th

Association Rules Based Feature Extraction for Deep Learning...

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4th international conference on soft computing and its Engineering Applications (icsoftComp)

作者： Kharsa, Ruba Al Aghbari, Zaher Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates

ISBN: (纸本)9783031276088;9783031276095

the number of extracted features from medical data, such as computer-aided diagnosis, has been known to be too large and affects the performance of the used classifiers. Moreover, the large number of input features affect the accuracy of the classifiers, such as the traditional machine learning classifier. therefore, in this paper, we proposed the use of association rules to select features from medical data, which result in dimensionality reduction of the input feature space. the selected features become the input to a deep neural network, particularly ResNet, which is known for its high accuracy of classification results. the conducted experiments prove that the use of association rules to select the most representative features and the use of deep neural networks as a classifier outperformed other traditional machine learning models in terms of accuracy of classification.

关键词： Association rules mining Feature selection Deep learning classification Resnet Neural networks Classification

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4th international conference on soft computing and its Engineering Applications, icsoftComp 2022

4th International Conference on Soft Computing and its Engin...

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4th international conference on soft computing and its Engineering Applications, icsoftComp 2022

ISBN: (纸本)9783031276088

the proceedings contain 36 papers. the special focus in this conference is on soft computing and its Engineering Applications. the topics include: Explainable AI for Predictive Analytics on Employee Attrition;graph Convolutional Neural Networks for Nuclei Segmentation from Histopathology Images;Performance Analysis of Cache Memory in CPU;extraction of Single Chemical Structure from Handwritten Complex Chemical Structure with Junction Point Based Segmentation;automatic Mapping of Deciduous and Evergreen Forest by Using machine learning and Satellite Imagery;RIN: Towards a Semantic Rigorous Interpretable Artificial Immune System for Intrusion Detection;SVRCI: An Approach for Semantically Driven Video Recommendation Incorporating Collective Intelligence;deep learning Based Model for Fundus Retinal Image Classification;one True Pairing: Evaluating Effective Language Pairings for Fake News Detection Employing Zero-Shot Cross-Lingual Transfer;reliable Network-Packet Binary Classification;semKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-source Aggregation and Recommendation of News with a Focus on Personalization;extraction and Analysis of Speech Emotion Features Using Hybrid Punjabi Audio Dataset;human Activity Recognition in Videos Using Deep learning;Brain Tumor Classification Using VGG-16 and MobileNetV2 Deep learning Techniques on Magnetic Resonance Images (MRI);five-Year Life Expectancy Prediction of Prostate Cancer Patients Using machine learning Algorithms;an Ensemble MultiLabel Classifier for Intra-Cranial Haemorrhage Detection from Large, Heterogeneous and Imbalanced Database;A Method for Workflow Segmentation and Action Prediction from Video Data - AR Content;convolutional Neural Network Approach for Iris Segmentation;reinforcement learning Algorithms for Effective Resource Management in Cloud computing;FedCLUS: Federated Clustering from Distributed Homogeneous Data;corpus Building for Hate Speech Detection of Gujarati Language;a software

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Classification of Sarcoma Based on Genomic Data Using machine learning Models 4

Classification of Sarcoma Based on Genomic Data Using Machin...

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4th international conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Gala, Pratham Pandloskar, Yash Godbole, Shubham Hakim, Fayed Kanani, Pratik Kurup, Lakshmi Dwarkadas J. Sanghvi College of Engineering Mumbai India Dept. of Artificial Intelligence and Data Science Dwarkadas J. Sanghvi College of Engineering Mumbai India

the proposed work provides a new machine-learnt classification approach for the various types of soft tissue sarcoma based on genomics data which addresses a considerable gap in sarcoma diagnostics. the previous studies have investigated various aspects of sarcoma but this study is unique in that it targets the predicting sarcoma variant types using genetic information, which has not been done before. Random Forest was used as the meta-estimator and a stacking ensemble model comprising of Random Forest, Extreme Gradient Boosting and LightGBM were used for this study. the model which was trained and validated on a complete dataset of 206 adult soft tissue sarcoma samples containing genomic alterations, transcriptomic, epigenomic and proteomic data achieved an accuracy of 89.44% at a precision level as high as 91%. Stratified k-fold cross validation is employed to ensure that class imbalance is not a hindrance to performance. this innovative approach outmatches single classifiers and traditional single model methods at great length hence making it possible and effective to use machine learning on genomic data for predicting sarcoma variants. thus, the findings from this research could change cancer diagnosis forever;they promise more accurate classification as well as personalized treatment modalities while also providing a framework for analogous applications in other rare complex cancers. © 2025 the Authors.

关键词： Adversarial machine learning

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Plant Disease Detection Using machine learning Approaches: A Review 4th

Plant Disease Detection Using Machine Learning Approaches: A...

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4th international conference on Data, Engineering, and Applications, IDEA 2022

作者： Saraf, Puja Dipak Patil, Jayantrao Bhaurao Department of Computer Engineering R. C. Patel Institute of Technology Shirpur425405 India

ISBN: (纸本)9789819700363

Agriculture plays an important role in the country’s development, and it is the key source of food for the population of the world. But in developing countries like India, Pakistan and Nepal, the condition of the agriculture sector is getting worse day by day. there are many reasons behind it, but one of the reasons is the degeneration of crops by pests and disease. Agriculture solely depends on the biological health of the seeds, crops, information on weather conditions soils and biotic features liable for the crop diseases. Robust diagnostic methods are accomplished by dispensation and scrutinizing large quantities of records near get additional consistent material and ample more accurate predictions remain vital. Deep learning, image processing, soft computing and data mining are likely to play a vital role in recognizing plant diseases. this paper aims to review existing studies and investigations using the recent practices to solve a variety of agricultural problems. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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