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Enhancing the Coexistence of 802.11 WLAN and 5G Networks using a Multi-Task Learning with Convolutional Neural Network Model 2

Enhancing the Coexistence of 802.11 WLAN and 5G Networks usi...

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2nd International Symposium on Information Technology and Digital Innovation, ISITDI 2024

作者： Putra, Dody Ichwana Ekariani, Shelvi Universitas Andalas Dept. of Computer Engineering Padang Indonesia Universitas Andalas Dept. of Mathematics and Data Science Padang Indonesia

ISBN: (纸本)9798350389241

The increasing need for high-speed internet for wireless devices has driven the development of 5G New Radio (NR) technology. The 5G NR operates in various spectrums such as sub-1 GHz, sub-6 GHz, and millimeter wave (mmWave) in the 26-28 GHz range. Conversely, the new standard Wi-Fi 6E operates on the unlicensed band and currently utilizes 2.4 GHz, 5 GHz, and 6 GHz. The coexistence of 5G NR sub-6 GHz and Wi-Fi 6E networks can result in interference and reduced performance for both technologies. To address this challenge, we introduce a Multi-Task Learning (MTL) approach using Convolutional Neural Networks (CNN) to improve the coexistence of 5G and Wi-Fi 6E networks. This proposes identifying and classifying packets transmitted and its modulation over both types of networks. The MTL-CNN model is trained to classify packets based on their type (e.g., 5G or Wi-Fi) and to identify the specific protocol used within each packet. We confirm the accuracy of this method with Wi-Fi and 5G signals produced by the MATLAB waveform simulator and subsequently deploy it on a hardware testbed using SDR. Our experimental results show that this method improves the accuracy of packet identification and classification, thus enhancing the coexistence between 5G and Wi-Fi 6E networks. MTL-CNN potentially leads to more efficient use of the wireless spectrum and improves the overall performance of wireless communication systems. © 2024 IEEE.

关键词： Wi-Fi

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Oral Cancer Using Deep Learning and Auto-Fluorescence Image Analysis

Oral Cancer Using Deep Learning and Auto-Fluorescence Image ...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Muhammed Yaseer, P. Arul Xavier, V.M. Shyni, S.S. Karunya Institute Of Technology And Sciences Division Of Computer Science And Engineering Tamilnadu Coimbatore India Karunya Institute Of Technology And Sciences Division Of Data Science And Cyber Security Tamilnadu Coimbatore India

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371567

Poor treatment outcomes result from the fact that oral cancer is frequently identified at an advanced stage, making it a serious and sometimes lethal disease. Thus, it is essential to develop quick and accurate procedures for detecting oral cancer in its early stages. In this work, we present a unique non-invasive detection technique for evaluating auto-fluorescence signals in oral tissues utilizing convolutional neural networks ResNet50 architecture. Biological tissues naturally produce auto-fluorescence signals, and variations in these signals may be a marker of the existence of malignant cells. Our approach seeks to precisely identify malignant and normal oral tissues by capturing these minute variations in auto-fluorescence patterns. Using a sizable dataset of pictures of oral tissue, we trained and evaluated our RestNet50 model, and the results showed impressive accuracy rates of 89%. The better performance of our detection method can be attributed to the effective capture of high-level features made possible by the ResNet50 architecture. Furthermore, the suggested approach demonstrated quick processing speeds, which allowed it to be used for real-time clinical applications. This approach may greatly enhance patient prognosis and treatment results by identifying oral cancer in its early stages with high accuracy of 89% when compared to VGG16 and VGG19. Although more validation research is necessary before the suggested approach can be used in clinical settings, it shows promise in facilitating early diagnosis and intervention to enhance patient outcomes. © 2024 IEEE.

关键词： Fluorescence

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Detection of PCOS Leveraging Machine Learning and Interpretation Using Explainable AI 1

Detection of PCOS Leveraging Machine Learning and Interpreta...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Madhavi, Bindu V. Firdouse, Asfiya Kodipalli, Ashwini Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798331518943

Polycystic Ovary Syndrome (PCOS) is a widespread endocrine disorder impacting women globally. This research aims to early predict and detect PCOS which is needed to reduce long-term complications. Since it is considered to be a hard-to-diagnose disorder, machine learning is utilized for this purpose. This study leveraged the power of computational algorithms trained on patient data for model prediction where Decision tree with criterion Gini index outperformed with 88.07% accuracy, 85.29% precision, 78.37% recall and 81.69% F1 Score. In addition to this, Bagging and boosting algorithms were used to monitor their performance metrics where Gradient Boost stood out with a remarkable accuracy of 91.74%, 91.00% precision, 97.00% recall and 94.00% F1 Score. By optimizing the chosen parameters through Hyperparameter tuning, a notable increase in the model's accuracy was observed. Besides, three ensemble models were proposed out of which the ensemble classifier ensemble model involving bagging, boosting and single classifiers brought a significant difference of 95.41% accuracy. Additionally, Explainable (XAI) methodologies like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) were used for model interpretability. © 2024 IEEE.

关键词： Adversarial machine learning

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Crop for Est - Crop Forecasting and Estimation. Crop Yield Estimation and Profitability Analysis for Precision Agriculture

Crop for Est - Crop Forecasting and Estimation. Crop Yield E...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Shanmugasundaram, C. Umamaheswari, C. Vijayalakshmi, A. Varghese, Prabha Elizabeth Manakula Vinayagar Institute of Technology Department of Electrical and Electronics Engineering Puducherry India St. Thomas College of Arts and Science Department of Computer Science with Data Science Tamil Nadu India Sri Manakula Vinayagar Engineering College Department of Biomedical Engineering Puducherry India

ISBN: (纸本)9798331510022

The agriculture industry significantly contributes to the nation's economy by employing a large segment of the population and enhancing the GDP. It is a primary source of livelihood for many people, particularly in rural areas. India's agricultural sector is known for its diversity, producing a wide range of crops including rice, wheat pulses, sugarcane, cotton, fruits, and vegetables. This industry faces a significant challenge in predicting crop yield production and crop demand accurately. Efforts have been made to address these challenges through various government initiatives such as subsidies and continued investment in agricultural research and development. The traditional methods of predicting crop yield and market demand is cumbersome and time consuming and is susceptible to errors, resulting in inefficient resource utilization and revenue losses. There is a need for predicting the most efficient crop based on various influential environmental markers and forecasting the profitability. This project aims to develop an intelligent platform that uses machine learning algorithms to estimate the crop for cultivation in each area based on environmental factors. The project also aims to forecast market demand and pricing trends for the identified crop with the aim of increasing the profitability for the farmers and other stakeholders in the agriculture industry. © 2024 IEEE.

关键词： Agricultural economics

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Deep Learning based Intelligent System for Skin Disease Detection 10

Deep Learning based Intelligent System for Skin Disease Dete...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Dhanush, K.B. Arul Xavier, V.M. Shyni, S.S. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Tamilnadu Coimbatore India Karunya Institute of Technology and Sciences Division of Data Science and Cyber Security Tamilnadu Coimbatore India

ISBN: (纸本)9798350353068

Malignant and benign skin diseases significantly impact public health and personal well-being. Traditional machine learning models encounter difficulties in processing image datasets, which is crucial for early and accurate diagnosis. This work employed two pre-trained deep learning models, ResNet50 and VGG19, known for their precise and swift image processing capabilities. These models underwent training utilizing an extensive dataset of images from patients battling benign keratosis, Melanoma, and Melanocytic nevus (Mole). The models were then tasked to distinguish between these three conditions. The results were remarkable, with the models achieving accuracies of 91.3 % and 95.6 %, respectively. A comparative analysis revealed that the VGG19 model outperformed in accurately identifying these three diseases, surpassing traditional AI and machine learning methods. The trained VGG19 model was subsequently incorporated into an Android application to enhance user experience. © 2024 IEEE.

关键词： Diagnosis

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Enhancing Detection of Breast Cancer Through a Novel Framework: A Machine Learning Approach with Explainable AI Interpretation 1

Enhancing Detection of Breast Cancer Through a Novel Framewo...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Vidyashree, R. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798331518943

Breast cancer, the most prevalent cancer among women, significantly contributes to increased mortality rates and ranks as the second leading cause of cancer-related deaths in women. Early detection is crucial for effective treatment, yet traditional diagnostic approaches often involve multiple laboratory tests supervised by medical experts. To mitigate human error and expedite breast cancer detection, there is a pressing need for automated systems capable of accurate and timely diagnosis. This study aims to investigate the application of machine learning (ML) techniques in classifying breast cancer using features from digitized images of fine-needle aspiration (FNA) breast mass samples. Machine learning methods are instrumental in addressing this imperative. The study conducts a comparative analysis of various ML classification methods, including Logistic Regression, Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Trees, Bagging-Boosting, and ensemble models, using a breast cancer dataset to establish a novel framework. The performance of each algorithm is assessed based on metrics such as accuracy, precision, recall, and F1 score. The findings underscore the effectiveness of ensemble classifier models, achieving a notable accuracy rate of 98.87%. Additionally, the integration of explainable Artificial Intelligence enhances the interpretability of the models by providing valuable insights into their decision-making processes. © 2024 IEEE.

关键词： Contrastive Learning

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Text Classification for Social Governance: A Novel Strategy with Adaptive Reward Mechanisms 6

Text Classification for Social Governance: A Novel Strategy ...

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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

作者： Kong, Xu Zhu, Xiaomin Wang, Maoli Wang, Xinming Zou, Min School of Computer Science Qufu Normal University Rizhao China Shandong Institute of Big Data Jinan China School of Cyber Science and Engineering Qufu Normal University Qufu China

ISBN: (纸本)9798350353174

Given the increasing need for societal governance systems to effectively address public grievances, classifying texts from public service hotlines and other governance communications has become challenging, especially for texts with complex hierarchical structures. This study explores how adaptive reward mechanisms can be applied to classify complaint texts in societal governance. We propose a novel hierarchical text classification model that overcomes the limitations of traditional methods in handling hierarchical data. We designed a hierarchical loss function that considers inter-label hierarchical relationships to enhance the model's learning of hierarchical information. Additionally, we used a simple label encoding method to reduce computational complexity and improve training efficiency. Experimental results show that our model significantly outperforms existing methods in classification accuracy, both flat and hierarchical. Furthermore, ablation studies confirmed the hierarchical loss function's crucial role in enhancing model performance. This research offers an effective strategy for classifying complex hierarchical data, significantly improving classification tasks in natural language processing. © 2024 IEEE.

关键词： Adversarial machine learning

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Regularized Adaptive Momentum Dual Averaging with an Efficient Inexact Subproblem Solver for Training Structured Neural Network 38

Regularized Adaptive Momentum Dual Averaging with an Efficie...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Huang, Zih-Syuan Lee, Ching-Pei Department of Computer Science and Information Engineering National Taiwan University Taipei106 Taiwan Department of Advanced Data Science Institute of Statistical Mathematics Tachikawa Tokyo190-8562 Japan

We propose a Regularized Adaptive Momentum Dual Averaging (RAMDA) algorithm for training structured neural networks. Similar to existing regularized adaptive methods, the subproblem for computing the update direction of RAMDA involves a nonsmooth regularizer and a diagonal preconditioner, and therefore does not possess a closed-form solution in general. We thus also carefully devise an implementable inexactness condition that retains convergence guarantees similar to the exact versions, and propose a companion efficient solver for the subproblems of both RAMDA and existing methods to make them practically feasible. We leverage the theory of manifold identification in variational analysis to show that, even in the presence of such inexactness, the iterates of RAMDA attain the ideal structure induced by the regularizer at the stationary point of asymptotic convergence. This structure is locally optimal near the point of convergence, so RAMDA is guaranteed to obtain the best structure possible among all methods converging to the same point, making it the first regularized adaptive method outputting models that possess outstanding predictive performance while being (locally) optimally structured. Extensive numerical experiments in large-scale modern computer vision, language modeling, and speech tasks show that the proposed RAMDA is efficient and consistently outperforms state of the art for training structured neural network. Implementation of our algorithm is available at https://***/ismoptgroup/RAMDA/. © 2024 Neural information processing systems foundation. All rights reserved.

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Masking of Colposcopy Images- A New Approach

Masking of Colposcopy Images- A New Approach

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Chatterjee, Priyadarshini Das, Surajit Siddiqui, Shadab B V Raju Institute Of Technology Department Of Computer Science And Engineering Medak Telengana Narsapur India B V Raju Institute Of Technology Department Of Artificial Intelligence And Data Science Medak Telengana Narsapur India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering Hyderabad India

ISBN: (纸本)9798350371567

Masking, in the context of image processing and computer vision, refers to the process of isolating specific regions or objects of interest within an image while suppressing or removing irrelevant information. It involves the creation of a mask, which is essentially a binary image that defines which pixels in the original image belong to the region of interest and which do not. The mask typically consists of black and white pixels, where white pixels represent the areas to be preserved or highlighted, and black pixels represent the areas to be ignored or masked out. This paper presents a novel approach aimed at enhancing the accuracy of masking algorithms for colposcopy images, a crucial component in the diagnosis of cervical cancer among women. While previous research has demonstrated promising outcomes in this domain, our study introduces a novel masking algorithm tailored specifically to improve segmentation accuracy. The colposcopy dataset utilized in this research is sourced from the International Agency for Research on Cancer in France. © 2024 IEEE.

关键词： Diseases

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Defending Against IoT Threats: A Comprehensive Framework with Advanced Models and Real-Time Threat Intelligence for DDoS Detection 2

Defending Against IoT Threats: A Comprehensive Framework wit...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Jyothsna, B. Jyothsna, V. Mohan Babu University Department of Computer Science and Engineering Sree Sainath Nagar Tirupati India Mohan Babu University Department of Data Science Sree Sainath Nagar Tirupati India

ISBN: (纸本)9798350365269

The incorporation of Software Defined Networking (SDN) with IoT emerges as an auspicious strategy for enhancing security and access control mechanisms. Nevertheless, substantial threats from DDoS attacks persist in IoT networks, with the potential for execution over botnet or zombie attack, incorporating a machine learning-based detection scheme enables establishing it a framework have been introduced to analyze the performance of IoT devices. Profiles that inform decision-making processes are amassed by these frameworks, ultimately protecting the security of the IoT devices. This work presents a machine learning-based approach for detecting DDoS attacks in an SDN-WISE IoT controller. The incorporation of a machine learning-based detection scheme enables the establishment of a testbed environment for simulating the traffic of DDoS attacks. A logging mechanism within the SDN-WISE controller detects the traffic, generating network logs that are pre-processed and transformed into a dataset. The machine learning DDoS detection module entrenched in the SDN-WISE controller uses Naive Bayes (NB), Decision Tree (DT), and Support Vector Machine (SVM) algorithms to categorize SDN-IoT data. The proposed framework's performance is assessed across various traffic simulation scenarios, and the results from the machine learning DDoS detection module are analyzed. The proposed framework exhibits an accuracy rate of 98.2%, 97.1%, and 97.18% for NB, SVM, and DT, respectively. The attack detection module consumes up to 36% of memory and CPU usage, saving approximately 64% of memory while maintaining up to 71% CPU availability to analyze SD-IoT network traffic. The proposed framework achieves an accuracy of 98.2% which proves the efficiency of the work over state of the work models. © 2024 IEEE.

关键词： Support vector machines

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