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Automated Symptom Tracking And Prediction Of Angioedema Disease Using Machine Learning

Automated Symptom Tracking And Prediction Of Angioedema Dise...

引用

2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Arunkumar, M. Palanisamy, M. Viknesh, K. Mugilarasan, M. Abinesh, B. Ifet College Of Engineering Dept. Of Information Technology Tamilnadu Villupuram India Dhanalakshmi Srinivasan Engineering College Dept. Of Information Technology Tamilnadu Perambalur India Ifet College Of Engineering Dept. Of Information Technology Tamilnadu Villupuram India

ISBN: (纸本)9798350352689

The timely and precise identification of skin diseases is essential to optimizing therapy effectiveness and attaining favorable patient outcomes. However depending solely on visual examination and conventional diagnostic techniques adds subjectivity and might take a long time, which increases the risk of misdiagnosis or postponed treatment. This research aims to develop a machine-learning model to detect angioedema disease, a potentially fatal illness marked by sudden swelling of the skin and mucous membranes. Using several feature extraction techniques, the proposed model extracts texture, color, and intensity information from skin photographs. These extracted attributes are then integrated into a transfer learning framework to enhance overall performance, utilizing data from previously trained models. The shortcomings of traditional machine learning methods, such the need for huge datasets or protracted training cycles, are addressed by this innovative methodology The created approach has the potential to help medical practitioners make well-informed diagnoses, especially in areas with limited resources. The methodology has the potential to greatly improve patient outcomes and save healthcare costs by enabling early diagnosis and permitting proactive treatment actions. This development is especially helpful for increasing access to precise diagnoses in a variety of healthcare environments, guaranteeing that patients receive prompt and effective therapy. © 2024 IEEE.

关键词： Local binary pattern

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Optimization of Computation Offloading in Mobile-Edge Computing Networks with Deep Reinforcement Approach

Optimization of Computation Offloading in Mobile-Edge Comput...

引用

2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Hassan, Md. Tarek Hosain, Md. Kamal Rajshahi University of Engineering & Technology Dept. of Electronics & Telecommunication Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350390759

Many diverse computation-intensive mobile applications such as augmented reality (AR), virtual augmented (VR) reality, and online gaming have evolved with the widespread use of mobile devices. Nevertheless, mobile devices have difficulties in completing the necessary computation in time because of their low computational power. However, wireless powered mobile-edge computing (MEC) networks offer a promising avenue for enhancing computational capabilities, particularly in resource-constrained environments. In this paper, an optimal approach leveraging Deep Reinforcement Learning (DRL) is proposed for online computation offloading decision dynamically within such networks. The proposed method dynamically optimizes task offloading decisions in response to evolving wireless channel conditions, thus maximizing computational efficiency. This paper demonstrates the efficacy of proposed approach, achieving significant improvements in computation rates compared to existing methods via extensive simulations. This work contributes to the advancement of efficient computation offloading strategies in wireless powered MEC networks, paving the way for enhanced performance in real-world applications like heterogeneous networks e.g., smart cities, health care, autonomous vehicle, etc. © 2024 IEEE.

关键词： Computation offloading

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Binary Classification of Breast Tumours using CBAM-Enhanced Collaborative Network 3

Binary Classification of Breast Tumours using CBAM-Enhanced ...

引用

3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Das, Soumit Nivel, Mst Fabia Akter Rajshahi University of Engineering & Technology Dept. of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350388282

For women breast cancer is a life-threatening problem, making the importance of early detection crucial. Expert pathologists assess cell shapes in breast histopathology images at various magnification levels for accurate diagnosis. Unfortunately, a shortage of pathologists in many countries heightens the risk of human error in disease identification and treatment. To address this challenge, this study proposed a CBAM-Enhanced Collaborative Network for classifying benign and malignant tumors in breast cancer histopathological images, offering a potential solution to enhance accuracy and efficiency in diagnosis. The model included a transfer learning branch using a pretrained DenseNet121 structure with integrated CBAM (Convolutional Block Attention Module). Simultaneously, a collaborative branch employed Inception-ResNet-v2 with CBAM for feature extraction from histopathological images. The feature fusion module then optimized information from both branches. The proposed method achieved impressive binary classification accuracies on the BreaKHis dataset: 98.31% overall, with 98.33%, 98.08%, 99.67%, and 97.80% for magnification factors of 40x, 100x, 200x, and 400x, respectively. CBAM-Enhanced Collaborative Network also exhibits strong generalization capabilities, as evidenced by its superior performance on the ICIAR2018 BACH Challenge dataset. © 2024 IEEE.

关键词： Image classification

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Unravel News: An Efficient Summarization Approach

Unravel News: An Efficient Summarization Approach

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2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Saha, Ankan Al Shafi, Abdullah Khulna University of Engineering and Technology Dept. Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798331529765

The process of automatically creating a concise and informative summary of a news piece is known as news summarizing. This can be a useful tool for people who want to quickly get the gist of a news story without having to read the entire article. Extractive and abstractive summarization are the two basic methods used to summarize news. The process of extractive summarization entails locating and separating the key phrases from the source text. In this paper, sentence weighting is used for extractive summarization. The words in the headlines are given ten times more emphasis than the other words in the news article. The final summarization is done by rearranging the sentences as per the sequence in the main news article. The paper concludes by discussing the future of news summarization, which is an active area of research. © 2024 IEEE.

关键词： Abstractive Summary Extractive Summary ROUGE Sentence Rank Algorithm Stemming Text Rank Algorithm

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Modified D Shaped Dual-Band Microstrip Patch Antenna for 5G Ka Band Application 3

Modified D Shaped Dual-Band Microstrip Patch Antenna for 5G ...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Saha, Showrajit Sultana, Farhin Hossain, Md. Farhad Das, Debprosad Hossain, Md. Azad Chittagong University of Engineering and Technology Dept. of Electronics and Telecommunication Engineering Chattogram Bangladesh

ISBN: (纸本)9798350388282

A dual-band microstrip patch antenna is designed and discussed in this article that operates at 28 GHz and 35 GHz frequency band which aligns with the 5G Ka band application. Two resonant cavities are formed by the antenna's modified D-shaped design, which has a half-ring slot and a rectangular slot etched into a rectangular patch. The antenna's return loss is computed within a range of -46.39 dB to -28.14 dB, making it suitable for 5G applications. Impedance matching is evident at 34.64 GHz and 27.68 GHz, where the antenna's voltage standing wave ratios are 1.08 and 1.009, respectively. The suggested dual band antenna provides gains of 3.59 dBi and 6.68 dBi at 34.64 GHz and 27.68 GHz, respectively, and operates in the 5G Ka band applications, which span 26.88 GHz to 28.56 GHz and 34.10 GHz to 35.60 GHz. © 2024 IEEE.

关键词： Microstrip antennas

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An Averaging Ensemble Method for Multiclass Brain Tumor Classification Using Transfer Learning 3

An Averaging Ensemble Method for Multiclass Brain Tumor Clas...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Shahin, Shahajalal Hossain Alam, Sk. Shariful Ullah, Shah Muhammad Azmat Khulna University of Engineering & Technology Dept. of Electronics & Communication Engineering Khulna Bangladesh

ISBN: (纸本)9798331534400

A brain tumor is a disease that is caused by the abnormal growth of cells inside the brain or central spinal canal. It originates from the brain or, may spread to the brain from the other parts of the human body. While the exact causes of brain tumors remain unknown, environmental exposure and mutations in genes are the primary factors that significantly influence their progression. It is very difficult to diagnose whether the tumor is cancerous or not by analyzing the MRI images with bare eyes. For this, we need to use computer-aided diagnoses (CAD) to make the treatment easier. We have proposed an ensemble model that consists of the prediction of two deep learning models- Xception & InceptionV3 with accuracy of 97.89% and 91.56% respectively. We have used the data augmentation technique here. The proposed model gives an accuracy of 98.82%. We have also used the DenseNet121 for comparison purposes but among them, the proposed model performs better and gives high accuracy for the dataset contains about 7023 human brain MRI images which are categorized into 4 classes: glioma, meningioma, no-tumor & pituitary. The model averaging ensemble method results demonstrate that utilizing it can successfully categorize the samples that are incorrectly classified by the individual models. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Deep Learning Model for Weather Prediction 1

Deep Learning Model for Weather Prediction

引用

1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Singh, Shubhanshi Tyagi, Neha Amity School of Engineering and Technology Dept. Computer Science and Engineering Noida India

ISBN: (纸本)9798350353648

Weather forecasting predicts atmospheric conditions at a particular time and place. It is an important task with a wide range of applications, including agriculture, transportation, and disaster prevention. Traditional weather forecasting methods rely on physical models of the atmosphere, which can be complex and computationally expensive. In recent years, deep learning techniques are effective for weather forecasting. Deep learning models can learn complex patterns from data, which can be used to predict future weather conditions accurately. This project aims to create a deep-learning model for forecasting weather. A huge dataset of historical meteorological data, including radar, satellite, and surface measurements, will be used to train the model. Numerous meteorological factors, such as temperature, precipitation, and wind speed, will be predicted by the model. We will compare the deep learning model's performance with conventional weather forecasting techniques. Creating a deep learning model that is more accurate and effective than current approaches is the project's main objective. The initiative may result in improved weather forecasts that are prompt and accurate, better agricultural, transportation, and disaster prevention decision-making, and less money lost because of weather-related incidents. Possible obstacles for the project are gathering and purifying a sizable dataset of meteorological data, using this data to train a deep learning model, and then comparing the model's output with more conventional techniques. © 2024 IEEE.

关键词： Wind forecasting

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Addressing the Long-Tailed Data Distribution in Bone Marrow Cell Identification through Class Balance Deep Classification Model 2

Addressing the Long-Tailed Data Distribution in Bone Marrow ...

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2nd IEEE International Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Ghosh, Rakesh Shankar Shill, Pintu Chandra Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350317558

The accurate classification of bone marrow cells has long been a critical component of diagnosing blood diseases. However, traditional methods rely on subjective human interpretation and lack standardized quantitative criteria. To address this challenge, this research proposes a Class Balance Deep Classification Model (CBDCM) designed to classify bone marrow cells using EfficientNet to handle the long-tailed data distribution problem. In order to validate the propose model by applying it to proper dataset and analyzing the experimental outcomes of the propose method. Notably, CBDCM attains impressive precision, sensitivity, and specificity values of 86.14%, 87.53%, and 99.45%, respectively. Simulation results show that CBDCM outperforms, demonstrating superior performance in a detailed comparison with both deep neural networks and traditional machine learning techniques. The findings of this study, with significant implications for addressing class imbalance in datasets, hold promise for standardizing the classification of bone marrow cells, potentially revolutionizing the diagnosis of blood diseases. © 2024 IEEE.

关键词： bone marrow cell CBDCM data imbalance deep learning EfficientNet gaussian smoothing

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Inverter-fed Based Electrical Machine Stator Winding Moisture Condensation Detection

Inverter-fed Based Electrical Machine Stator Winding Moistur...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Laborda, Diego F. Zapico, Marcos Orviz Tiemblo, Javier G-A. Lee, Sang Bin Shaikh, Muhammad Faizan Reigosa, David University of Oviedo Dept. of Elect. Computer & System Engineering Gijón Spain Korea University Dept. of Electrical Engineering Seoul Korea Republic of

ISBN: (纸本)9798350376067

Presence of moisture in the stator windings of electrical machines increases the risk of insulation failure. Integrating humidity transducers into the system results in additional costs and fails to provide direct measurements of moisture levels within the stator windings. This paper proposes the use of the capacitive leakage currents generated by a conventional inverter commutation (i.e., square-wave pulses) whenever the machine is off to identify winding moisture condensation offline. This method helps prevent insulation failure during service without additional hardware and can be triggered automatically and remotely. It will be demonstrated that the presence of moisture condensation influences both the magnitude of the capacitive leakage current and its resonance frequency;both of them will serve as reliable indicators of moisture presence in the stator windings. © 2024 IEEE.

关键词： Stators

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Optical Character Recognition of Persian Epigraphs by Using Artificial Neural Networks in MATLAB

Optical Character Recognition of Persian Epigraphs by Using ...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Khan, Sumaiyya Bardekar, Aashish Sipna College of Engineering & Technology Dept. of Computer Science and Engineering Amravati India

ISBN: (纸本)9798331517953

Persian, also known as Farsi, was once a dominant language and cultural influence worldwide. Today, it is nearly extinct in the Indian subcontinent. However, for centuries, Farsi played a major role in high culture and administration across the subcontinent. Persian characters are complex, with various features such as ascenders, descenders, vertical lines, and curves. Automatic translation of Persian characters from inscriptions into English is currently unavailable. The process of deciphering and translating inscribed text in epigraphy can be time-consuming. This paper specifically focuses on identifying Persian epigraphs. First, the inscription image is cleaned up, and images containing Persian characters are obtained. Using preprocessing techniques, the images are then prepared for input to artificial neural networks. The artificial neural networks are trained to effectively recognize various patterns of Persian characters, as they can learn complex patterns and adapt to changes in images, resulting in high accuracy in Persian character recognition. Finally, the proposed method's performance is evaluated using accuracy, precision, and speed metrics. The results demonstrate that the method accurately and rapidly detects Persian characters in various images. This research has the potential to advance Persian character recognition and image processing, enabling broader applications such as converting image documents into text, character recognition in documents, and text translation with Persian image content. © 2024 IEEE.

关键词： MATLAB

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