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Review on TIRADS Scoring Based Thyroid Nodule Malignancy Detection 9

Review on TIRADS Scoring Based Thyroid Nodule Malignancy Det...

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9th International Conference on Smart Computing and Communications, ICSCC 2023

作者： James, Jisha Anand, H.S. Dept. of Computer Science and Engineering Kochi India Muthoot Institute of Technology and Science Kochi India Cvv Institute of Science and Technology Dept. of Computer Science Ernakulam India

ISBN: (纸本)9798350314090

Thyroid nodules are a common clinical finding, and their accurate risk stratification is essential for determining appropriate management strategies. The Thyroid Imaging Reporting and Data System (TIRADS) has emerged as a valuable tool for evaluating thyroid nodules by integrating various ultrasound features to predict malignancy. This research article aims to assess the efficacy of TIRADS scoring in detecting thyroid nodule malignancy and its potential as a standardized diagnostic tool. A comprehensive literature review was conducted to identify studies that evaluated the diagnostic performance of TIRADS scoring systems in different patient populations. The included studies demonstrated that TIRADS scoring systems exhibited substantial accuracy, sensitivity, and specificity in predicting thyroid nodule malignancy. The incorporation of ultrasound features, such as nodule size, composition, echogenicity, margins, and presence of calcifications, allowed for a standardized and reproducible risk stratification. TIRADS scoring systems also demonstrated the ability to optimize the decision-making process for further diagnostic workup, reducing unnecessary fine-needle aspiration biopsies and surgical interventions. The article also discusses the limitations and challenges associated with TIRADS scoring. © 2023 IEEE.

关键词： Machine learning

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Deep Learning Approaches for Feature Extraction in Big Data Analytics 10

Deep Learning Approaches for Feature Extraction in Big Data ...

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10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Dhaygude, Amol Dattatray Varma, Raj A. Yerpude, Poonam Swarnkar, Suman Kumar Kumar Jindal, Rupesh Rabbi, Fazle University of Washington Department of Data Science Seattle United States Maharashtra Pune India Indian Institute of Technology Dept of Computer Science & Engineering Bhilai India Shri Shankaracharya Institute of Professional Management and Technology Dept of Computer Science and Engineering Raipur India National Institute of Design Dept of Computer Science & Engineering Ahmedabad India Australian Computer Society Australia

ISBN: (纸本)9798350382471

In the context of big data analytics, this study examines the use of algorithms based on deep learning for feature extraction. Traditional methods usually have trouble sifting through the complexity and volume of data to find the important elements. We investigate the application of auto encoders, transformer-based models, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) to address this problem. Our comprehensive review of existing literature compares these deep learning techniques with traditional methods and highlights their adaptability to large-scale datasets. The efficacy and precision of these methodologies are demonstrated by empirical investigations conducted on authentic datasets across a range of disciplines, including but not limited to time-series analysis, picture identification, and natural language processing. Despite challenges like computational requirements and model interpretability, our findings indicate that deep learning-based feature extraction holds significant promise for enhancing big data analytics, leading to valuable insights and discoveries in various fields. © 2023 IEEE.

关键词： Autoencoders Big Data Analytics CNN) Deep Learning Efficiency Feature Extraction Image Recognition Natural Language Processing RNNs Scalability Time-Series Analysis Real-World Datasets Transformer Models

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Evaluating Machine Learning Methods for Bangla Text Emotion Analysis

Evaluating Machine Learning Methods for Bangla Text Emotion ...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Akter, Tahmina Akter, Mst. Sharmin Mahmud, Tanjim Islam, Dilshad Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh Chattogram Veterinary and Animal Sciences University Dept. of Physical and Mathematical Sciences Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350361537

Text-based emotion identification goes beyond simple sentiment analysis by capturing emotions in a more nuanced way, akin to shades of gray rather than just positive or negative sentiments. This paper details our experiments with emotion analysis on Bangla text. We collected a corpus of user comments from various social media groups discussing socioeconomic and political topics to identify six emotions: sadness, disgust, surprise, fear, anger, and joy. We evaluated the performance of four widely used machine learning algorithms - RF, DT, k-NN, and SVM - alongside three popular deep learning algorithms - CNNs, LSTM, and Transformer Learning - using TF-IDF feature extraction and word embedding techniques. The results showed that among the machine learning algorithms, DT, RF, k-NN, and SVM achieved accuracy scores of 82%, 84%, 73%, and 83%, respectively. In contrast, the deep learning models CNN and LSTM both achieved higher performance with an accuracy of 85% and 86% respectively. These findings highlight the effectiveness of traditional ML and DL approaches in detecting emotions from Bangla social media texts, indicating significant potential for further advancements in this area. © 2024 IEEE.

关键词： Contrastive Learning

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Alpha-x Framework for Assessing Network Robustness in Nakagami-m Fading and Co-Channel Interference Environments 3

Alpha-x Framework for Assessing Network Robustness in Nakaga...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Upadhyay, Deepak Siwach, Karan Dhondiyal, Shiv Ashish Upadhyay, Abhay Gupta, Mridul Sharma, Kunj Bihari Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India G.L. Bajaj Institute of Technology and Management Dept. of Computer Science and Engineering Greater Noida India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798331518592

This article explores how the Alpha-x framework can improve network robustness under Nakagami-m fading and co-channel interference (CI), The study evaluates network performance using key metrics such as outage probability, spectral efficiency, and capacity degradation. The simulations show a 50% reduction in outage probability, a 20% boost in spectral efficiency, and a 33.3% lower capacity degradation compared to traditional models. These results demonstrate how the Alpha-x framework enhances network performance by adapting to various fading conditions and interference levels in wireless communication systems. The framework not only improves reliability and efficiency but also shows practical relevance in network planning and management. This can potentially lead to better user experiences and increased operational resilience. Future research could focus on optimizing the underlying algorithms and testing their performance on a larger scale in real-world environments. As telecommunications systems evolve, frameworks like Alpha-x could play a crucial role in ensuring reliable and efficient wireless networks. © 2024 IEEE.

关键词： Cochannel interference

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Hyper Triglycerides Prognosis Using Machine Learning and Data Science

Hyper Triglycerides Prognosis Using Machine Learning and Dat...

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2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Sunitha, T. Sankaradass, Veeramalai Anna University P B College of Engineering Dept. Computer Science and Engineering Chennai India Chennai Institute of Technology Dept. Computer Science and Engineering Tamil Nadu Chennai600069 India

ISBN: (纸本)9798350348910

This study investigates the prognostic potential of machine learning and data science in evaluating hypertriglyceridemia, a condition linked to various health risks. Triglycerides, a substantial component of body fat derived from dietary intake, are intricately associated with these risks. The research explores the use of data science methodologies, especially machine learning algorithms, for precise prognoses. The integration of artificial neural networks and diverse data sources contributes to advancing predictive models, aiming to enhance understanding and improve early detection and preventive interventions for hypertriglyceridemia. This study also emphasizes the role of triglycerides in the body, originating from dietary sources such as butter and oils. Excess calories, not immediately utilized, transform into stored triglycerides, released by the body for energy distribution through VLDL cholesterol particles. Elevated triglyceride levels correlate with increased risks of heart disease, coronary artery disease (CAD), stroke, and chronic conditions affecting the liver and kidneys. The rapid progress in data science, driven by diverse data sources, positions the field among the fastest-evolving. Integration with machine learning technologies significantly improves healthcare solutions, particularly in disease prediction through artificial neural networks. These advancements have a substantial impact on addressing heart disease (4.4 million annual deaths according to the WHF dossier) and cholesterol-related fatalities (estimated at nearly 2.6 million by the WHO). © 2023 IEEE.

关键词： Cholesterol

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Artificial Intelligence-Driven Power Management System for Enhanced Efficiency in Smart Grids

Artificial Intelligence-Driven Power Management System for E...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Deepak, B.L. Sabin, T.T. Darshana, A. Vindya, L. Suma Manjunath, N. Mallikarjunaswamy, S. Shilpa, M. Rekha, V. Sjc Institute of Technology Dept of Information Science and Engineering Chickballapur India Sjc Institute of Technology Dept of Ai & Ds Chickballapur India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Christ University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350388602

Smart grids are crucial in modern energy systems, integrating sensors, communication networks, and control mechanisms to improve energy distribution. However, conventional methods like Time-Based Scheduling (TBS), Demand Response Mechanism (DRM), and Static Load Distribution (SLD) struggle with inefficiencies such as poor real-time load balancing, limited adaptability, and inaccurate power usage predictions. These drawbacks hinder the overall effectiveness of smart grid *** address these challenges, the proposed AI-Driven Power Management System (AI-PMS) leverages machine learning to optimize energy distribution and predict demand more accurately. By using historical and real-time data, AI-PMS dynamically adjusts power flow, ensuring better load distribution and efficiency. This method surpasses the limitations of TBS, DRM, and SLD. The implementation of AI-PMS has resulted in a 0.25% improvement in energy efficiency and a 0.15% boost in grid reliability, significantly reducing power losses. The system also enhances the integration of renewable energy sources, making it a more sustainable and adaptive solution for modern smart grids. These advancements promise better power management and lower operational costs for grid operators. © 2024 IEEE.

关键词： Smart power grids

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HAC-Bchain: A Secure and Scalable Blockchain-Shard Based E-Voting System

HAC-Bchain: A Secure and Scalable Blockchain-Shard Based E-V...

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2023 IEEE technology and engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Joni, Sohel Ahmed Rahat, Rabiul Tasnin, Nishat Ghose, Partho Gaur, Loveleen Bangladesh University of Business and Technology Dept of Computer Science and Engineering Bangladesh Taylorâs University School of Computer Science Malaysia

ISBN: (纸本)9798350384659

Voting is a fundamental right of citizens in a democratic country and crucial for any thriving democracy. Reliable voting systems are essential for free and fair elections in the modern era. Biometric Electronic Voting Machines (EVMs) address many issues with paper-ballot systems, but their closed-source nature undermines voter trust. Traditional election systems are also vulnerable to cyberattacks. In this paper, we propose a hybrid blockchain-based electronic voting system (HAC-Bchain) to address the limitations of conventional e-voting systems and ensure a secure, auditable, tamper-proof, transparent, and privacy-preserving voting process. A scripting system for our blockchain facilitates a limited set of predefined operations for each layer, which helps authoritative figures to manage the election securely. We also combine a category-based sharding mechanism with the HAC-Bchain hybrid approach to create more data-concentrated shards, which improves scalability, performance, and data availability. Furthermore, we compare and discuss the performance and efficiency of different sharding configurations. Our experiments shed new light on the overall security, performance, and scalability of blockchain-based evoting systems. © 2023 IEEE.

关键词： Blockchain

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An Algorithmic Approach for Text Summarization 2

An Algorithmic Approach for Text Summarization

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2nd International Conference for Advancement in technology, ICONAT 2023

作者： Joshi, Amogh More, Prathamesh Shah, Soumya Sahitya, Ms. Aarti K. J. Somaiya Institute of Engineering and Information Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9781665475174

With the advent of technology and increase in ease of access to digital devices, there has been a burgeoning increase in the flow and creation of information all across the internet. However, this has led to a lot of saturation and an increased amount of redundant information, which needs to be sorted through manually many times to find the important or pertinent information. But this ends up becoming a tiring and laborious task for many people. Hence, text summarizers, which are softwares that, when given a particular text input, summarize it effectively and output only the important parts in the text, while discarding the unimportant or redundant bits. A number of Text summarizers currently exist on the internet, for free and for a cost as well. Many of them are efficiently able to extract the important information and text out of the given input. However most of them are a single type of text summariser titled 'Extractive Text Summarisers', which, while fairly accurate, is based on a model that merely extracts the important texts from the given input as it is, without specifically phrasing it in any other way, or without semantic understanding of the text itself. This leads to some inaccuracies in the summarized texts, such as some irrelevant words being put in the summarized output, even though they are unimportant, or some important sentences or phrases missing out on being in the output, as they have not been discussed enough in the input. That is why, the proposed system of Query based Summarizer not only works on queries, but is also aimed to be an Abstractive Text Summarizer, which will use semantic and syntactic understanding of the given input to summarize it and deliver a clear, concise and accurate output. © 2023 IEEE.

关键词： Semantics

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Narrow Stock Trends using Machine Learning Techniques 3

Narrow Stock Trends using Machine Learning Techniques

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Prashanthi, Bh. Vamshi, B. Naga Kumar, D. Ajith Surya Vinay, G.S.S. Reddy, P. Narasimha Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Hyderabad India GRIET Dept of CSE Hyderabad India

ISBN: (纸本)9798350343632

For many years, the prediction of stock market prices has captivated the attention of researchers and academics. The unpredictability of stock prices stems from their susceptibility to myriad political and economic events, leadership shifts, investor sentiments, among other factors. Solely relying on historical data or textual records has proven inadequate in anticipating stock price movements. Past sentiment analysis studies have underscored a notable correlation between stock price fluctuations and few publication belongs to news articles. Sentiment analysis has been explored using numerous methodologies, such as deep learning, naive Bayes deterioration, and support vector machines. The efficacy of deep learning prototypes is contingent according to volume of exercise records delivered. A shortcoming of past research was the limited textual data examined, leading to less-than-optimal forecasts. In our research, we leverage deep learning techniques to amass an extensive amount of time series data and cross-reference it with pertinent news stories to enhance the precision of stock price forecasts. Over a span of five years, we gathered daily stock prices for S&P500 firms and scrutinized over 265,000 financial bulletin pieces of entities. Assumed the enormity of our dataset, we harness the power of cloud computing to craft predictive models and draw real-time insights for specific stocks. © 2023 IEEE.

关键词： Bigdata and cloud Machine Learning prediction Stock estimation

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Convolutional-Neural Network Bases Speaker Emotion Detection Based on Human-computer Interaction 1

Convolutional-Neural Network Bases Speaker Emotion Detection...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Nithya, B. Saranya, R. Joseph, Mary Josphineleela, R. Thirunagalingam, Arunkumar Kodali, Arun Kumar B.S. Abdur Rahman Crescent Institute of Science and Technology Dept of Computer Science and Engineering Chennai India V.S.B. Engineering College Department of Computer Science and Engineering Karur India St. Joseph's Institute of Technology OMR Department of IT Chennai119 India Panimalar Engineering College Department of Computer Science and Engineering India Mckesson Corporation United States Mphasis Corporation

ISBN: (纸本)9798331518578

The present investigation proposes a Convolutional Neural Network (CNN)-based method for human-computer interaction (HCI) speaker emotion identification. Optimising the user experience through clearer and sympathetic connections is made possible by emotional knowledge. Conventional techniques for detecting emotions depend on manually created characteristics, which are frequently laborious and less resilient to speech variability. By using CNNs' automatic feature extraction capabilities, the suggested system analyses speech signal spectrograms and extracts crucial emotional indicators. The method obtains notable gains in emotion classification accuracy by retraining the system on a varied collection of emotional speech samples. Important emotional states like happiness, anger, sadness, and neutrality are all successfully distinguished using the CNN model. This study demonstrates how deep learning can help develop emotion-sensitive HCI systems and promote more organic and contextually aware human-machine interactions. Applications in customer service, healthcare, and virtual assistants can benefit from the technique's adaptability and versatility. © 2025 IEEE.

关键词： Convolutional neural networks

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