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检索条件"任意字段=International Conference on Advances in Data Science and Computing Technologies, ASDC 2022"

共 6589 条记录，以下是31-40 订阅

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Optimizing Household Energy Consumption: A Web-Based Tool for Real-Time Carbon Footprint Calculation and Sustainable Benchmarking 1

Optimizing Household Energy Consumption: A Web-Based Tool fo...

引用

1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Raja, Harsh Kishore, Jayaditya Kalaivani, J. Angayarkanni, V. Rama, P. Srm Institute Of Science And Technology Faculty Of Engineering And Technology Department Of Computing Technologies Kattankulathur Chengalpattu District Tamil Nadu 603203 India

ISBN: (纸本)9798331518578

This is an innovative project from an energy-saving perspective. This project ensures that users become in control of their energy consumption and able to take an actively reducing their carbon footprints. It provides users with an interface to input details about household appliances and usage patterns, through which a correct calculation of the carbon footprint could be done. Compared against standard norms, it would help the users identify certain areas where improvement is possible and adopt more sustainable use of energy. Apart from personalized energy assessments, it has now become the central source that connects users to local schemes and initiatives on sustainable energy, including information on resources and programs into waste energy and carbon emission reduction. This integration, therefore, promotes more engagement and involvement within the community as users are nudged to join in the efforts that are being made for the goal of energy conservation. The platform educates its users about energy consumption and aids them in making green choices in data;however, with a holistic approach in carbon footprint management and education pertaining to sustainable long-term behavior, the process takes place through integrated accurate energy tracking, fine recommendations, and community-driven action. © 2025 IEEE.

关键词： Carbon sequestration

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5G to 6G Shaping and Effect of the Wireless data Transmission 1

5G to 6G Shaping and Effect of the Wireless Data Transmissio...

引用

1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Walia, Anita Das, Neeraj Department of CETT Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331518578

Digital advancement and smart production have been made possible via artificial intelligence (AI) and computing in the cloud (CC) because of the fifth-generation wireless network (5G), which is a key enabler of the fourth industrial revolution. Nevertheless, beyond 5G (B5G) is seen as a watershed moment that will radically alter current worldwide tendencies in mobile phone practices and impact the lives of many. Physical and digital worlds will eventually merge, according to B5G. The focus of this research is on the future of technology for wireless communications, specifically the evolution from 5G into 6G. Still, mankind hasn't arrived to the age of instantaneous contact with aliens, lightning-fast internet, plus zero latency, despite a number of advancements. In order to accomplish these more elevated goals, this article investigates the potential obstacles and hurdles that the shift from 5G to 6G may encounter. The article lays out the plan for 6G, the enabling infrastructures, the problems, and the research that will lead to the realization of the 'technology for humanity' goal and improved assistance for the poor. © 2025 IEEE.

关键词： 5G mobile communication systems

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Sentiment Detection in Financial News: A Deep learning Approach to Extreme and Moderate Classification 3

Sentiment Detection in Financial News: A Deep learning Appro...

引用

3rd international conference on Intelligent and Innovative technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Didolkar, Amey Lokulwar, Prasad G H Raisoni College of Engineering Computer Science and Engineering Nagpur India

ISBN: (纸本)9798331515911

Today's financial industries need precise, directed news analysis with sentiment identification more than ever in order to forecast possible future moves. This research focuses on developing a robust system for sentiment detection in financial news using a deep learning approach, enabling both extreme and moderate. The data collection has been ensured through the use of web scraping to gather data from multiple reliable financial news sources. To reliably forecast the attitudes encoded in the news items, this dataset has been processed using state-of-the-art deep learning algorithms. The latest technologies in deep learning have been employed, and a comparative analysis of different models has been conducted to identify the most effective one of this specific task. Providing an easy-to-use interface that allows users to obtain news from many sources and their corresponding sentiment analysis is the main goal of this research. With the help of this technology, stakeholders should be able to make well-informed decisions by using the sentiment trends found in financial news. By providing the effectiveness of deep learning models in sentiment identification, the system not only improves user experience by offering sentiment-based news insights but also advances the field of financial news analysis. The deep learning model that was constructed has demonstrated encouraging results in reliably classifying attitudes through extensive testing and evaluations, indicating that it has the potential to be an instrumental tool for financial analysis. For future research in the fields of sentiment identification and financial forecasting, this study establishes a precedent and emphasis the significance of incorporating cutting-edge technologies into news analysis. © 2025 IEEE.

关键词： Contrastive Learning

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Pelvic Uretero Junction Obstruction Detection Using Machine Learning Algorithm 5

Pelvic Uretero Junction Obstruction Detection Using Machine ...

引用

5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Mohan, Divya Jain, Vimanyu Dayalani, Aryan Naresh Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus Chennai India

ISBN: (纸本)9798350356236

Uretero-Pelvic Junction (UPJ) obstruction, if undetected, can lead to severe kidney dysfunction, underscoring the need for early and reliable diagnostic solutions. This research aims to develop an effective, non-invasive diagnostic tool by integrating real-time ultrasound imaging with advanced machine learning techniques. Pre-processed ultrasound images are analyzed using MobileNetV2 for feature extraction, followed by classification through a Support Vector Machine (SVM). SVM was selected for its robustness with limited training data, its ability to operate effectively in high-dimensional spaces, and its interpretability - an essential factor in medical diagnostics. By combining the strengths of MobileNetV2 and SVM, the model ensures a balance between computational efficiency and diagnostic accuracy, addressing challenges like limited datasets and the complexity of medical imaging. The purpose is to enable precise detection of UPJ obstruction while minimizing the limitations of traditional diagnostic methods. The proposed model demonstrates significant accuracy, supported by evaluation techniques such as confusion matrices and t-SNE. This approach underscores the transformative potential of combining machine learning with medical imaging, offering a pathway to enhanced patient outcomes through timely and reliable intervention. © 2025 IEEE.

关键词： Support vector machines

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Classification of nuclei in whole slide imaging based on ensemble deep learning

Classification of nuclei in whole slide imaging based on ens...

引用

international conference on Innovation and Technological advances for Sustainability, ITAS 2023

作者： Al-Thelaya, Khaled Alzubaidi, Mahmood Majeed, Fahad Ullah, Gilal Nauman Agus, Marco Schneider, Jens Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar

ISBN: (纸本)9781032803722

Recently, several innovative digital imaging technologies led to the development of several medical imaging data representations. Whole slide imaging (WSI) refers to capturing and storing tissue glass slides and biopsy samples in a digital form. This newly emerging modality is increasingly gaining huge interest in pathology departments worldwide due to the unique advantages introduced by this medical imaging modality for diagnostic, educational, and research purposes. This work provides a comparative analysis of multiple classification approaches of nuclei in WSI where multiple nuclei context-aware manipulation strategies are evaluated by our nuclei classification experiments. We propose a contextaware deep ensemble approach based on ResNet deep architecture using different forms of input images pre-processed using multiple manual feature extraction techniques. Results show that our approach attains higher accuracy performance compared to traditional context-ware strategies in the literature. © 2025 The Author(s).

关键词： Medical imaging

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Automated Regional Language News Article Classification System for Kannada and Telugu 5

Automated Regional Language News Article Classification Syst...

引用

5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Gupta, Deepa Thejaa, K. Nair, Aarathi Rajagopalan Katti, Panduranga Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350356236

The increasing amount of digital news content has created a pressing need for automated systems that can efficiently organize and categorize articles. This study focuses on developing an automated classification system for Kannada and Telugu news articles, targeting predefined categories. For Kannada, the categories include Entertainment, Sports, and Lifestyle, while for Telugu, the categories are Sports, Business, and Entertainment. With the help of the techniques available in natural language processing and the available models in machine learning, the system preprocesses the textual data in both languages to extract meaningful features for classification. Several classification algorithms are explored, with a focus on evaluating model performance using standard metrics. The results demonstrate that embeddings like KannadaSBERT and TeluguSBERT significantly improve classification accuracy, showing a clear advantage in understanding and categorizing regional language text. This work addresses a critical gap in the field of NLP for regional Indian languages by providing a structured approach to classifying Kannada and Telugu news articles. It contributes to enhancing the organization and accessibility of information for Kannada - and Telugu-speaking communities, thereby advancing research in low- resource language processing. © 2025 IEEE.

关键词： Natural language processing systems

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Smart Water Ball Launcher: An Arduino-based Fire Extinguishing System 3

Smart Water Ball Launcher: An Arduino-based Fire Extinguishi...

引用

3rd international conference on Intelligent data Communication technologies and Internet of Things, IDCIoT 2025

作者： Sridhar, Govindu L.V.N. Janakiram, M. Vishnu Ram Sai, M. Surya Narayana Reddy, G. Belwal, Meena Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The proposed work offers the production of an Arduino controlled Fire-RoboCar equipped with a water ball launcher. In a world dominated by human-machine interaction in industry, the efficiency of any task to be done is promoted by this proposed work and delivers solutions to needs like fighting fires through the provision of a productive method. At a time when increased pollution and the expense of fossil fuel are becoming very high, this alternative is environment-friendly. The Fire-RoboCar is a moving robot that can accomplish fire extinguishing tasking in an industrial complex, proving that the system may easily transport water to desired locations. This proposed work advances robotic vehicles’ capabilities and provides innovative solutions to modern industrial challenges. © 2025 IEEE.

关键词： Fire extinguishers

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Leveraging Sequential Deep Learning Models for Robust Compiler Bug Detection in Software Logs 3

Leveraging Sequential Deep Learning Models for Robust Compil...

引用

3rd international conference on Intelligent data Communication technologies and Internet of Things, IDCIoT 2025

作者： Mishra, Sahil Tiwari, Utkarsh Kavitha, C.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Compiler bugs critically impact the correctness of software applications, making their detection and resolution vital for improving software quality. This paper proposes a novel approach leveraging sequential deep learning models, including Long short-term memory, Gated recurrent unit, Recurrent neural network, and a stacked ensemble model, to enhance compiler bug identification. By combining the strengths of these architectures, the proposed method improves predictive accuracy and generalization. To ensure model interpretability, we employ LIME (Local Interpretable Model-agnostic Explanations) to identify the features influencing bug detection decisions. Experimental results demonstrate that the stacked ensemble outperforms individual models in terms of precision, recall, F1-score, and ROC-AUC. This work advances the state of compiler log analysis and contributes significantly to software quality assurance by integrating robust deep learning techniques with explainable AI. © 2025 IEEE.

关键词： Application programs

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Extending the Progression Model of Alzheimer's Disease Combining KNN, LDA and SMOTE 5

Extending the Progression Model of Alzheimer's Disease Combi...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Reddy, Tatireddy Subba Gopisetti, Sowmya Kumar, Cheviti Sanjay Prasad, Gunnala Krishna Vaishnavi, Dommati B V Raju Institute of Technology Department of Computer Science and Engineering Narsapur Telangana Medak India

ISBN: (纸本)9798350356236

Alzheimer's disease (AD) is a challenging condition that significantly impacts quality of life and leads to cognitive impairment. As the most prevalent form of dementia, AD causes severe damage to nerve cells. Individuals affected by AD struggle with cognitive functions such as memory, thinking, and behaviour, which in turn disrupts their daily activities. Current methods for creating medical concept structures from various data sources - such as ICU and outpatient clinical records - often fall short in capturing the full complexity of disease progression. The primary issues include limitations in attention weight learning and restricted handling of data sources. To address these challenges, this study introduces a novel approach that combines three key techniques: Linear Discriminant Analysis (LDA) for reducing dimensionality, the (SMOTE) Synthetic Minority Over-sampling Technique for enhancing data through augmentation and addressing class imbalance, and K-Nearest Neighbours (KNN) for classification purposes. The proposed method was empirically tested and achieved a high accuracy rate of 98.6%, demonstrating its effectiveness in representing AD progression more comprehensively. © 2025 IEEE.

关键词： k-nearest neighbors

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Smart Contract Vulnerability Detection Method Based on Edge-Deactivation Graph Convolutional Network 8th

Smart Contract Vulnerability Detection Method Based on Edge-...

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8th international conference on Information science, Communication and computing

作者： Fan Dong Cheng Jieren Tang Xiangyan Lu Kai Hainan Univ Sch Comp Sci & Technol Haikou 570228 Hainan Peoples R China Hainan Blockchain Technol Engn Res Ctr Haikou 570228 Hainan Peoples R China Hainan Univ Sch Cyberspace Secur Sch Cryptol Haikou 570228 Hainan Peoples R China Hainan Vocat Coll Polit & Law Dept Publ Safety Technol Haikou 571100 Hainan Peoples R China

ISBN: (纸本)9789819617838;9789819617814;9789819617807

In traditional graph neural neworks, node features are usually transmitted and aggregated through adjacency matrices, but the mask-based smart contract graph node feature enhancement method will cover up some node features during the transmission of the smart contract graph, resulting in loss of available information and causing the subsequent graph convolutional network to over-focus on invalid information during the learning process. In order to better preserve node feature information, this chapter introduces the concept of edge deactivation, which is to make some edge connections in the smart contract graph structure inactive according to the Bernoulli probability distribution during the message transmission process, making the information transmission between nodes more randomized and global. Experimental results show that the smart contract vulnerability detection method based on edge deactivation graph convolutional network has higher detection accuracy than existing methods.

关键词： smart contract vulnerability detection graph convolutional networks graph neural networks data augmentation contrastive learning vulnerability detection system

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