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作者： Gul, Recep Stotz, David Jafar, Syed Ali Bolcskei, Helmut Shamai Shitz, Shlomo Chair for Mathematical Information Science Eth Zürich Zürich8092 Switzerland Kantonsschule Schaffhausen Schaffhausen8200 Switzerland Department of Electrical Engineering and Computer Science University of California at Irvine IrvineCA92697 United States Department of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa3200003 Israel

We present a condition for 1/2 degree of freedom for each user in constant K-user single-antenna interference channels. This condition is sufficient for all and necessary for almost all channel matrices. Moreover, it applies to all channel topologies, i.e., to fully-connected channels as well as channels that have individual links absent, reflected by corresponding zeros in the channel matrix. Moreover, it captures the essence of interference alignment by virtue of being expressed in terms of a generic injectivity condition that guarantees separability of signal and interference. Finally, we provide codebook constructions achieving 1/2 degree of freedom for each user for all channel matrices satisfying the condition we identified. © 1963-2012 IEEE.

关键词： Degrees of freedom (mechanics)

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Applications of Artificial Intelligence in Biomedical Image Processing 2

Applications of Artificial Intelligence in Biomedical Image ...

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Rajeyyagari, Sivaram Hung, Bui Thanh Chakrabarti, Prasun Shaqra University College of Computing and Information Technology Department of Computer Science Shaqra Saudi Arabia Thu Dau Mot University Data Analytics and Artificial Intelligence Laboratory Engineering Technology Faculty Thu Dau Mot Viet Nam Provost and Institute Endowed Distinguished Senior Chair Professor Techno India Njr Institute of Technology Rajasthan India

ISBN: (纸本)9781665400527

The biomedical applications of nanoparticles are growing in recent years. The uses range from diagnostic to therapeutic purposes, and researchers are constantly introducing new ways. Biosensors, miniaturization techniques, surface enhancement in implantable biomaterials, and better nanofibers in medical textiles are some of the contributions of biomedical nanotechnology. Therapeutic (medical) imaging successfully maps the anatomy of the human body. Image enhancement, analysis, categorization, and image based representation are all possible areas of computer processing and analysis of medical images. Therapeutic image understanding uses computer vision, applied mathematics, signal analysis, and artificial intelligence to recognise objects in medical images. Image processing is the use of computer algorithms to manipulate digital images. Hand-held transducers emit and receive pulsed acoustic waves for medical ultrasound imaging. In this study, a computer-aided diagnosis method is developed to help cytotechnicians locate and classify aberrant cells in cervical cytology images. With the outcomes of morphological and statistical edge based segmentation are compared to traditional segmentation techniques. The proposed segmentation methodology outperforms the other methods. This proposed technique's segmentation accuracy is 98%. © 2022 IEEE.

关键词： Artificial intelligence

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Intelligent Hybrid Heat Management System: Overcoming Challenges and Improving Efficiency

Intelligent Hybrid Heat Management System: Overcoming Challe...

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Annual IEEE Systems Conference

作者： Vira Shendryk Andriy Perekrest Yuliia Parfenenko Reza Malekian Olha Boiko Paul Davidsson Department of Computer Science and Media Technology Internet of Things and People Research Centre Malmö University Malmö Sweden Department of Information Technologies Sumy State University Sumy Ukraine Department of Computation Engineering and Electronics Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Department of Computer Science and Media Technology Malmö University Internet of Things and People Research Centre Malmö University Malmo Sweden

ISBN: (数字)9798350358803

ISBN: (纸本)9798350358810

The research delves into intelligent hybrid heat management Systems, exploring the challenges faced and solutions for enhancing efficiency. Hybrid heating systems are complex cyber-technical systems that combine city heating networks with renewable energy sources, such as heat pumps and solar panels. Traditional heating systems often lack adaptability to internal and external conditions, leading to suboptimal performance and user expectations. This paper proposes a new approach by integrating smart technologies, the Internet of Things, Artificial Intelligence, Machine Learning, optimization techniques, and trade-offs into the management of hybrid heat systems. The emphasis is also placed on the fact that the introduction of smart technologies makes it possible to make hybrid heating systems human-oriented and meet individual needs. Energy efficiency improvement is achievable by combining solutions, such as actual forecasting, with intelligent management that adapts to changing climates and user behaviors. The challenges addressed include inadequate responsiveness to load changes, inaccurate heat consumption forecasting, and inefficient data management. The paper emphasizes the need for intelligent systems that comply with the current standards, providing cost optimization, socializing and ensuring resilience, customer orientation, reliability, safety, and trustworthiness. This exploration of intelligent hybrid heat management systems seeks to overcome existing challenges and pave the way for a sustainable, digitally optimized future in district heating systems.

关键词： Heating systems Energy consumption Renewable energy sources Heat pumps Machine learning Internet of Things Intelligent agents

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Sensor System for Real-time Water Quality Monitoring

Sensor System for Real-time Water Quality Monitoring

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Proceedings of the International Convention MIPRO

作者： Elena Simonoska Dijana Capeska Bogatinoska Ile Dimitrievski Reza Malekian University of Information Science and Technology “St. Paul the Apostle” Ohrid N. Macedonia Department of Computer Science and Media Technology Malmö University Malmö Sweden Department of Electrical Electronic and Computer Engineering University of Pretoria Pretoria South Africa

Water pollution is a global issue that has an impact on the entire ecosystems’ life cycles. Traditional sampling and laboratory testing techniques are labor-intensive and error-prone, making them ineffective for quickly detecting changes in water quality. This paper presents the development of a low-cost, portable and efficient prototype sensor-based system for monitoring water quality in real-time. The system consists of a microcontroller, temperature, turbidity, pH, and distance sensors, and an application for a visual representation of the data. Extensive testing was carried out to ensure uninterrupted operation. The prototype is a user-friendly sensor system that can be positioned close to the target area in order to assist in preventing environmental and biological harm. This can ensure safe, healthy, and sustainable water supplies for the communities, environment, and the economy. Continuous monitoring of water parameters can also help avoid critical situations. The experimental results demonstrate a successful development of a smart water quality monitoring system with potential applications in various scenarios.

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One-Dimensional EEG Artifact Removal Network Based on Convolutional Neural Networks

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第1期9卷 142-159页

作者： Xiong, Jun Meng, Xiang-Long Chen, Zhao-Qi Wang, Chuan-Sheng Zhang, Fu-Quan Grau, Antoni Chen, Yang Huang, Jing-Wei School of Computer and Data Science Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China College of Electronic Engineering Shandong University of Science and Technology No. 579 Qianwangang Road Huangdao District Qingdao China College of Computer and Big Data Fuzhou University Fuzhou University Town No. 2 Wulongjiang North Road Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Autonomous Region of Catalonia Barcelona Spain Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou University Town No. 200 Xiyuangong Road Fuzhou China Fujian Province University No. 1 Campus New Village Longjiang Street Fuqing China School of Mechanical and Automotive Engineering Fujian University of Technology No. 33 Xuefu South Road University New District Fuzhou China

The electroencephalogram (EEG) serves as a significant tool in the realms of clinical medicine, cerebral investigation, and neurological disorders research. However, the EEG records we obtain are often easily contaminated by various artifacts, which can blur or distort the underlying EEG signals and make data interpretation difficult. Generally speaking, removing EEG artifacts is considered an essential step in brain signal analysis. Therefore, removing artifacts is crucial for obtaining accurate and reliable EEG signals for subsequent analysis. Recently, deep learning techniques have found widespread application across various domains for denoising tasks, including image denoising and EEG denoising. Many advanced algorithms have been developed in image denoising, which has achieved good results in enhancing low-quality images. Moreover, it has shown superior performance in EEG denoising. In contrast, few people have devoted themselves to studying EEG denoising, and existing convolutional neural network EEG denoising methods still have problems of overfitting and poor denoising effect in Electromyograph(EMG) and ElectroOculoGram(EOG) artifact removal. Therefore, this paper proposes a method called DWINet (De-artifacting with Image-based Network for EEG Signals) based on an image dehazing network DRHNet for removing artifacts from EEG signals. Specifically, our approach DWINet, addresses the de-artifacting issue in EEG signals by converting it as an image dehazing problem and utilizes the image dehazing capability of DRHNet to enhance the denoising performance of EEG signals. Experimental results demonstrate that the proposed method outperforms the compared algorithms in removing the ocular artifact in EEG signals and exhibits higher accuracy and robustness. © 2024, J. Network Intell. All rights reserved.

关键词： Image enhancement

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Brain Tumor Classification System Using Decision Tree Algorithm

Brain Tumor Classification System Using Decision Tree Algori...

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Cybernetics and Innovations (ICCI), International Conference on

作者： Sattarpoom Thaiparnit Thanakrit Janchidfah Adil Farooq Dep. Of Information System and Computer Business Faculty of Business Administration Rajamangala University of Technology Suvarnabhumi Suphan Buri Thailand dep. Of Digital Media Technology Muban Chombueng Rajabhat University Ratchaburi Thailand Department of Electronic Engineering Maynooth University Ireland

ISBN: (数字)9798331533267

ISBN: (纸本)9798331533274

This research aims to study, design, and develop a brain tumor classification system using artificial intelligence, specifically decision tree algorithms. The system's primary objective is to assist medical personnel in making decisions, as distinguishing between two types of brain tumors-benign and malignant (cancerous)-can be challenging, especially for less experienced medical staff. Due to the similarities between these two types of tumors, it may be difficult for medical practitioners with limited expertise to differentiate between them in a timely manner. The proposed brain tumor classification system is designed to address this challenge. The research team conducted the study over approximately seven months, starting from the second semester of the 2023 academic year through the first semester of the 2024 academic year. Experimental results revealed that among the tree-based algorithms, Random Forest achieved the highest accuracy, followed by M5P. The best average accuracy from the experiments was obtained using a 70% training and 30% testing split, yielding an accuracy of 96.83%. Therefore, the research team selected the 70% training and 30% testing split for model development, as it provided the highest accuracy compared to the 60% training and 40% testing split, and the 80% training and 20% testing split, which showed lower accuracy. The results demonstrate that the 70% training and 30% testing approach is optimal for the brain tumor classification system, ensuring high accuracy in classifying brain tumors.

关键词： Training Technological innovation Accuracy Machine learning algorithms Brain tumors Brain modeling Classification algorithms Decision trees Testing Cancer

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Bibliometric Research: Tracking How the Artificial Intelligence Inntegration Changed the Medical Systems

Bibliometric Research: Tracking How the Artificial Intellige...

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Sustainable Computing and Integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Jasneet Kaur S. Barath Kuamr Pradnya Mehta Shalini Ninoria Vishal Sharma C. Karthikeyan Symbiosis college of Nursing University and symbiosis international deemed University pune India Department of Biotechnology Karpagam Academy of Higher Education Coimbatore Computer Engineering Vishwakarma Institute of Information Technology Pune CCSIT TMU School of Media Film&Television Studies IIMT University Meerut Department of Computer Science Engineering Karpagam Institute of Technology Coimbatore

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

In latest generations, there has been a significant increase in study interest in the growing applications of artificial intelligence in health and medicine. The purpose of this study is to present a worldwide and chronological overview of AI study in the areas of medical care and health. The online science tool was used to obtain a total number of articles that were released between. The detailed study looked at the number of publications, as well as the cooperation between writers and nations. Generally, indicate that a factor, along with robotics, algorithms, neural networks, artificial cognitive computing, and natural language processing, were identified through a vast network of researchers' key words and phrases and message review of pertinent academic papers. These methods are regularly used in clinical forecasting and rehabilitation. The most articles were on cancer, followed by those on cardiovascular disease, stroke, blindness, Early onset dementia, and sadness. Additionally, the lack of study on applying AI to some illnesses with a high disease prevalence indicates potential paths for Ai development. The study proposes the creation of international and national guidelines and laws on the rationale and application of pertaining to medical products and provides a first and complete image of the global efforts made in this significant and lucrative study area.

关键词： Training Industries Machine learning algorithms Databases Reviews Medical services Natural language processing Artificial intelligence Robots Biomedical imaging

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Speech Enhancement Generative Adversarial Network Architecture with Gated Linear Units and Dual-Path Transformers

Speech Enhancement Generative Adversarial Network Architectu...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Zhang, Dehui Dong, Anming Yu, Jiguo Cao, Yi Zhang, Chuanting Zhou, You Qilu University of Technology Shandong Academy of Sciences School of Computer Science and Technology Jinan China Jinan China University of Jinan School of Information Science and Engineering Jinan China University of Bristol Department of Electrical and Electronic Engineering Bristol United Kingdom Shandong HiCon New Media Institute Co. Ltd. Jinan China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Generative Adversarial Networks (GANs) have been used in the field of speech enhancement due to their huge potentials in reducing the noise mixed in the signals. Most of existing GAN-based speech enhancement approaches either operate on time domain or exploit the magnitude spectra in time-frequency domain, but lack consideration of direct optimization of the phase. In this paper, we propose a GAN architecture for speech enhancement based on gated linear units (GLUs) and Dual-Path Transformers (DPTs), which simultaneously deals with the amplitude and phase information on the time-frequency domain. The generator of the proposed GAN architecture is designed following an autoencoder structure fed by the real and imaginary parts of the time-frequency frames. The encoder of the generator is constructed by multiple cascaded convolutional GLUs (ConvGLUs), while the decoder consists of two groups of cascaded deconvolutional GLUs (DeconvGLUs), one for the real part of the spectrogram and the other for the imaginary part. The GLUs are adopted since they are potential in avoiding the gradient vanishing issue dwelling in deep architectures by providing a linear path for the gradients while retaining non-linear capabilities. Aiming at capturing the long-range dependent features in speech, we place DPTs between the encoder and the decoder of the generator, which contains multi-head attention modules and Bi-directional Gated Recurrent Units (BiGRUs). Moreover, the DPT structure is also merged with multiple one-dimensional convolutional layers in the discriminator of the GAN. Such a design not only improves the speech enhancement performance of GAN by focusing on multiple features of speech, but also reducing the volume of model parameters of GAN. Experimental results suggest that the proposed GAN architecture outperforms the existing benchmark GANs in terms of both objective speech intelligibility and quality with less computational complexity. © 2022 IEEE.

关键词： Generative adversarial networks

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Use of Different Machine Learning Algorithms for Hate Speech Detection

Use of Different Machine Learning Algorithms for Hate Speech...

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2022 International Conference on Cyber Resilience, ICCR 2022

作者： Munir, Muhammad Sohail Parveen, Kausar Farooq, Umer Shaalan, Khaled Abualkishik, Abedallah Zaid Mohammed, Abdul Salam National College of Business Administration University Computer Science Department Lahore Pakistan Lahore Garrison University Department of Computer Science Lahore Pakistan Faculty of Engineering and It The British University in Dubai United Arab Emirates Department Chair of Information Technology Management United Arab Emirates Skyline University Sharjah United Arab Emirates

ISBN: (纸本)9781665461221

With the speedy advancement of the web, a consistently expanding number of people that utilize online social media. Subsequently, hate speech becomes uncontrolled in social media, and it is critical to group the hate speech and control it before it spread. With the presentation and the advancement of deep learning, hate speech recognition becomes practice. Many examinations use information from social platforms, for example, Twitter and Facebook along with machine learning or deep learning advances to identify and perceive hate speech. In any case, there are insufficient surveys about this area. After studying various article's and research papers, no such review is available to see assortment of feature extraction/engineering methods (FEM) and ML-Algorithms that assess, which feature extraction/engineering procedure and ML-Algorithms can perform better on open source or openly accessible dataset. Thus, the purpose of this research paper is to look at 3-FET or strategies and 8-ML-Algorithums to assess their performance on an openly accessible dataset and this dataset has 3 classes. The research outcomes exhibited that SVM-Algorithm with BIGRAM features performed better with almost 80% accuracy. This research paper shows viable ramifications what's more, can be used as a gauge concentrate on to identifying hate speech communications/messages. Also, the result of various correlations will be utilized as condition of- workmanship strategies to look at future explores for existing mechanized text classification procedures. © 2022 IEEE.

关键词： Decision trees

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Social media Emotion Analysis in Indonesian Using Fine-Tuning BERT Model 3

Social Media Emotion Analysis in Indonesian Using Fine-Tunin...

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3rd East Indonesia Conference on computer and information technology, EIConCIT 2021

作者： Setiawan, Esther Irawati Kristianto, Lukas Hermawan, Arya Tandy Santoso, Joan Fujisawa, Kimiya Purnomo, Mauridhi Hery Institut Sains Dan Teknologi Terpadu Department of Information Technology Surabaya Indonesia Institut Sains Dan Teknologi Terpadu Department of Information Technology Surabaya Indonesia Tokyo University of Technology School of Media Science Tokyo Japan Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9781665405140

Social media is the leading platform were users' express opinions and emotions. Emotion Analysis aims to identify emotions: happy, sad, angry, fear, disgust, shame, and guilt. InaMoodMeter framework takes the unstructured status of a Facebook user. It processes it to extract emotion from teenage users for self-Assessment and to observe emotion related to online customer satisfaction. We also created an Indonesian Dataset for Emotion Analysis from Facebook the dataset can be utilized as a valuable benchmark for emotion classification in Indonesian. With this dataset, many state-of-The-Art approaches are evaluated. We also experimented on the ISEAR Indonesian translated dataset. This research consists of two stages: obtaining training data to build the dataset and performing classification. Our proposed method can achieve the highest accuracy of 79% with the Fine-Tuning Bert Model. © 2021 IEEE.

关键词： Classification (of information)

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