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A Low-Light-Level Image Enhancement Algorithm Combining Retinex and Transformer

A Low-Light-Level Image Enhancement Algorithm Combining Reti...

引用

2024 International Conference on Remote Sensing, Mapping, and Image Processing, RSMIP 2024

作者： Zhao, Xinpu Li, Liang Chongqing University of Technology College of Computer Science and Engineering Chongqing Banan China

ISBN: (纸本)9781510680012

The Low Light Image Enhancement (LLIE) task aims to restore images with poor lighting conditions and visual effects to images with good lighting conditions and visual effects. However, the enhancement results output by existing methods always include various image degradation such as overexposure, artifacts, and color shift. To alleviate these issues, a low light image enhancement method based on Retinex theory and Transformer is proposed. Designed a dual branch network architecture with Transformer blocks. One branch is used to enhance local details of the image, while the other branch is used to adjust the color and brightness of the enhancement results. The dataset used in the experiment was the LOL dataset, which was 23.42 and 0.81 in the PSNR and SSIM indicators, respectively. Compared with the suboptimal algorithm, it improved by 1.06 and 0.016, respectively. The experimental results show that this method has achieved excellent performance in low light image enhancement tasks and has certain application value. © 2024 SPIE.

关键词： Image enhancement

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Voice Over Vision: A Sequence-to-Sequence Model by Text to Speech technology 1

Voice Over Vision: A Sequence-to-Sequence Model by Text to S...

引用

1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Satish, E.G. Naidu, P. Ramesh Mogera, Girish Madhava Karthik, H.V. Abhinandan Nitte Meenakshi Institute of Technology Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350376517

Text-to-speech (TTS) turns written text into spoken words using artificial voices. This uses natural language processing (NLP) and speech synthesis to make audio from text input. TTS has many uses - for people with visual impairments, hands-free communication, voiceovers for multimedia content. The development of neural network-based models like WaveNet and Tacotron has made synthetic speech much more natural and expressive. This paper reviews the history of TTS, the key components and techniques of modern TTS and future directions including emotion and personalization in synthetic speech. We also explore the ethical considerations and societal impacts of TTS and why we need to be responsible with its development and deployment. © 2024 IEEE.

关键词： Natural language processing systems

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Bridging Perspectives: Enhancing Trustworthy AI Through Transparency, Reliability, and Safety

Bridging Perspectives: Enhancing Trustworthy AI Through Tran...

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2024 IEEE International Conference on Industrial engineering and engineering Management, IEEM 2024

作者： Jonelid, Gustav Karl, Rikard Alkhaled, Lama Mokayed, Hamam Department of Computer Science Electrical and Space Engineering Lulea University of Technology Sweden

ISBN: (纸本)9798350386097

When developing and implementing ethical Artificial Intelligence (AI) in industrial settings, various viewpoints on building trustworthy AI emerge. This research emphasizes these differences and provides suggestions to close these divides. It goes beyond conventional ethical dilemmas, such as the trolley problem, to tackle intricate issues surrounding trustworthy and ethical AI. We identify three core principles of trustworthy AI: transparency, reliability, and safety. Transparency involves clear, open communication about AI decision-making processes, which fosters trust among stakeholders. Reliability ensures consistent, dependable performance under various conditions, essential for critical operations. Safety focuses on preventing harm to humans, the environment, and infrastructure, requiring robust safeguards and adherence to safety standards. By prioritizing these pillars, the research provides practical recommendations for developing AI systems that balance technological advancement with ethical principles, enhancing user trust and ensuring responsible AI integration across industries. © 2024 IEEE.

关键词： decision making ethical AI Reliability Safety Transparency Trustworthy AI

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Review of Deep Learning-Based Entity Alignment Methods 18th

Review of Deep Learning-Based Entity Alignment Methods

引用

18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Lu, Dan Han, Guoyu Zhao, Yingnan Han, Qilong College of Computer Science and Technology Harbin Engineering University Harbin150009 China

ISBN: (纸本)9789819998920

Entity alignment aims to discover different references to the same entity in different graphs, and it is a key technique for solving graph-related problems. It has developed into one of the important tasks in knowledge graphs and has received extensive attention from scholars in recent years. Through entity alignment, data from multiple isolated knowledge graphs with different sources and modes can be summarized and classified, forming a more information-rich knowledge base. In early research on entity alignment, researchers first proposed a class of alignment methods based on knowledge representation learning and verified that these methods have significant improvements over traditional methods. However, entity alignment still has many defects and challenges to be addressed, such as a lack of scalability, differences in language and relationship type definitions in knowledge graphs from different sources, which make entity alignment difficult. There are also problems such as the need to improve the quality of large-scale knowledge graph data and optimize computational efficiency. Thus, it is difficult to perform entity alignment on multiple knowledge graphs through simple translation and transformation. This paper discusses the deep learning-based methods that have emerged in the field of entity alignment based on the definition of entity alignment and data sets as standards, summarizes the shortcomings and limitations of these methods, and introduces commonly used data sets in the entity alignment task. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Alignment

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Parkinson's Disease Prediction Using Machine Learning Techniques

Parkinson's Disease Prediction Using Machine Learning Techni...

引用

2024 IEEE International Conference on Information technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Harry, M. Vijula, V. Princess, J. Pearly Karunya Institute of Technology and Sciences Dept of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350382693

Parkinson's disease, a progressive neurological disorder, presents a spectrum of symptoms primarily affecting motor functions such as tremors and coordination challenges. Beyond the physical symptoms, it encroaches upon cognitive and emotional domains, often leading to depression and cognitive decline. A recent breakthrough in diagnosis involves a voice-based model that detects subtle speech irregularities linked to Parkinson's. This innovative approach relies on a dataset comprising 24 columns capturing diverse symptoms, from motor impairments to non-motor indicators. This dataset serves as a crucial foundation for training and validating the model's accuracy. By analyzing individual symptom profiles, this multidimensional approach facilitates early detection and tailored intervention, essential for effectively managing the disease's progression. Integrating advanced technology with comprehensive clinical data represents a significant step forward in Parkinson's disease management, promising personalized care and improved outcomes for patients by addressing their unique needs and challenges. Furthermore, the integration of voice analysis adds a non-invasive dimension to Parkinson's diagnosis, potentially improving accessibility to screening methods. This approach holds promise for reaching underserved populations who may face barriers to traditional diagnostic procedures. By enhancing early detection and intervention, it offers the possibility of mitigating the disease's impact and improving overall patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Privacy Preserving Techniques in Data science : A Review

Privacy Preserving Techniques in Data Science : A Review

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Panchal, Yuvraj Sheikh, Rashid Sethi, Kamal Kumar Bhanodia, Praveen Kumar Rajput, Shiv Shankar Rathore, Narendra Pal Singh Acropolis Institute of Technology and Research Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350388800

In the field of data science, privacy-preserving methods are essential to guaranteeing the moral and secure use of data. This extensive overview study offers an in-depth examination of privacy-preserving approaches, including federated learning, homomorphic encryption, secure multi-party computation (SMPC), anonymization strategies, and differential privacy. This paper explains the theoretical underpinnings, real-world applications, difficulties, and future possibilities of privacy-preserving strategies in data science. The study explores anonymization strategies such generalization, suppression, k-anonymity, l-diversity, and t-closeness after providing an outline of privacy risks in data science. The concepts and uses of federated learning, homomorphic encryption, SMPC, and differential privacy are then examined, emphasizing how these techniques protect sensitive data and promote teamwork in analysis. Examples from the fields of healthcare, banking, telecommunications, social media, and government are shown to show how useful privacy-preserving techniques are in the real world. We hope to give scholars, practitioners, and policymakers a thorough knowledge of privacy-preserving strategies and their implications for ethical and long-term data-driven decision-making through this extensive review. © 2024 IEEE.

关键词： Anonymity

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Comparative Analysis of Convolutional Neural Networks for Brain Tumor Detection: A Study of VGG16, ResNet, Inception, and DenseNet Models 3

Comparative Analysis of Convolutional Neural Networks for Br...

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Mukesh Kumar, C. Sree Sankar, J. Karunya Institute of Technology and Sciences Department of Computer Science Engineering Coimbatore India

ISBN: (纸本)9798350375190

This research work evaluates VGG16 against ResNet, Inception, and DenseNet models in a comparative analysis of convolutional neural network (CNN) architectures for brain tumour identification. The research study explores the effectiveness of these cutting-edge CNN architectures in precisely diagnosing brain tumours by utilising a collection of medical photographs. It is shown by thorough testing and analysis that VGG16 performs better in terms of classification accuracy than ResNet, Inception, and DenseNet models. The evaluation metrics encompassing accuracy, precision, recall, and F1-score highlight the superior performance of VGG16, showcasing its robustness and effectiveness in detecting brain tumors from medical images. Moreover, a comprehensive analysis of the computational complexity and training time associated with each model is conducted, providing insights into their practical viability in real-world applications. These findings contribute to the advancement of medical image analysis and provide valuable guidance for researchers and practitioners in selecting suitable CNN architectures for brain tumor detection tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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An Overview of Predictive Analysis for Air Quality Measurements and Visualization 1

An Overview of Predictive Analysis for Air Quality Measureme...

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1st IEEE International Conference on Computing and Data science, ICCDS 2024

作者： Revathi, A.R. Arockia Agash, A. School of Computer Science and Engineering Vellore Institute of Technology - Chennai Chennai India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365337

Air quality is closely connected to the well-being of humans, ecosystems, and wildlife, making its continuous monitoring and preservation as a prime importance. Various causes contribute to air pollution, exerting a notable influence on the degradation of air quality. Recent research employs machine learning, AI, data analysis, Internet of things and microcontroller-based devices to construct resilient models for monitoring and collect the data for predicting air quality. The incorporation of diverse datasets enhances their capacity to provide well-informed predictions and judgments. This overview study systematically abstracts the significance of Air Quality Index (AQI) and India Meteorological Department (IMD) data, synthesizing contemporary study in the realm of air pollution. It emphasizes on an array of data sources, advanced monitoring methodologies, and the deployment of forecasting models, particularly highlighting the integration of Machine Learning (ML) techniques for effective AQI measurement. Furthermore, the paper presents an in-depth examination of the interdependencies among various parameters post data preprocessing. It features a detailed analysis of the interplay between various air pollutants and meteorological factors post data preprocessing. Moreover, this study introduces visualization techniques to effectively present the complex dynamics of air pollutants and weather conditions, offering a comprehensive perspective on air quality determinants. By aligning with Sustainable Development Goals, it advocates for public health and clean energy, aiming to contribute to sustainable urban development and mitigate air pollution challenges. © 2024 IEEE.

关键词： Air quality

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Latency-Aware Cell Deletion in Link Scheduling for 6TiSCH Networks 16

Latency-Aware Cell Deletion in Link Scheduling for 6TiSCH Ne...

引用

16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Tapadar, Karnish N A Khatua, Manas Tamarapalli, Venkatesh Indian Institute of Technology Guwahati Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350383119

The 6TiSCH standard uses IEEE 802.15.4 Time Slotted Channel Hopping (TSCH) as a MAC layer protocol. TSCH cell scheduling determines packet transmission schedules in terms of timing and channel allocation. The Minimal Scheduling Function (MSF) and the 6P protocol facilitate schedule management dynamically. MSF involves a decision-making process to add/delete one cell at a time from the current schedule to adapt the schedule to modified traffic rate. The cell deletion is accomplished by randomly selecting one of the scheduled cells between two communicating nodes. However, random cell deletion can elevate end-to-end latency and energy consumption. Therefore, this paper proposes a Latency-aware Cell Deletion (LCD) mechanism to minimize associated average end-to-end latency and energy consumption. Simulations using the 6TiSCH simulator verified that in an 80-node network, compared to the standard MSF and another benchmark scheme IMSF, LCD achieved a substantial 44.40% and 42.39% reduction in end-to-end latency, respectively. © 2024 IEEE.

关键词： 6TiSCH Network Adaptive Link Scheduling Link Scheduling Minimal Scheduling Function

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Abusive Content Detection using Deep Learning: A BERT-Based Approach 16

Abusive Content Detection using Deep Learning: A BERT-Based ...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Pahujani, Ria Bhuyan, Sakshi Rai, Satvick Kaliyar, Rohit Kumar School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798331505264

The rise of social media platforms on the internet has made combating hate speech and other abusive content more challenging. In this work, we investigate the effectiveness of deep learning techniques for abusive content detection. In particular, we applied the modern learning model BERT. In order to accurately identify abusive language in a variety of textual data types, such as social media posts and online comments, our research focuses on optimizing pre-trained BERT models. We demonstrate, via rigorous testing, that the BERT-based strategy could substantially improve the efficiency of classification with respect to conventional machine learning methods. In conclusion, this research contributes to ongoing efforts to prevent cyberbullying and foster positive interactions with others online. By leveraging cutting edge methods like BERT, we aim to equip online platforms with powerful tools for abusive behavior detection and remediation. The study's findings provide important novel data into the practical applications of deep learning for abuse content detection, which in turn opens up new avenues for future improvements in online safety measures. © 2024 IEEE.

关键词： Adversarial machine learning

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