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An AI-Driven Intelligent Traffic Management Model for 6G Cloud Radio Access Networks

IEEE Wireless Communications Letters 2023年第6期12卷 1056-1060页

作者： Swain, Smruti Rekha Saxena, Deepika Kumar, Jatinder Singh, Ashutosh Kumar Lee, Chung-Nan National Institute of Technology Department of Computer Applications Kurukshetra136119 India Goethe University Frankfurt Department of Computer Science Frankfurt60323 Germany National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan

This letter proposes a novel Cloud Radio Access Network (C-RAN) traffic analysis and management model that estimates probable RAN traffic congestion and mitigate its effect by adopting a suitable handling mechanism. A computation approach is introduced to classify heterogeneous RAN traffic into distinct traffic states based on bandwidth consumption and execution time of various job requests. Further, a cloud-based traffic management is employed to schedule and allocate resources among user job requests according to the associated traffic states to minimize latency and maximize bandwidth utilization. The experimental evaluation and comparison of the proposed model with state-of-the-art methods reveal that it is effective in minimizing the worse effect of traffic congestion and improves bandwidth utilization and reduces job execution latency up to 17.07% and 18%, respectively. © 2012 IEEE.

关键词： Bandwidth

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Image Forgery Detection Using Deep Learning 7th

Image Forgery Detection Using Deep Learning

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7th International Conference on Smart Trends in Computing and Communications, SmartCom 2023

作者： Koshy, Litty Chithu, K. Department of Computer Science and Engineering SCMS School of Engineering & Technology Ernakulam India Master of Computer Applications SCMS School of Engineering & Technology Ernakulam India

ISBN: (纸本)9789819908370

Since cameras are so widely available, taking pictures has become more and more common. In order to gain more information, it is frequently necessary to enhance photographs, which are crucial in our daily lives as memories or sources of wealth of information. There are many tools accessible to enhance the quality of photos;however, some of them are also widely used to alter images, leading to the dissemination of false information. This makes picture forgeries more severe and frequent, which is now a major cause of concern. To identify fake images, many conventional methods have been developed over time. Convolutional neural networks (CNNs) have drawn a lot of interest recently, and they have had an impact on the area of picture fraud detection as well. However, the majority of CNN-based picture forgery detection methods currently used in the literature are restricted to identifying a certain kind of fraud (either image splicing or copymove). As a result, a method that can quickly and precisely identify any hidden forgeries in a picture is needed. We present a powerful deep learning-based approach for detecting picture forgeries in this study. The RGB format images converted to error level analysis (ELA) are used to train our model. The suggested model is compact, and its effectiveness shows that it outperforms cutting-edge methods in terms of speed. The experiment's findings are promising, with a 91.37% total validation accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Convolutional neural networks

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Addressing Food Waste: Survey of Approaches, Technological Innovations and Sustainable Solutions 11

Addressing Food Waste: Survey of Approaches, Technological I...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Nandeesh, Talari Rakhra, Manik Chowdary, Pullela Harish Amanpreetsingh Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Lovely Professional University Department of Computer Applications Punjab Phagwara India

ISBN: (纸本)9798350350357

Food waste management in our local is to high for solution. This review article critically examines the complex issue of food waste, analyzing root causes and examining environmental and social impacts. The essay delves into the issue of waste across various phases of the food supply system, specifically concentrating on production, processing, distribution, retail, and consumption stages. Environmental impacts, particularly greenhouse gas emissions and resource scarcity, are scrutinized alongside social consequences such as increased food poverty and hunger. The review evaluates existing strategies and technologies to reduce food waste and proposes comprehensive, long-term solutions. © 2024 IEEE.

关键词： Greenhouse gases

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Low-Cost Weather Monitoring System with Intelligent Design and XGBoost Regression

Low-Cost Weather Monitoring System with Intelligent Design a...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Vishnu Vilashini, S. Kishore, S. Nair, Rekha. R. Babu, Tina Bhadani, Anant Thanushree, G.R. Dixit, Anuj Kumar Dayananda Sagar University School of Engineering Department of Computer Applications Bengaluru India Nitte Institute of Professional Education Department of Computer Applications Karnataka India Allaince University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350313413

Weather monitoring is a critical aspect of weather forecasting and analysis, providing essential data for decision-making in various sectors. However, traditional weather monitoring systems face limitations such as high costs, limited accuracy, and inadequate coverage. The proposed system addresses these challenges by utilizing the Internet of Things (IoT) technology and NodeMCU, a low-cost microcontroller. The novelty of this work lies in the implementation of a cost-effective weather monitoring system that accurately forecasts upcoming weather conditions. Notably, the XGBoost Regression model emerges as the top performer, exhibiting exceptional accuracy with a mean absolute error (MAE) of 0.0213 indicating high accuracy in weather prediction. This innovative approach not only provides an affordable solution but also demonstrates the system's advanced capability to predict future weather patterns. Utilizing a NodeMCU board, real-time sensor data is gathered and transmitted to ThingSpeak's cloud server for analysis. This methodology has the potential for future applications in industries such as agriculture, transportation, and disaster management. © 2023 IEEE.

关键词： NodeMCU Real-time monitoring Recursive feature elimination ThingSpeak Weather prediction XGBoost Regression

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Flood Detection from Satellite Images Using Self-Attention Empowered EfficientNetV2 Mechanism 7

Flood Detection from Satellite Images Using Self-Attention E...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Selva Jeba, G. Chitra, P. Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India Thiagarajar College of Engineering Dept. of Computer Applications Madurai India

ISBN: (纸本)9798350304268

Floods occur when water overflows onto normally dry land and are a destructive natural disaster. In recent times, deep learning models have demonstrated their remarkable capabilities in identifying objects and classifying images. As a result, the utilization of deep learning in remote sensing, especially for the crucial task of flood detection, has become an increasingly prominent and promising area of research and application. This study proposes a lightweight yet powerful feature extraction framework by leveraging a self-attention(SA) based EfficientNetV2. The EfficientNetV2 architecture serves as the backbone of the model and has exhibited effectiveness in image classification tasks. By incorporating self-attention mechanisms, the model can capture spatial relationships within remote sensing images, enhancing its accuracy and efficiency in flood detection. The model was trained and assessed using the SEN12-FLOOD dataset, and the outcomes demonstrate that the suggested framework exhibits enhanced flood detection performance, achieving an accuracy of 95.9%. © 2023 IEEE.

关键词： attention mechanism deep learning EfficientNetV2 flood detection remote sensing

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A Comparative Analysis of Different Machine Learning Techniques For Medical Insurance Premium Prediction 1

A Comparative Analysis of Different Machine Learning Techniq...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Dash, Satyabrata Panigrahi, Bhawani Sankar Sanikommu, Vijay Venkata Bhaskar Reddy Madhavi, B.K. Sahoo, Susanta Kumar Department of Computer Science & Engineering Vishakhapatnam India Vardhman College of Engineeing Department of Information Technology Telangana Hyderabad India Ramachandra College of Engineering Department of Computer Science & engineering Andhra Pradesh Eluru India Indira Gandhi Institute of Technology Department of Computer Science Engineering & Applications Odisha Sarang India

ISBN: (纸本)9798350361780

Predicting health insurance premiums is a crucial task for both insurance companies and policyholders. This paper explores the use of regression approaches to predict health insurance premiums. The study uses a dataset of insurance premiums and related variables, including age, gender, BMI, and location to determine the factors that have the biggest effects on insurance rates. The study found that age and BMI were the main predictors of insurance rates, with gender and location having a relatively little influence. To create predictive models, the study combined various regression approaches, including logistic regression, Random Forest Regression, Decision Tree Regression, Gradient Boosting Regression, and linear regression. The predictive models were highly accurate, with a mean absolute error of less than 5% across all examined samples. The findings have significant implications for both insurers and customers, as accurate premium pricing is essential for insurers to maintain their financial health and offer customers coverage at reasonable prices. Accurate premium forecasts help policyholders plan their spending wisely for insurance costs and make informed decisions regarding their healthcare coverage. © 2024 IEEE.

关键词： Health insurance

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Empowering Diabetic Care: Building an Edge-Enabled Machine Learning Model for Classification 3

Empowering Diabetic Care: Building an Edge-Enabled Machine L...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Sahoo, Suchi Prangya Dash, Subhashree Subudhi, Anisha Agarwal, Shiva Dabata, Prajna Paramita Agarwal, Shashank Computer Science and Engineering Odisha Siksha 'O' Anusandhan Bhubaneswar India Utkarsh Business School Bachelor of Computer Applications Uttar Pradesh Bareilly India

ISBN: (纸本)9798350354379

Diabetes mellitus is a chronic metabolic dis-order characterised by elevated amounts of glucose in the bloodstream. The categorization and forecasting of diabetes are crucial for the effective administration of this prevalent long-term condition. Machine learning (ML) enables real-time monitoring and personalised therapy on edge devices. Machine learning can be employed to predict the likelihood of developing diabetes mellitus using advanced technology. Many symptoms are too subtle to be readily observed. It is a primary factor contributing to global blindness. It has a gradual lethal effect and there is currently no recognised remedy for it. This study explores the potential of edge-based predictive modelling for early identification of diabetes. Neural networks have the ability to accurately detect and analyse patterns and trends within patient data, leading to more accurate predictions of diabetes. Principal component analysis (PCA), quantization, dynamic pruning, and model compression are employed to optimise model performance while working within the limitations of the available re-sources. The proposed methodology is used and evaluated on the Pima Indian diabetic datasets, demonstrating its efficacy in achieving precise classification within the constraints of edge devices. Additionally, the possibility of shifting trained models from cloud servers to edge devices is being examined in order to reduce the computational burden. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning for Recognition of Handwritten Devanagri Character 1

Machine Learning for Recognition of Handwritten Devanagri Ch...

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

作者： Tyagi, Shilpa Dutta, Chiranjit Singh, Manu SRM Institute of Science and Technology Department of Computer Applications Uttar Pradesh Ghaziabad201204 India SRM Institute of Science and Technology Department of Computer Science & Engineering Uttar Pradesh Ghaziabad201204 India ABES Engineering College Department of Computer Science Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798350353648

A framework known as optical character recognition is capable of converting handwritten or printed pictures into machine-editable structures. Several Indian dialects, including Hindi, Nepali, Marathi, Sindhi, and others, use the Devanagari script. In India, Hindi is the national language and the language that is spoken by most people. This script organises the development of the language. Nowadays, people are interested in gathering data in sophisticated configurations that are available in paper archives and then employing a search process to reuse this data later. In this study, we provide a novel approach to Devanagari script character recognition for printed Hindi characters. The principal objective of this research is to identify the consonants and the vowels, which can subsequently be used for recognizing the complicated inferred words. The primary focus of this project is on the identification of each individual consonant and vowel. Several pre-processing techniques, including feature extraction, segmentation, and classification, have been researched and used in this project to create an advanced Hindi OCR system. The K-NN classification technique was used in earlier research;however, in the work that is being suggested, we have employed a hybrid technique that combines neural networks with K-NN. The recognition rate of the suggested approach is 97.4%, whereas that of the existing techniques is 94.5%. This suggests that the approach which proposed here is superior to techniques employed in the method which already exist. © 2024 IEEE.

关键词： Support vector machines

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A Review of Segmentation and Recognition Techniques for Indian Sign Language using Machine Learning and computer Vision 10

A Review of Segmentation and Recognition Techniques for Indi...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Kumari, Subhangi Tarlue, Ernest Diallo, Aissatou Chhabra, Megha Mishra, Gouri Shankar Goyal, Mayank Kumar School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India

ISBN: (纸本)9789380544472

Indian Sign Language (ISL) is a complex and diverse language used by the deaf and hard-of-hearing community in India. ISL recognition and segmentation have been active areas of research in recent years due to the growing demand for assistive technologies for this population. In this review paper, we provide an overview of the various techniques used for ISL recognition and segmentation using machine learning and computer vision. We begin by discussing the challenges involved in ISL recognition and segmentation, such as the variations in sign language gestures, lighting conditions, and background noise. We then review the literature on various techniques used for ISL recognition. We analyze the strengths and limitations of each technique, comparing their accuracy, speed, and robustness against various factors. Finally, we present the recent advancements and future directions for ISL recognition and segmentation © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Machine learning

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Quantum Cryptography: Mathematical Modelling and Security Analysis 3

Quantum Cryptography: Mathematical Modelling and Security An...

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Takalkar, Atharva Shiragapur, Bahubali School of Computer Science Engineering and Applications D y Patil International University Pune India

ISBN: (纸本)9798350302288

This paper offers a thorough investigation into quantum cryptography, a security paradigm based on the principles of quantum mechanics that provides exceptional guarantees for communication and information protection. The study covers the fundamental principles of quantum cryptography, mathematical modelling, practical applications, and future prospects. It discusses the representation of quantum states, quantum operations, and quantum measurements, emphasising their significance in mathematical modelling. The paper showcases the real-world applications of quantum cryptography in secure communication networks, financial systems, government and defence sectors, and data centres. Furthermore, it identifies emerging domains such as IoT, 5G networks, blockchain technology, and cloud computing as promising areas for implementing quantum cryptographic solutions. The paper also presents avenues for further research, including post-quantum cryptography, quantum cryptanalysis, multi-party quantum communication, and device-independent quantum cryptography. Lastly, it underscores the importance of developing robust infrastructure, establishing standards, and ensuring interoperability to facilitate widespread adoption of quantum cryptography. This comprehensive exploration of quantum cryptography contributes to the advancement of secure communication, information protection, and the future of information security in the era of quantum technology. © 2023 IEEE.

关键词： Secure communication

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