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No reference retinal image quality assessment using support vector machine classifier in wavelet domain

Multimedia Tools and Applications 2024年第36期83卷 84381-84400页

作者： Sahu, Sima Singh, Amit Kumar Priyadarshini, Nishita Maisammaguda Telangana Hyderabad India Department of Computer Science & amp Engineering National Institute of Technology Patna Patna India AIIMS Bibinagar Telangana Hyderabad India

The automatic retinal screening system (ARSS) is a valuable computer-aided diagnosis tool for healthcare providers and public health initiatives. The ARSS facilitates mass retinal screenings that analyse retinal images and detect early signs of vision-threatening retinal diseases. The degradation in retinal image’s naturalness causes imprecise diagnosis. This paper proposed a quality assessment method that is suitable for ARSS and is important for closing care gaps and reducing healthcare costs in the field of healthcare. A no-reference (NR) quality assessment method utilizing natural scene statistics (NSS) and the multi-resolution approach is developed to detect retinal image quality. Image quality classification is performed combining NSS features and statistical featurenns of retinal image. A support vector machine classifier is used to map the retinal image features and find image quality. The proposed method is compared with existing NR image quality assessment methods. The results show that the proposed method has improved accuracy, recall, precision and F-measure values of 3.42%, 3.66%, 1.63% and 2.66%, respectively, over the competing methods, demonstrating its suitability for ARSS. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Wavelet transforms

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IFCSim: An IoT-Fog-Cloud Simulator for Analyzing Performance of Topologies 15

IFCSim: An IoT-Fog-Cloud Simulator for Analyzing Performance...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Panda, Sanjaya Kumar Bhagat, Aditi National Institute of Technology Warangal Department of Computer Science and Engineering Telangana Warangal506004 India

ISBN: (纸本)9798350370249

Nowadays, many real-life applications, namely healthcare, transportation, fire detection, and many more, rely on data collected from Internet of Things (IoT) devices. The collected data is too extensive and requires enormous processing for analysis. As cloud device provides massive computation and storage, IoT devices transmit data for analysis without considering the proximity, resulting in latency and network usage issues. Therefore, fog computing brings computation and storage closer to the IoT devices to resolve these issues. However, it still depends on cloud device for massive computation and storage. On the other hand, a simulation platform is essential to evaluate IoT, fog, and cloud environments effectively and show resource management techniques' impact on latency and network usage. Recently, the iFogSim simulator was introduced to show the impact of resource management techniques. However, this tool can be extended by adding various components, namely sensors, actuators and fog devices, to solve more real-life problems. This paper introduced various components within the iFogSim tool and named the IoT-fog-cloud simulator (IFCSim) tool. IFCSim is more user-friendly for developing, simulating, and reviewing the results of different topologies. It can simulate more real-life applications, from factories to intelligent buildings, exploring fog computing to its fullest potential. We show how fog computing can make an efficient fire detection systems with reduced latency and network usage using IFCSim. We study how spreading the data from the edge devices can make detection faster and more reliable. Our simulator tool lets one create different network topologies for performing simulations. © 2024 IEEE.

关键词： Resource allocation

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Optimizing Urban Transportation Using Big Data and Reinforcement Learning Techniques 15

Optimizing Urban Transportation Using Big Data and Reinforce...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Arunakumari, B.N. Shashidhara, Shrividya Bms Institute of Technology & Managament Department of Computer Science Engineering Karnataka Bangalore560064 India

ISBN: (纸本)9798350370249

There is no surprise that it is known that there have emerged several benefits as well as drawbacks with the progressive growth of industrialization, vehicles-be it public or private, as well as urban transit systems. Obviously, this issue is addressed to requite frustrating especially when there is a need to travel from once place to another within a particular period of time. Thankfully, this is where big data emerges into the frame to help reduce congestion and traffic problems in order to improve the approaches and efforts made towards optimizing transport by proposing a framework that makes use of collecting, aggregating and disseminating data from various kinds of sources. The fundamental approach towards achieving this is making use of reinforcement learning techniques-particularly the application of Deep Q Network Based Scheduling algorithm. By harnessing vast datasets, this algorithm dynamically optimizes traffic signal control, route planning, and resource allocation in real-time. © 2024 IEEE.

关键词： Deep reinforcement learning

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Optimal Placement of Radio Frequency Energy Harvester for IoT-Based Applications 8

Optimal Placement of Radio Frequency Energy Harvester for Io...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Binsiya, P.N. Mini, S. National Institute of Technology Goa Department of Computer Science and Engineering Goa Cuncolim403703 India

ISBN: (纸本)9798331531539

The Internet of Things (IoT) is redefining how we interact with our surroundings, from smart home gadgets to crucial industrial applications. The increasing deployment of IoT devices has necessitated efficient and sustainable energy solutions. Harvesting energy from radio frequency (RF) signals has developed as a sustainable way to power IoT devices by converting ambient RF signals into usable electrical energy as we are surrounded by powerful electromagnetic waves in the 5G communication era. Capturing maximum power in RF energy harvesting is a primary challenge due to the low power density of radio frequency energy. Positioning the RF harvester in the ideal location is very important for maximizing the capture of ambient RF waves. A Particle Swarm Optimization (PSO) based method is deployed for the optimal positioning of RF energy harvesters in IoT environment to maximize energy capture and device performance. The PSO algorithm is employed to find near-optimal solutions by simulating particle movements in a multidimensional search space. This optimization considers factors such as RF signal intensity, frequency and the spatial distribution of RF signal sources to determine the optimal locations for deploying energy harvesters. The placement strategy dynamically adjusts to maximize energy harvesting by moving towards the RF source with highest available power while maintaining a balance between coverage and energy harvesting. Results indicate that optimal harvester placement significantly enhances energy efficiency and extends the operational lifespan of IoT networks, thereby contributing to more sustainable and autonomous IoT deployments. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Breast Cancer Detection using Deep Learning 2

Breast Cancer Detection using Deep Learning

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Afaq, Shajal Singh, Nanhay Netaji Subhas Institute of Technology Department of Computer Science and Engineering New Delhi110031 India

ISBN: (纸本)9798350368109

Breast cancer is a leading cause of morbidity for women all over the world, BC, or breast cancer, affects many women globally. It ranks fourth in fatality among colon, brain, and cervical malignancies. New BC cases are predicted to rise 70% in the following two decades. Thus, early identification and accurate breast cancer diagnosis improve prognosis and long-term survival. Machine learning methods like "Support Vector Machines"(SVM) and Nearest Neighbor have shown promising but limited efficacy in recognizing BC. This study introduced a "Convolutional Neural Network"(CNN) model of utilizing deep neural networks to diagnose BC using the Wisconsin BC database. The research demonstrates the application of DL technology for diagnosing BC using the Kaggle Dataset. The research method involves data acquisition, data preprocessing and using the dataset to train the CNN model and assess its performance using four key metrics: precision, recall, accuracy, and F-measure. CNN model accuracy is 95.57%, precision is 97.70%, recall is 98.64%, and F-measure is 98.14%. This approach outperforms standard models, proving its effectiveness in detecting BC. © 2024 IEEE.

关键词： Lung cancer

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Video Frame Interpolation Using Real-Time Intermediate Flow Estimation

Video Frame Interpolation Using Real-Time Intermediate Flow ...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Prasanna, B. Niranjan, S. Sabaresan, V. St.Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359688

RIFE is a breakthrough in real-time video frame interpolation, introducing the IFNet neural network to estimate intermediate flows directly and efficiently. By avoiding the complexities of bidirectional optical flow estimation, RIFE achieves impressive speed and quality improvements, outperforming leading methods like SuperSlomo and DAIN. This research is timely, addressing the increasing demand for high-quality video content and the need to convert low-frame-rate videos into high-frame-rate content for modern displays. RIFE's privileged distillation scheme and support for arbitrary-timestep frame interpolation make it a promising solution for real-time video enhancement. © 2024 IEEE.

关键词： Optical flows

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Unlocking the Viability of Blockchain Adoption for Healthcare: A Feasibility Study 15

Unlocking the Viability of Blockchain Adoption for Healthcar...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gupta, Shreya Mishra, Shreya Bajpai, Shubhranshi Yadav, Shreya Verma, Rajat Pranveer Singh Institute of Technology Department of Computer Science and Engineering Uttar Pradesh Kanpur India

ISBN: (纸本)9798350370249

Blockchain technology holds several exciting features such as security, decentralization, data integrity, access control, consent management, etc. The healthcare industry can reach its peak with the emergence of blockchain technology in terms of an efficient Electronic Health Record (EHR) Management System. This research paper explores the feasibility of implementing blockchain to improve the existing healthcare record management system and enable better data security, interoperability, data integrity, and patient consent management. The research thoroughly addresses issues in various articles, including data privacy, regulatory compliance, and scalability, and proposes blockchain recommendations. The paper will feature a comprehensive analysis of the prospects of decentralization, which can facilitate data exchange and improve interoperability among various systems. By thoroughly analyzing the potential advantages and vulnerabilities in the existing system, this research will provide a holistic understanding of the blockchain's contribution to bringing about a revolution in the healthcare sector. © 2024 IEEE.

关键词： Blockchain technology Electronic Health Records Feasibility Healthcare Industry Interoperability

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Neutrino Direction Detection using GNN 1

Neutrino Direction Detection using GNN

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Vijayakumar, K. Padmanaban, A. Rajesh Kumar, D. St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372748

The IceCube Neutrino Observatory is located at the South Pole, which is an important tool for studying high-energy neutrinos, and provides novel insights into the universe's most energetic processes. In this study, a new reconstruction technique for IceCube events, called "dynedge,"based on Graph Neural Networks (GNNs) is presented. Simulated low-energy data are used to assess the technique, with an emphasis on atmospheric neutrino oscillations in the 1 GeV-30 GeV energy range. Across the low-energy spectrum, the suggested dynedge method shows notable improvements in reconstruction accuracy for the T/C and v/μ classifications. With respect to typical atmospheric vμ-v oscillation studies, dynedge exhibits a notable 15-20% improvement in reconstructing important parameters, such as energy, zenith, direction, and interaction vertex, in the critical energy range of 1 GeV-30 GeV. © 2024 IEEE.

关键词： Germanium alloys

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Implementing Gradient Boosting Techniques for Real-Time Attack Detection in Vehicular Networks 4

Implementing Gradient Boosting Techniques for Real-Time Atta...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Sharma, Anshika Babbar, Himanshi Chitkara University Institute of Engineering and Technology Chitkara University Department of Computer Science Punjab India

ISBN: (纸本)9798350387490

Several attacks pose serious dangers to vehicular networks (VN), crucial to creating smart transportation systems (ITS). Due to ITS, VN has become more important since it allows for smooth communication between infrastructure and cars. These networks are becoming more interdependent with smart transportation systems, which makes them more susceptible to assaults. By applying machine learning (ML) strategies, this paper seeks to strengthen VN security by making intrusion detection systems (IDS), applying ML models Gradient Boosting Machine (GBM), Adaptive Gradient Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) to the ToN-IoT dataset for training and evaluation. The dataset contains normal and malicious network traffic data. These results show that XGBoost and LightGBM, two GB algorithms, are super effective in detecting attacks in VN. The research shows that these models can alert drivers of impending threats in real time, making vehicle communication systems much safer. More research is underway into testing these models in real-world vehicle networks under varying conditions and attack scenarios. Intelligent transportation systems rely on developing cutting-edge security solutions, which this research helps to provide. © 2024 IEEE.

关键词： Network intrusion

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Fake News Detection Using Logistic Regression and LightGBM 2

Fake News Detection Using Logistic Regression and LightGBM

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Carmel Mary Belinda, M.J. Alex David, S. Kannan, E. Ruth Naveena, N. Rajathi, K. Mahesh, C. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of computer science and engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of computer science and engineering Chennai India Hindustan Institute of Technology & Science Department of mathematics Chennai India

ISBN: (纸本)9798350368413

The spread of false information via fake news in recent years has raised serious issues and caused social discontent and division. It is critical to create efficient techniques for quickly and reliably recognizing bogus news. In the context of identifying fake news, this paper compares two popular machine learning techniques: LightGBM and Logistic Regression. Our analysis makes use of a dataset that includes both fake and real news pieces, with 23,481 fraudulent news stories and 21,417 real news stories. Important performance parameters including recall, accuracy, precision, F1-score, and Matthews correlation coefficient are taken into account while evaluating these algorithms. Our goal in doing this research is to clarify how effective these strategies are at solving the urgent problem of the spread of false information. © 2024 IEEE.

关键词： Logistic regression

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