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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Rengarajan, A. Ahmed, Zahid Pandey, Rajendra P. Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

deep mastering has revolutionized the field of scientific imaging, providing practical affected person prognosis competencies. A current example is deep studying for Magnetic Resonance Imaging (MRI) acquisition, reconstruction, segmentation, and interpretation. Deep mastering-primarily based strategies leverage convolutional neural networks (CNNs) to automatically classify and section not unusual disease areas in MRI scans, allowing particular and correct prognosis. Those computerized strategies can potentially conquer the bottleneck of the complex work-intensive guide segmentation and visualization procedure. Moreover, they can provide an extra complete assessment of complex sicknesses. Through incorporating both the semantic and spatial information of image information, the overall performance of deep-gaining knowledge of-based totally structures for MRI evaluation has dramatically progressed. Moreover, CNN-based total MRI segmentation has proven promise in targeted and effective remedies for numerous diseases, including mind tumors, stroke, and dementia. The demonstration of superior effects in segmentation and classification tasks, in terms of accuracy and efficiency, shows the potential of deep learning-based techniques as an effective device within the automation of MRI analysis. © 2024 IEEE.

关键词： classification demonstration performance prognosis visualization

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Designing Blockchain Based Consensus Mechanism for Smart Healthcare IoT

Designing Blockchain Based Consensus Mechanism for Smart Hea...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Kumari, Trishla Kumar, Rakesh Dwivedi, Rajendra Kumar Madan Mohan Malviya University of Technology Dept. of Computer Science and Engineering Gorakhpur India Madan Mohan Malviya University of Technology Dept. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9781665462631

In today's world, healthcare system is improving day by day by using smart IoT based system. Blockchain based system is totally a decentralized system due to which attackers are unable to access the confidential records easily. Blockchain play an important role in controlling and management of sensitive information in healthcare system and insurance sector. Consensus mechanisms used in blockchain technology to validate whichever block is going to be added next in the blockchain network. In this paper we proposed a hybrid based system which solves trust issues arises in the blockchain. Hybrid consensus mechanism provides reputation according to the activities of blocks in a blockchain network. Four layer architecture is designed in Hybrid model. Five algorithms are used to create, validate, fork handling, reward and punishment scheme and Markle tree is constructed respectively. This model is implemented on Ethereum based platform which shows that HCM model is better than a PoXR model as it consume less memory (50 percent) and consume less CPU(below 2 percent). © 2023 IEEE.

关键词： Blockchain

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Unsupervised Anomaly Detection Based on CNN-VAE with Spectral Residual for KPIs 24

Unsupervised Anomaly Detection Based on CNN-VAE with Spectra...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and application, HPCC/DSS/SmartCity/DependSys 2022

作者： Li, Gongliang Wen, Zepeng Xie, Xin Institute of Computer Application Technology China Academy of Engineering Physics College of Computer Science Sichuan University Mianyang China Institute of Computer Application Technology China Academy of Engineering Physics Mianyang China

ISBN: (纸本)9798350319934

Current large-scale applications, such as trading systems, blockchain, social software, etc, are increasingly adopting microservice architecture, which bring challenges to manual operation and maintenance, intrusion detection. In both operations and intrusion detection, there are a common characteristic that service metrics and network traffic are normal for most of the time, but anomaly data is more important. In this paper, we propose an unsupervised anomaly detection algorithm based on convolutional neural network with Spectral Residual, which is verified experimentally and has potential application capability with 19.2% f1-score improvement compared to the Variational AutoEncoder. © 2022 IEEE.

关键词： Intrusion detection

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Correction to: Evaluation of land use land cover dynamics and urban heat island effects over Mumbai metropolitan Region, India (International Journal of Environmental Science and technology, (2024), 10.1007/s13762-024-06266-4)

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International Journal of Environmental Science and technology 2025年 1-2页

作者： Kavathekar, V. Tripathy, A.K. Chettri, S.K. Bhanage, V. Computer Science and Engineering Computer Application Assam Don Bosco University Assam India Computer Engineering Don Bosco Institute of Technology Mumbai India PHIS Centre The IDEC Institute Hiroshima University IDEC Bldg. 1-5-1 Kagamiyama Higashi-Hiroshima739-8529 Japan

The original version of this Article contained an error in the representation of elevation values depicted in the Fig. 1. Incorrect version of Fig. 1 Geographical location of the Mumbai metropolitan region contributes to achieving the region’s sustainable development goals. (Figure presented.) Readers should consider following Figure as Fig. 1. Correct version of Fig. 1 Geographical location of the Mumbai metropolitan region (Figure presented.) © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2025.

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Convolutional Neural Networks Aided Reinforcement Learning for Accelerated Optimization of Antenna Topology

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Applied Computational Electromagnetics Society Journal 2025年第1期40卷 35-41页

作者： Dou, Jiangling Gong, Hao Wei, Shuaibing Chen, Haokang Chen, Yujie Shen, Tao Song, Jian Yunnan Key Laboratory of Computer Technologies Application Kunming University of Science and Technology Kunming650500 China School of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China College of Mechanical and Electrical Engineering Yunnan Electromechanical Vocational and Technical College Kunming650500 China

A machine learning (ML) framework is proposed to achieve the automatic and rapid optimization of antenna topologies. A convolutional neural network (CNN) is utilized as a surrogate model (SM) and is combined with reinforcement learning (RL) algorithms. Specifically, the RL agent interacts with simulation software to learn. Data accumulated from electromagnetic (EM) simulations are used to train the SM. The CNN-based SM predicts antenna performance based on the topology of the antenna. Subsequently, the SM replaces EM simulations within the RL training environment. The RL agent interacts with the CNN-based SM to search for the optimal topology. This approach significantly reduces dependence on time-consuming EM simulations. To validate the effectiveness of the optimization method, a center-fed microstrip patch antenna is optimized. Simulation results demonstrate that, compared to other optimization methods, impedance bandwidth is improved, while the number of simulation samples and optimization time are significantly reduced. © ACES.

关键词： Convolutional neural networks

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Applying Non-Local Means Filter on Seismic Exploration

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computer Systems Science & Engineering 2022年第2期40卷 619-628页

作者： Mustafa Youldash Saleh Al-Dossary Lama AlDaej Farah AlOtaibi Asma AlDubaikil Noora AlBinali Maha AlGhamdi College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal UniversityDammamP.O.1982Saudi Arabia Geophysical Application Division Exploration Application Services DepartmentSaudi AramcoDhahranSaudi Arabia

The seismic reflection method is one of the most important methods in geophysical *** are three stages in a seismic exploration survey:acquisition,processing,and *** paper focuses on a pre-processing tool,the Non-Local Means(NLM)filter algorithm,which is a powerful technique that can significantly suppress noise in seismic ***,the domain of the NLM algorithm is the whole dataset and 3D seismic data being very large,often exceeding one terabyte(TB),it is impossible to store all the data in Random Access Memory(RAM).Furthermore,the NLM filter would require a considerably long *** factors make a straightforward implementation of the NLM algorithm on real geophysical exploration data *** paper redesigned and implemented the NLM filter algorithm to fit the challenges of seismic *** optimized implementation of the NLM filter is capable of processing production-size seismic data on modern clusters and is 87 times faster than the straightforward implementation of NLM.

关键词： Seismic exploration parallel programming seismic processing optimizing methods

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Using Ant Colony Optimised Algorithms for Shortest Path Exploration by Robots 2

Using Ant Colony Optimised Algorithms for Shortest Path Expl...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Singh, Aashdeep Dhawan, Sakshi Verma, Jyoti Singh, Gurpreet Kaur, Amanpreet Dept. of Computer Science & Engineering Punjab Rajpura India Dept. of Computer Application Punjab Rajpura India Chitkara University Institute of Engineering & Technology Dept. of Computer Science & Engineering Punjab Rajpura India

ISBN: (纸本)9798350360523

In the realm of robotics and autonomous systems, efficient path planning is a critical aspect for optimizing resource utilization and achieving mission objectives. This study explores the application of Ant Colony Optimized (ACO) algorithms to enhance the efficiency of robots in navigating and exploring unknown environments. Inspired by the foraging behavior of ants, ACO algorithms are employed to find the shortest paths by leveraging pheromone communication and decentralized decision-making. Ant Colony Optimization is then introduced as a bio-inspired solution to address these challenges. The experimental results demonstrate the capability of the ACO algorithm to efficiently explore complex environments, adapt to changes in the surroundings, and converge to optimal paths. Insights gained from these analyses contribute to the refinement of the ACO algorithm for diverse robotic applications. The findings of this research have practical implications for the development of autonomous robots operating in real-world scenarios, such as search and rescue missions, environmental monitoring, and surveillance. This study provides a valuable foundation for the integration of bio-inspired algorithms in robotics, advancing the state-of-the-art in autonomous exploration and path planning. © 2024 IEEE.

关键词： Motion planning

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Transforming Modern Medicine with the Role of the Healthcare Internet of Things in Enhancing Patient Care and Monitoring 13

Transforming Modern Medicine with the Role of the Healthcare...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Inzimam Bhuvana, J. Desai, Karishma Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Atlas SkillTech University Department of UGDX Maharashtra Mumbai India

ISBN: (纸本)9798350305463

The present healthcare sector is experiencing a fundamental upheaval with the emergence of the Healthcare Internet of Things (HIoT). This article delves into the transformational potential of HIoT by examining its use in bettering patient care and monitoring. The paper dives into the introduction of HIoT, the methodologies adopted for research and development, the compelling findings gained, and the farreaching consequences in conclusion The opening part sets the scene by describing the fundamental transformation in healthcare facilitated by HIoT. It emphasizes the catalyst for rethinking patient care and monitoring as the combination of internet-connected devices, smart sensors, and data analytics. HIoT equips doctors with up-to-the-minute information, allowing for more precise diagnoses and more individualized plans of care. Furthermore, deep learning and artificial intelligence were incorporated to exploit the richness of HIoTgenerated data, allowing sophisticated analytics and informed decision-making. The compelling findings of this study are clear, with the suggested technique regularly beating existing methods across numerous criteria. A remarkable accuracy score of 0.94 was reached, demonstrating the reliability of the suggested strategy in supporting informed healthcare decisions. Furthermore, in terms of cost efficiency, the suggested approach scored 0.95, suggesting its costeffectiveness and potential for economic viability. The development of HIoT promises a healthcare system that is more patient-centered, data-driven, and efficient in the future. To ensure healthcare continues to grow into an age of unmatched patient care and monitoring, researchers, healthcare providers, and governments must work together to harness this transformational potential while tackling the issues that come with it. © 2024 IEEE.

关键词： Predictive analytics

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Hate Speech detection in English and Malayalam Code-Mixed Text using BERT embedding

Hate Speech detection in English and Malayalam Code-Mixed Te...

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2022 International Conference on Computing, Communication, Security and Intelligent Systems, IC3SIS 2022

作者： Deepasree Varma, P. Vinod, P. Nandakumar, M. Akshay, K. Madhu, Akhil Scms School of Engineering and Technology Department of Computer Application Cochin India Cochin University of Science and Technology Department of Computer Application India Christ College of Engineering Irinjalakkuda Department of Electrical and Electronics Engineering India SCMS College of Engineering and Technology Karukkutty Department of Computer Application Ernakulam India

ISBN: (数字)9781665468831

ISBN: (纸本)9781665468831

Hate speech detection is a very popular research area for past few years. Hate speech is given various definition by various researchers. In this paper we try to analyse the use of BERT embedding in hate speech detection in low resource language like Malayalam. The crucial challenge faced by researchers in this area are that most non-English languages are represented in code-mixed form in Social media. Here we work with transformer-based models to classify tweets as hate or non-hate content. Hence this is a novel approach that uses BERT in non-English text. © 2022 IEEE.

关键词： Embeddings

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Kidney Stone Prediction Using Neural Network Classifier 2

Kidney Stone Prediction Using Neural Network Classifier

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2nd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2022

作者： Pavithra Sanjurajan Chitradevi Eliyas, Sherin Benitta, Angeline Kumar, Sathish Hindustan Institute of Technology and Science Department of Computer Application Chennai India

ISBN: (纸本)9781665437899

kidney stone disorder is a frequent hassle in the World today. The majority of kidney stones is tiny and bypass on their own. Overwork, which suppresses human antibodies, excessive sodium intake, and worse exterior surroundings stipulations such as bloodless and dampness too are effortless reasons of kidney illnesses nowadays. Using bodily inspection or operators to acquire findings for large datasets is tough. In CT scans there is a lot of noise, which will increase the mistakes. Deep gaining knowledge of strategies like neural networks has proven to be enormously really helpful in this field. As a result, this challenge employs the Convolutional Neural Network (CNN). The facets are extracted the usage of GLCM and then classified the usage of CNN. This find out about makes use of the Fuzzy C-Mean clustering approach to section computed tomography pictures in order to pick out the kidney stones early on. © 2022 IEEE.

关键词： Convolutional neural networks

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