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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Ananta Ojha Dhyan Chandra Yadav Manish Kumar Goyal Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India

Deep gaining knowledge of is a place of artificial intelligence that is becoming increasingly famous inside the scientific area. This paper provides an improved optimization of computerized detection of cardiac abnormalities the use of deep learning. particularly, the authors recommend using a convolutional neural community (CNN) to stumble on abnormalities from ECG records. They use an ensemble of models to further improve accuracy and reduce false superb quotes. moreover, they apply transfer learning techniques to higher generalize the mastering from the EEG facts. The authors take a look at their optimized set of rules on two datasets of ECG recordings and file an normal accuracy of 88.9%. This demonstrates the potential for deep getting to know techniques to end up an increasing number of reliable and sturdy for detecting cardiac abnormalities. The authors also talk the feasible directions of future studies and the potentials of deep learning for clinical safety and diagnostics in phrases of fee and efficiency.

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Revolutionizing Healthcare Record Management: Secure Documentation Storage and Access through Advanced Blockchain Solutions

arXiv

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arXiv 2025年

作者： Brijwani, Geeta N. Ajmire, Prafulla E. Junaid, Mohammad Atique Mohammad Charasia, Suhashini Awadhesh Bhende, Deepali O. Computer Science Dept. Sant Gadge Baba Amravati University Amravati India Department of Computer Science Faculty of Engineering & Technology Sant Gadge Baba Amravati University India Department of Computer Science Rashtrasant Tukadoji Maharaj Nagpur University Nagpur India Department of Master of Computer Application Rashtrasant Tukadoji Maharaj Nagpur University Nagpur India

Integrating blockchain technology into healthcare systems presents a transformative approach to documenting, storing, and accessing electronic health records (EHRs). This research introduces a novel blockchain-based EHR system designed to significantly enhance security, scalability, and accessibility compared to existing solutions. Current systems primarily utilize SHA-256 for security and either IPFS or centralized storage, which, while effective, have limitations in providing comprehensive data integrity and security. The proposed system leverages a hybrid security algorithm combining Argon2 and AES and integrates a hybrid storage and consensus mechanism utilizing IPFS and PBFT. This multifaceted approach ensures robust encryption, efficient consensus, and high fault tolerance. Furthermore, the system incorporates Multi-Factor Authentication (MFA) to safeguard against unauthorized access. It utilizes advanced blockchain tools like MetaMask, Ganache, and Truffle to facilitate seamless interaction with the decentralized network. Simulation results demonstrate that this system offers superior protection against data breaches and enhances operational efficiency. Specifically, the proposed hybrid model substantially improves data integrity, consensus efficiency, fault tolerance, data availability, latency, bandwidth utilization, throughput, memory usage, and CPU usage across various healthcare applications. To validate the performance and security of the proposed system, comprehensive analyses were conducted using real-world healthcare scenarios. The findings highlight the significant advantages of the blockchain-based EHR system, emphasizing its potential to revolutionize healthcare data management by ensuring secure, reliable, and efficient handling of sensitive medical information. © 2025, CC0.

关键词： Electronic health record

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2022 Workshop on Advances in Computational Intelligence, its Concepts and applications, ACI 2022

作者： Gupta, Atul Sharma, Kalpana Goel, Krishan Kumar Department of Computer Science and Engineering Bhagwant University Ajmer India Department of Computer Application Raja Balwant Singh Management Technical Campus Agra India

In the current era, the digital data size increased enormously and available abundantly. Retrieving the relevant and accurate information from the available data still a big challenge. In this paper we are finding the similarity of noun-noun pairs and verb-verb pairs using WordNet as corpus. Computation of similarity between noun-noun pairs in a sentence using different semantic algorithm computed and *** has been observed that computation of similarity between verb-pair is found to be not as easy as computation of similarity between noun-pair. There are two challenges observed during the experimentation of this work. The first one is no standard data set available for verb pair and second is no exact hierarchy of verb of available in word-net. © 2020 Copyright for this paper by its authors.

关键词： Semantics

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S-EVPF: A Secure and Decentralized EV Prediction Framework using Federated Learning

S-EVPF: A Secure and Decentralized EV Prediction Framework u...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Aparna Kumari Deepika Shukla Rajan Datt Aaryan Patel Prasun Kumar Sagar Kumar Akshajan K Urvi Katarmal Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of Biotechnology SSET Sharda University Greater Noida U.P India Department of Computer Application Marwari College Ranchi University Ranchi Jharkhand India Department of Computer Application APJ Abdul Kalam Technological University Kerala India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The S-EVPF framework introduces a secure, decentralized solution for improving prediction models in the electric vehicle (EV) industry, addressing key concerns around privacy and data security. It enables multiple EVs to work together to enhance predictions related to battery health, maintenance schedules, time, and route efficiency, all while keeping sensitive data private. By using federated learning, EVs can contribute to a shared model without needing to centralize their data, benefiting from a wide range of information while complying with privacy rules. What sets S-EVPF apart is its use of blockchain technology to secure the exchange of data and model updates. This creates a permanent, auditable record of transactions and changes among all participants, ensuring the integrity and transparency of the system. By combining federated learning with blockchain, S-EVPF offers a solution that boosts both the accuracy and security of EV predictions. The framework not only enhances data protection but also encourages collaboration across the EV ecosystem, helping improve prediction performance and build trust. S-EVPF holds great promise for strengthening the decentralized management of EVs, making it a scalable and secure option for the industry.

关键词： Training Industries Accuracy Federated learning Biological system modeling Computational modeling Predictive models Data models Blockchains Context modeling

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Understanding the Dynamics of Sparse Coding in Auto-Encoders for Cancer Detection

Understanding the Dynamics of Sparse Coding in Auto-Encoders...

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Optimization Computing and Wireless Communication (ICOCWC), International Conference on

作者： Thiruvenkadam T Kalyan Acharjya Manish Kumar Goyal Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Application Vivekananda Global University Jaipur India

This summary outlines how sparse coding in vehicle encoders may be used to come across most cancers. It explains that by using learning sparse representations of raw records, you can benefit from a higher understanding of the underlying dynamics of cancer improvement. Furthermore, this summary outlines two of the most commonplace packages of vehicle encoders for cancer detection: function extraction and fitness tracking. This look gives insights into the capability of car encoders for efficient cancer analysis. For this reason, it should prove to be a vital element of most cancer detection and prevention. Sparse coding has been gaining interest inside the region of auto-encoder-based total methods for most cancer detection. With the increasing complexity of deep getting-to-know fashions being used for detection, the information and mastery of this research approach moderately encoded representations have turned out to be vital. Auto-encoders are used to encapsulate and encode inputs from high-dimensional facts into a lower-dimensional representation to keep the most crucial or crucial data from the facts. A selected type of vehicle encoder, a sparse automobile encoder, is designed to analyze sparsely encoded representations of records by introducing sparsity into the hidden layer of the automobile encoder via sure specialized regularizes.

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DQN-Based applications Offloading with Multiple Interdependent Tasks in Mobile Edge Computing 19th

DQN-Based Applications Offloading with Multiple Interdepend...

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19th EAI International Conference on Collaborative Computing: Networking, applications and Worksharing, CollaborateCom 2023

作者： Tu, Jiaxue Zhu, Dongge Xia, Yunni Li, Yin Ma, Yong Li, Fan Peng, Qinglan College of Computer Science Chongqing University Chongqing China Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd. Ningxia Yinchuan China Guangzhou Institute of Software Application Technology Guangzhou China School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang China Sichuan University Chengdu China School of Artificial Intelligence Henan University Zhengzhou China

ISBN: (纸本)9783031545207

Recently, Vehicular Edge Computing (VEC) is evolving as a solution for offloading computationally intensive tasks in in-vehicle environments. However, when the number of vehicles and users is large, pure edge resources may be insufficient and limited, most existing work focuses on minimizing system latency by designing some offloading strategies. Therefore, hybrid multilayer edge structures are in dire require of mission deployment strategies that can synthesize cost and mission latency. In this paper, we argue that each application can be decomposed into multiple interdependent subtasks, and that the different subtasks can be deployed separately into different edge layers in a hybrid three-tier edge computing infrastructure for execution. We develop an improved DQN task deployment algorithm based on Lyapunov optimization to jointly optimize the average workflow latency and cost under a long-term cost constraint, and simulation results clearly show that, comparing with the traditional approach, our proposed method effectively reduces the cost consumption by 92.8% while sacrificing only some latency. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Computation offloading

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Detection of Barrett's Esophagus in Endoscopic Images Using Machine Learning Algorithms

Detection of Barrett's Esophagus in Endoscopic Images Using ...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Al-Fatlawy, Ramy Riad Divya Khare, Saurabh Nitika Satyanarayana, A.N. Rajashree The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Ies University Department of Information Technology M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Mohali India Sreenidhi Institute of Science and Technology Ece Department Hyderabad India Department of Cse Nitte India

ISBN: (纸本)9798350390759

BE is a precursor to esophageal adenocarcinoma in which the patient is at a higher risk compared to those who the disease is not diagnosed. As for cancer, its early detection and proper handling should be of a significant concern to avoid chances of becoming CANCER. This paper aims at investigating the application and development of machine learning particularly Convolutional Neural Network (CNN) for automated identification of BE when seen in Endoscopy. Utilizing a mixture of open-source databases and specific clinical materials, a complex and well-established CNN model was created and calibrated to diagnose BE. Its efficacy was further supported by high performance rates such as the accuracy of 91% and sensitivity and specificity of 89% and 92% respectively along with AUC ROC value of 0. 94. To solve for the overfitting problem, data augmentation and transfer learning strategies were applied to improve the generalization of the model. Also, to increase the explainability of the deep-learning-based prediction model, certain explainable AI techniques were adopted. The model has been compared with the traditional ML model as well as the basic CNN, and the improved model seems to perform better. About the early detection of BE, this study reveals that the application of the ML-based tools shall provide new ideas and methods to help clinicians to detect BE at its early stage so as to improve the quality of patients' lives and the survival rate of death from esophageal adenocarcinoma. The clinical photographs generated by this software still require further fine-tuning and refinement to alleviate issues that hinder its implementation in clinical use and to integrate this software seamlessly in clinical settings. © 2024 IEEE.

关键词： Endoscopy

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Intelligent Control of an Integrated Energy System: Low Carbon Modeling and Intelligent Optimization

Intelligent Control of an Integrated Energy System: Low Carb...

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International Conference on Electronic engineering and Informatics (EEI)

作者： Zezheng Fu Wenting Lin Degang Yang College of Computer and Information Science Chongqing Normal University Chongqing China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application Chongqing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

An integrated energy system is a complex system that requires intelligent control to optimize its operation. This paper proposes an intelligent control method for integrated energy systems based on a low carbon model. The model integrates a variety of low carbon technologies such as carbon capture systems, demand response, carbon emissions trading, and clean energy generation. The effect of the proposed intelligent control methods on reducing system costs and carbon emissions is verified through a case study. The results show that the combination of the intelligent control method and the low carbon model leads to a significant reduction in system operating costs and carbon emissions. This provides an important reference for intelligent control of the existing integrated energy system from a low-carbon approach.

关键词： Green energy Costs Carbon dioxide Emissions trading Mathematical models Demand response Sustainable development Informatics Optimization Intelligent control

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Denoised Internal Models:A Brain-inspired Autoencoder Against Adversarial Attacks

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Machine Intelligence Research 2022年第5期19卷 456-471页

作者： Kai-Yuan Liu Xing-Yu Li Yu-Rui Lai Hang Su Jia-Chen Wang Chun-Xu Guo Hong Xie Ji-Song Guan Yi Zhou School of Life Sciences and Technology ShanghaiTech UniversityShanghai 201210China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201602China School of Life Sciences Tsinghua UniversityBeijing 100084China National Engineering Laboratory for Brain-inspired Intelligence Technology and Application School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei 230026China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-intelligence Nanophotonics School of Optical-electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Despite its great success,deep learning severely suffers from robustness;i.e.,deep neural networks are very vulnerable to adversarial attacks,even the simplest *** by recent advances in brain science,we propose the denoised internal models(DIM),a novel generative autoencoder-based model to tackle this *** the pipeline in the human brain for visual signal processing,DIM adopts a two-stage *** the first stage,DIM uses a denoiser to reduce the noise and the dimensions of inputs,reflecting the information pre-processing in the *** by the sparse coding of memory-related traces in the primary visual cortex,the second stage produces a set of internal models,one for each *** evaluate DIM over 42 adversarial attacks,showing that DIM effectively defenses against all the attacks and outperforms the SOTA on the overall robustness on the MNIST(Modified National Institute of Standards and Technology)dataset.

关键词： Brain-inspired learning autoencoder robustness adversarial attack generative model

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

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

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Gongliang Li Zepeng Wen Xin Xie 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

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.

关键词： Heating systems Sensitivity Intrusion detection Microservice architectures Telecommunication traffic Maintenance engineering Feature extraction

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