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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Joshi, Apoorva Preethi, D. Gill, Amandeep Noida Institute of Engineering & Technology Master of Computer Application Uttar Pradesh Greater Noida India Department of Computer Science Bangalore India Vivekananda Global University Department of R&d Cell Jaipur India

ISBN: (纸本)9798350357769

Conventional methods like classification or clustering have limits when it comes to handling the complex task for dispersed data mining across heterogeneous sources and divergent data formats. This is especially true in scenarios where stochastic models or mathematical formulas may not be feasible. Data centralization is a prevalent trend that exacerbates problems including communication channel congestion and privacy difficulties related to sensitive data flows. In order to overcome these obstacles, this paper presents the Distributed Academic Engine (DAE), an adaptive edge-based scattered analytic engine designed for distributed networks. The DAE routinely breaks down and divides computations that need data from multiple sources among the nodes that are already in place, enabling the sharing of partial results without disclosing the original data. To get the full expression output, the master node combines these partial results. The expression is decomposed and processing jobs are distributed among nodes using three computational intelligence techniques: genetic algorithm, genetic algorithm using evolution control, the particle swarm optimization. This method's effectiveness is illustrated with a case study on smart grid KPI identification, which shows a noteworthy (over 91%) decrease in communication messages over conventional approaches. This demonstrates how well the suggested method works to reduce the difficulties related to distributed data mining. © 2023 IEEE.

关键词： Computational Intelligence Data Integration and Heterogeneous Data Source Integration Distributed Analytics Edge Computing Genetic Algorithm Particle Swarm Optimization Smart Grid

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Applying Transfer Learning to Medical Image Classification Tasks 5th

Applying Transfer Learning to Medical Image Classification T...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Gupta, Manali Haripriya, V. Singh, Akhilendra Pratap Yadav, Surendra Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science & Application Vivekananda Global University Jaipur India

ISBN: (纸本)9789819780426

Transfer studying is a machine learning approach that permits a model to leverage know-how from a previous challenge to boost studying in the following tasks. It's been used increasingly in medical photo classification obligations, allowing area-particular knowledge to be leveraged from current fashions. This method can enhance the overall performance of clinical photograph-type structures and reduce the amount of labeled information required to analyze a brand-new venture. In this paper, we assess the present-day kingdom of switches, getting to know the scientific photo category and the results for clinical picture evaluation. We discuss present switch studying techniques and their influences on the accuracy of medical photo category responsibilities and the challenges related to constructing generalizable fashions. Eventually, we take a look at existing datasets and implementations available for the clinical photo category and factor into promising destiny studies guidelines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Investigating Wireless Networks with Network Coding Algorithms 5th

Investigating Wireless Networks with Network Coding Algorith...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Ahmed, Zahid Gupta, Anjali Preethi, D. Singh, Akhilendra Pratap Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Electrical and Electronic Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Community coding algorithms had been demonstrated to have capability to improve the performance of wireless networks. This paper offers a research, assessment, and implementation of community coding algorithms for Wi-Fi networks. The paper affords an overview of community coding algorithms and discusses the related blessings, drawbacks, and tradeoffs that have to be taken into account while evaluating the performance of these algorithms. Furthermore, simulation outcomes are offered to assess the overall performance of one of a kind network coding algorithms while implemented in a wireless community surroundings. Ultimately, the paper outlines the hardware and software elements required to correctly put into effect and test those algorithms in an operational wireless community. The promising consequences of the experiments imply that these community coding algorithms can be used to efficiently improve the overall performance of Wi-Fi networks within the future. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network coding

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Bio-assembled smart nanocapsules for targeted delivery of KRAS shRNA and cancer cell bioimage

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Chinese Chemical Letters 2023年第1期34卷 270-273页

作者： Maonan Wang Zengchao Guo Jiayu Zeng Liu Liu Yihan Wang Jinpeng Wang Hongbing Lu Haijun Zhang Hui Jiang Xuemei Wang State Key Laboratory of Bioelectronics(Chien-Shiung Wu Lab) School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China Department of Biomedical Engineering/Computer Application Fourth Military Medical UniversityXi’an 710032China Department of Oncology Zhongda HospitalMedical School of Southeast UniversityNanjing 210009China

The five-year survival rate for pancreatic cancer is less than 5%. However, the current clinical multimodal therapy combined with first-line chemotherapy drugs only increases the patient’s median survival from 5.0 months to 7.2 months. Consequently, a new strategy of cancer treatments is urgently needed to overcome this high-fatality disease. Through a series of biometric analyses, we found that KRAS is highly expressed in the tumor of pancreatic cancer patients, and this high expression is closely related to the poor prognosis of patients. It shows that inhibiting the expression of KRAS has great potential in gene therapy for pancreatic cancer. Given those above, we have exploited the possibility of targeted delivery of KRAS shRNA with the intelligent and bio-responsive nanomedicine to detect the special oxidative stress microenvironment of cancer cells and realize efficient cancer theranostics. Our observations demonstrate that by designing the smart self-assembled nanocapsules of melanin with fluorescent nanoclusters we can readily achieve the bio-recognition and bioimaging of cancer cells in biological solution or *** self-assembled nanocapsules can make a significant bio-response to the oxidative stress microenvironment of cancer cells and generate fluorescent zinc oxide Nanoclusters in situ for targeted cell bioimaging. Moreover, it can also readily facilitate cancer cell suppression through the targeted delivery of KRAS shRNA and low-temperature hyperthermia. This raises the possibility to provide a promising theranostics platform and self-assembled nanomedicine for targeted cancer diagnostics and treatments through special oxidative stress-responsive effects of cancer cells.

关键词： Smart bio-responsive nanocapsules Intelligent nanomedicine shRNA KRAS Fluorescent bioimaging Oxidative stress response

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Big Five Personality Prediction Based on Pre-training Language Model and Sentiment Knowledge Base 6

Big Five Personality Prediction Based on Pre-training Langua...

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6th International Conference on computer Information Science and application Technology, CISAT 2023

作者： Lin, Hao Li, Xiaolei Tianjin University of Technology School of Computer Science and Engineering Tianjin300191 China College of Data Science and Application Inner Mongolia University of Technology Hohhot010080 China

ISBN: (纸本)9781510668546

Machine learning-based personality prediction methods have become the mainstream prediction method. But existing Big Five personality prediction methods based on user-generated text rely too much on pre-trained language models to ignore the sentiment information. To tackle these issues, we propose a novel Big Five personality prediction method based on pre-trained language models and SenticNet6, which is a new sentiment knowledge base. The correlation analysis and ablation experiment shows that SenticNet6 can be used for Big Five personality prediction tasks. The experiment results indicate that our proposed method improves the state-of-art results for each Big Five personality trait by 9.89%, 9.39%, 2.99%, 3.01%, and 4.28% on Essays dataset, respectively. © 2023 SPIE.

关键词： Forecasting

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Classification and Prediction of Medical Dataset Using Ensemble DeepQ Classifier 4th

Classification and Prediction of Medical Dataset Using Ensem...

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4th International Conference on Communication and Intelligent Systems, ICCIS 2022

作者： Salman, Rahama Gupta, Subodhini Jain, Neelesh Department of Computer Science SAM Global University MP Bhopal India Department of Computer Application School of Information Technology SAM Global University MP Bhopal India Department of Computer Science and Engineering SAM Global University MP Bhopal India

ISBN: (纸本)9789819923212

The clinical dataset consists of a huge amount of disease information and which marks negative effects with respect to features. Feature selection is a major key player removing redundant information. It also increases the decision making result in an effective manner. The important step in feature selections is classification and dimensionality reduction. Medical experts and researchers are using various machine learning techniques to predict and analyze the huge volume of data. The large scale volume of medical dataset is having number attributes and dimensions. Dimensions can affect the analytics and diagnose the results. In this paper we evaluate principle component analysis, support vector machine, factor analysis and ranking methods compared with our proposed CNN-based ensemble feature selection using K-nearest classifier. Our proposed method is followed as standard testing features with parameter optimization. Compared with the existing method our method has 94% of accuracy result by using TensorFlow. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature Selection

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Automated Anomaly Detection in Medical Imaging Using Support Vector Machines 5th

Automated Anomaly Detection in Medical Imaging Using Support...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Goyal, Manish Kumar Shahid, Mohammad Karthikeyan, M.P. Kumar, Ritesh Department of Computer Science & Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Clinical imaging techniques such as X-ray, Magnetic Resonance Imaging (MRI), and laptop Tomography (CT) scanners are used to diagnose, compare, and deal with sufferers. Automatic Anomaly Detection is an automatic technique for identifying and diagnosing abnormalities in clinical imaging. It has been diagnosed that Support Vector Machines (SVM) can be effectively used to generate correct automatic anomaly detection for scientific imaging. SVM is an approach that uses supervised getting-to-know to create a choice boundary between classes, with the decision primarily based on the information to be had. The gain of SVM is that it plays an automatic function extracting from the medical imaging facts and uses those extracted capabilities for anomaly detection. It combines photograph segmentation and classification to classify the abnormal regions in an image. So one can enhance the accuracy of SVM;it's miles viable to use an expansion of kernel capabilities and unique parameter tuning. Moreover, introducing an automatic pipeline from training to deploying SVM reduces the efforts of manual feature extraction from photos. Consequently, aid Vector Machines is a feasible approach for automatic anomaly detection in scientific imaging. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

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Optimizing Drug Dosage and Medication Management for Geriatric Patients with Artificial Neural Networks 3

Optimizing Drug Dosage and Medication Management for Geriatr...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Beemkumar, N. Singh, Daljeet Pal Ahmed, Zahid Faculty of Engineering and Technology Department of Mechanical Engineering Bagalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350343274

In this paper, we utilize ANNs to investigate how medicine dosing and management might be improved for the elderly population. The suggested strategy calls for the use of patient-specific data as inputs for the ANNs, such as age, renal and hepatic function, body mass index, and preexisting prescription profiles. The neural network model dynamically changes drug doses using the combination of the Multilayer Perceptron (MLP) algorithm, Backpropagation, and the Stochastic Gradient Descent (SGD) algorithm to maximize therapeutic benefits and reduce the likelihood of adverse drug events. The research highlights the improved accuracy, sensitivity, specificity, precision, and F1 score of the suggested technique compared to numerous conventional methods. As a result, the suggested system has great potential to revolutionize medication management for the elderly population since it is more personalized, flexible, patient-safe, effective, and efficient in its use of resources. © 2023 IEEE.

关键词： Optimization

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Understanding the Role of Data-Driven Communications Software Design in Networks 5th

Understanding the Role of Data-Driven Communications Softwar...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Singh, Vaishali Inzimam Dadhich, Shruti R, Murugan Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Application Vivekananda Global University Jaipur India Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Facts-pushed verbal exchange software program layout is a critical element in the design of statistics networks. It encompasses the identification, control, and manipulate of network traffics, which includes the prioritization of communications and associated statistics, in addition to the regulation of connection distributions and assets over the complete community. Furthermore, information-pushed communique software program design encompasses algorithms designed to mitigate potential verbal exchange bottlenecks, via controlling the drift of information via the network, thereby improving its reliability and serviceability. Thru this technique of design, it's far possible to create a dynamically optimized community that is capable of speedy and successfully routing facts to its supposed vacation spot. Through making use of these standards, designers and engineers are able to create high-appearing networks that fulfill the demands located upon them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Software design

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COMBINING PHYSICS-INFORMED GRAPH NEURAL NETWORK AND FINITE DIFFERENCE FOR SOLVING FORWARD AND INVERSE SPATIOTEMPORAL PDES

arXiv

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arXiv 2024年

作者： Zhang, Hao Jiang, Longxiang Chu, Xinkun Wen, Yong Li, Luxiong Xiao, Yonghao Wang, Liyuan Institute of Computer Application China Academy of Engineering Physics Sichuan Mianyang621000 China

The great success of Physics-Informed Neural Networks (PINN) in solving partial differential equations (PDEs) has significantly advanced our simulation and understanding of complex physical systems in science and engineering. However, many PINN-like methods are poorly scalable and are limited to in-sample scenarios. To address these challenges, this work proposes a novel discrete approach termed Physics-Informed Graph Neural Network (PIGNN) to solve forward and inverse nonlinear PDEs. In particular, our approach seamlessly integrates the strength of graph neural networks (GNN), physical equations and finite difference to approximate solutions of physical systems. Our approach is compared with the PINN baseline on three well-known nonlinear PDEs (heat, Burgers and FitzHugh-Nagumo). We demonstrate the excellent performance of the proposed method to work with irregular meshes, longer time steps, arbitrary spatial resolutions, varying initial conditions (ICs) and boundary conditions (BCs) by conducting extensive numerical experiments. Numerical results also illustrate the superiority of our approach in terms of accuracy, time extrapolability, generalizability and scalability. The main advantage of our approach is that models trained in small domains with simple settings have excellent fitting capabilities and can be directly applied to more complex situations in large domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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