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A secured energy aware resource allocation and task scheduling based on improved cuckoo search algorithm and deep reinforcement learning for e-healthcare applications

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Measurement: Sensors 2024年 31卷

作者： Palani, S. Rameshbabu, K. Research Scholar School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Healthcare industries have begun using modern technologies and tools like cloud computing these days. When cloud computing and assisted technologies are used in the healthcare domain the storage cost improves along with the quality and time availability. The present work makes use of cryptography based architecture for improving the overall security aspects. However, the tasks of the user take a longer duration for the execution to complete when the needed resources are unavailable on the main server. ICSA (Improved Cuckoo Search Algorithm) with DRL (Deep Reinforcement Learning) to solve this issue. The main goal of this work is to increase overall security and reduce execution times of jobs. The objective also includes using underutilized resources. Scheduling jobs, Binary In-order Traversal Trees with weights are used. Subsequently, DRL algorithm has been used to decrease the space complexity by splitting the individual resources. There will be idle resources in a state space that are used in allocation of tasks. The scheduled tasks will then do a search on the resources based on the ICSA algorithm. The server will then distribute the resources to the action area after an optimal resource has been chosen and assigned to the job. DRL for resource allocation so that the inactive resource usage is minimized and the execution time is reduced. Finally, MTAC-IBEA (multi tenant authentication control with improved blowfish encryption algorithm) for decrypt health data. The outcomes of simulation demonstrate that the suggested method offers optimum work schedules, resource allocations, and security in e-healthcare systems. © 2023 The Authors

关键词： Reinforcement learning

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Patterned graphene oxide via one-step thermal annealing for controlling collective cell migration

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Journal of Materials Chemistry B 2024年第35期12卷 8733-8745页

作者： Huang, Chien-Yu Chen, Chong-You Wei, Chia-Hung Yang, Jia-Wei Lin, Yu-Chien Kao, Chih-Fei Chung, Johnson H. Y. Chen, Guan-Yu Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Electrical and Computer Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Department of Biological Science and Technology College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Intelligent Polymer Research Institute Institute for Innovative Materials University of Wollongong NSW2500 Australia National Yang Ming Chiao Tung University Hsinchu300093 Taiwan

Graphene oxide (GO) is a two-dimensional metastable nanomaterial. Interestingly, GO formed oxygen clusterings in addition to oxidized and graphitic phases during the low-temperature thermal annealing process, which could be further used for biomolecule bonding. By harnessing this property of GO, we created a bio-interface with patterned structures with a common laboratory hot plate that could tune cellular behavior by physical contact. Due to the regional distribution of oxygen clustering at the interface, we refer to it as patterned annealed graphene oxide (paGO). In addition, since the paGO was a heterogeneous interface and bonded biomolecules to varying degrees, arginine-glycine-aspartic acid (RGD) was modified on it and successfully regulated cellular-directed growth and migration. Finally, we investigated the FRET phenomenon of this heterogeneous interface and found that it has potential as a biosensor. The paGO interface has the advantages of easy regulation and fabrication, and the one-step thermal reduction method is suitable for biological applications. We believe that this low-temperature thermal annealing method would make GO interfaces more accessible, especially for the development of nano-interfacial modifications for biological applications, revealing its potential for biomedical applications. © 2024 The Royal Society of Chemistry.

关键词： Biomolecules

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Cancer Cytoplasm Segmentation from Cell Image Based on Transfer Learning 24

Cancer Cytoplasm Segmentation from Cell Image Based on Trans...

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6th International Conference on Image, Video and Signal Processing, IVSP 2024

作者： Sultana, Rebeka Horibe, Hibiki Murakami, Tomoaki Shimizu, Ikuko Institute Of Global Innovation Research Tokyo University Of Agriculture And Technology Tokyo Japan Institute Of Agriculture Division Of Animal Life Science Tokyo University Of Agriculture And Technology Tokyo Japan Institute Of Engineering Division Of Advanced Information Technology & Computer Science Tokyo University Of Agriculture And Technology Tokyo Japan

ISBN: (纸本)9798400716829

Image segmentation is one of the most popular approaches used in digital pathology. Most existing deep learning-based image segmentation models extract nuclear from the background in cell images. The nuclear of cancer cell images are irregular in shape and size, with unclear borders. Detecting cancer regions from cell images for detailed analysis depending on the diagnosis's purpose, including other regions, is also necessary. Because of the characteristics of cancer cell images, conventional segmentation models trained on existing datasets targeted to extract cell regions only fail to detect cancer cytoplasm in cell images. Therefore, in this research, H&E stained cancer cell images are labeled manually, and segmentation is performed by transfer learning using a conventional method to segment cancer cytoplasm in cell images. Experimental results confirm that the IoU and dice coefficient are improved by 14% and 2%, respectively, compared to the method without transfer learning. © 2024 Owner/Author.

关键词： Image segmentation

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An Extensive Overview of anamoly Detection Analysis in FL-Based IDS

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Procedia computer science 2025年 259卷 326-335页

作者： Himani Tyagi Abhilasha Singh Research Scholar Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi- NCR Campus Ghaziabad Associate Professor Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi- NCR Campus Ghaziabad

In this paper, we present a Federated Learning-based Intrusion Detection System (FL-IDS) aimed at enhancing security in Connected Autonomous Vehicles (CAVs) within IoT environments. The novelty lies in the dual utilization of Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) for anomaly detection, leveraging the Flower framework for privacy-preserving, decentralized model training. Unlike traditional centralized approaches, FL-IDS ensures data confidentiality by sharing only model updates. Using the NSL-KDD dataset, our evaluation highlights superior accuracy and loss metrics, validating the efficiency of both machine learning and deep learning methods for detecting DDoS, Probe, and U2R attacks. This work contributes to the advancement of secure and efficient vehicular communication systems, providing scalable solutions for real-world IoT deployments.

关键词： Federated Learning Deep Learning Machine learning CAV IDS Edge Computing

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Formation Control of Heterogeneous Multi-Agent Systems Under Fixed and Switching Hierarchies

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IEEE Access 2024年 12卷 97868-97882页

作者： Shamrooz Aslam, Muhammad Bilal, Hazrat Chang, Wen-Jer Yahya, Abid Anjum Badruddin, Irfan Kamangar, Sarfaraz Hussien, Mohamed China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221116 China University of Science and Technology of China Department of Automation Hefei230026 China Department of Marine Engineering Keelung202 Taiwan Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana King Khalid University College of Engineering Department of Mechanical Engineering Abha Saudi Arabia King Khalid University Faculty of Science Department of Chemistry Abha61413 Saudi Arabia

Modern information technology is driving the rapid development of the information and automation industry through the combination of computer technology, communication technology, and control technology. Inspired by nature's cluster movement, such as birds, people proposed the concept of Multi-Agent Systems (MASs). This paper deals with the formation control problem for a class of heterogeneous multi-agent systems with time-delays. First, the formation control of heterogeneous multi-agent systems with communication delay in fixed and switching topologies is investigated. A heterogeneous multi-agent system model with input delay and communication delay is established. Furthermore, based on this model, the formation control protocol is designed and the stability of the proposed control protocol in fixed and switched topologies is analyzed. The first- and second-order multi-agent control system was formulated for both fixed and switched topology, respectively. Then, using the new LMI toolbox, the feasibility of the parameters is obtained and the example is created by MATLAB. The simulation results show that the proposed formation control protocol with delay in this paper can make the systems converge to the expected value under fixed and switched hierarchies. © 2013 IEEE.

关键词： Topology

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Prediction of Heart Disease Using Machine Learning Algorithms 2

Prediction of Heart Disease Using Machine Learning Algorithm...

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

作者： Burle, Rushikesh Gaurkhede, Sanskruti Dewangan, Leelkanth Pacharaney, Utkarsha Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Computer Science and Design Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Data Science Faculty of Engineering and Technology Maharashtra Wardha442001 India

ISBN: (纸本)9798350360523

In India, heart disease claims the lives of about one person every day. The human body's most vital organ is the heart. The heart has two primary purposes: first, it gathers blood from body tissues and pumps it to the lungs;second, it gathers blood from the lungs and pumps it to all body *** disease has claimed the lives of many people. Predicting that disease at the appropriate moment is crucial, for this reason. Making use of machine learning Additionally, illnesses are easily identified and anticipated. It also covers the use of many algorithms on the Heart Disease Dataset. The dataset used in this research was obtained from Kaggle. Analyzing fundamental machine learning algorithms for the prediction of heart disease, such as Random Forest, K-Nearest Neighbours, Decision Tree Classifier, and Support Vector Machine, is the primary goal of this study. Our Acquired Model for The Decision Tree Classifier has a score of 79.0%. And the Random Forest Classifier has an 84.0% score. With a linear kernel, the Support Vector Classifier scores 83.0%. Next, K Neighbours Classifier~ outperformed all previously employed methods, achieving the maximum score of {87%} with ~8 nearest result. RF (Random Forest), K-Nearest Neighbours Algorithm, and Decision Tree, together with Python programming and Visual Studio, which excels at both data and code. © 2024 IEEE.

关键词： Heart

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Interference Cancellation by Using Viterbi Algorithm for Space Base AIS System 2nd

Interference Cancellation by Using Viterbi Algorithm for Spa...

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2nd International Conference on Signal and Data Processing, ICSDP 2022

作者： Parmar, Naina Darji, Mohini Trivedi, Dhruvi Department of Computer Science and Engineering Devang Patel Institute of Advance Technology and Research Charotar University of Science and Technology Gujarat Changa India Charotar University of Science and Technology Gujarat Changa India

ISBN: (纸本)9789819914098

Automatic Identification systems are becoming more popular and more likely to be incorporated Collison minimization in navigational frame-works. The recently developed satellite automatic identification system outperforms the previously used Terrestrial Automatic Identification system in terms of precision. GMSK is used to govern the message in a space-based automatic identification system. Over a self-organized TDMA channel, the regulated message of the automatic identification system (AIS) is then sent and received among boats and satellite Automatic ID Systems. Due to message cover, the conventional single-axis spacecraft Automatic ID System receiver failed to decode the message precisely. In this paper, the implementation of an interference cancellation method corrects the covered AIS message and decodes the obtained data. Very high-speed description language is used to design the receiver, transmitter, and interference cancellation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Time division multiple access

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ML for Sustainable Solutions: Applications in Renewable Energy Optimization and Climate Change Prediction 4

ML for Sustainable Solutions: Applications in Renewable Ener...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Awachat, Arya Dube, Ananya Chaudhri, Shivnath Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

This paper delves into the transformative potential of ML in addressing some of the world's most pressing challenges in optimizing renewable energy and mitigating climate change. How ML algorithms work to improve efficiency within solar and wind energy systems, smart grids, and energy storage technologies is revealed by large data sets and predictive analytics. As ML improves the forecasting of energy, minimizes waste, and allows for better grid integration, its contribution to cutting carbon emissions significantly is immense. In addition, it facilitates climate adaptation by accurately predicting extreme weather occurrences and climate variability. Challenges facing the approach include data paucity, computational cost, and integration with older systems. Interdisciplinary collaboration and newer ML models tackle these challenges well. The study recognizes the significant role of machine learning in creating a sustainable environment and notes its ability to actually reconcile environmental, economic, and social objectives toward a robust and greener future. © 2025 IEEE.

关键词： Clean energy

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Facial expression recognition in videos using hybrid CNN & ConvLSTM

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International Journal of Information technology (Singapore) 2023年第4期15卷 1819-1830页

作者： Singh, Rajesh Saurav, Sumeet Kumar, Tarun Saini, Ravi Vohra, Anil Singh, Sanjay Department of Electronic Science Kurukshetra University Kurukshetra India CSIR-Central Electronics Engineering Research Institute Pilani 333031 India Department of Computer Science Birla-Institute of Technology and Science Pilani 333031 India

The three-dimensional convolutional neural network (3D-CNN) and long short-term memory (LSTM) have consistently outperformed many approaches in video-based facial expression recognition (VFER). The image is unrolled to a one-dimensional vector by the vanilla version of the fully-connected LSTM (FC-LSTM), which leads to the loss of crucial spatial information. Convolutional LSTM (ConvLSTM) overcomes this limitation by performing LSTM operations in convolutions without unrolling, thus retaining useful spatial information. Motivated by this, in this paper, we propose a neural network architecture that consists of a blend of 3D-CNN and ConvLSTM for VFER. The proposed hybrid architecture captures spatiotemporal information from the video sequences of emotions and attains competitive accuracy on three FER datasets open to the public, namely the SAVEE, CK + , and AFEW. The experimental results demonstrate excellent performance without external emotional data with the added advantage of having a simple model with fewer parameters. Moreover, unlike the state-of-the-art deep learning models, our designed FER pipeline improves execution speed by many factors while achieving competitive recognition accuracy. Hence, the proposed FER pipeline is an appropriate candidate for recognizing facial expressions on resource-limited embedded platforms for real-time applications. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： 3D convolutional neural networks (3D-CNN) Convolutional LSTM (ConvLSTM) Long short-term memory (LSTM) Video-based facial expression recognition (VFER)

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An Anonymous IoT-Based E-Health Monitoring System Using Blockchain technology

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IEEE Systems Journal 2023年第2期17卷 2422-2433页

作者： Samuel, Omaji Omojo, Akogwu Blessing Mohsin, Syed Muhammad Tiwari, Prayag Gupta, Deepak Band, Shahab S. Department of Computer Science Osara264103 Nigeria Edo State University Uzairue300281 Nigeria Shestco Applied Mathematics and Simulation Advanced Research Centre Abuja186 Nigeria Comsats University Islamabad Department of Computer Science Islamabad45550 Pakistan Aalto University Department of Computer Science Espoo02150 Finland Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology Delhi110086 India Research Advisor Ucrd Chandigarh University GharuanPunjab Mohali140413 India National Yunlin University of Science and Technology Future Technology Research Center Douliou64002 Taiwan

The Internet of Things (IoT) has made it possible for health institutions to have remote diagnosis, reliable, preventive, and real-time decision-making. However, the anonymity and privacy of patients are not considered in IoT. Therefore, this article proposes a blockchain-based anonymous system, known as GarliMediChain, for providing anonymity and privacy during COVID-19 information sharing. In GarliMediChain, garlic routing and blockchain are integrated to provide low-latency communication, privacy, anonymity, trust, and security. Also, COVID-19 information is encrypted multiple times before transmitting to a series of nodes in the network. To ensure that COVID-19 information is successfully shared, a blockchain-based coalition system is proposed. The coalition system enables health institutions to share information while maximizing their payoffs. In addition, each institution uses the proposed fictitious play to study the strategies of others in order to update its belief by selecting the best responses from them. Furthermore, simulation results show that the proposed system is resistant to security-related attacks and is robust, efficient, and adaptive. From the results, the proposed proof-of-epidemiology-of-interest consensus protocol has 15.93% less computational cost than 26.30% of proof-of-work and 57.77% proof-of-authority consensus protocol, respectively. Nonetheless, the proposed GarliMediChain system promotes global collaborations by combining existing anonymity and trust solutions with the support of blockchain technology. © 2007-2012 IEEE.

关键词： COVID-19

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