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Soft computing and the Applications based on Machine Learning for the Healthcare Organization

Soft Computing and the Applications based on Machine Learnin...

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international conference for convergence of technology (I2CT)

作者： Devvret Verma Poonam Verma Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computing Graphic Era Hill University Dehradun Uttarakhand India

The soft computing process is an important factor that plays an impactful role in the health care organization. Additionally, the impact of the machine learning process has to be generated within the healthcare system. The approximate application for the health care organization has to be determined and it helps to improve the structure of the health care institution. Solving different kinds of problems have to be evaluated within the healthcare administration. Therefore, different topic-related objectives have to be evaluated in this research article. Moreover, the methods that are used in this article have to be analyzed within this portion. Additionally, detailed information about the soft computing process and its role within the health care administration has to be evaluated. Moreover, the positive impact of the machine learning process has to be determined within the organization.

关键词： Medical services Organizations Machine learning Medical diagnostic imaging convergence

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The Detection of Fake News through the use of a Hierarchical Convolutional Attention Network 8

The Detection of Fake News through the use of a Hierarchical...

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8th IEEE international conference for convergence in technology, I2CT 2023

作者： De Silva, Chameera Halloluwa, Thilina University of Colombo School of Computing Colombo Sri Lanka

ISBN: (纸本)9798350334012

In the current digital era, the popularity of social media platforms has expanded tremendously. Every one of us, no matter what age or gender or class we belong to, are active on social media, sharing so much of our personal and professional life with the world. We are connected with people, countries and borders apart via our phone now. News companies are also recognizing this change in the world and are now making business through this by publishing information online. The rapid spread of an article on the internet by the normal public without checking for its authenticity is resulting in a surge in the amount of incorrect information available online. No end-user finds it simple to confirm the veracity of a given news article. These fake articles are increasing rapidly and making it far more difficult for the real information to reach the public. The purpose of this work is to develop an efficient model for assessing the credibility of a news article. This work addresses the classification problem of Fake News, given just the news article's content and the author's name. The system proposed in this paper works with Hierarchical Convolutional-Attention Network (HCAN) and Convolutional Neural Network to classify the fake news articles based on the words and phrases used in the published piece. Hierarchical Convolutional-Attention Network (HCAN) is comprised of attention-enhanced word-level and sentence-level encoders as well as a CNN to capture sequential correlation. We have also used Recurrent Neural Network (RNN) for word encoder and sentence encoder in our system. We carried out the experiment on our system using a dataset and compared the results with various baseline models like CNN, RCNN, BERT or LR. Extensive trials demonstrate that HCAN outperforms the most advanced baseline models on a collected dataset. Results of our experiment shows that our model is achieving the F1 score of 0.98. © 2023 IEEE.

关键词： Convolutional neural networks

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Attack Detection Scheme based on Blackmailing nodes using Adaptive Tunicate Swarm Algorithm in MANET-IoT Environment

Attack Detection Scheme based on Blackmailing nodes using Ad...

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2023 international conference on Sustainable computing and Smart Systems, ICSCSS 2023

作者： Varaprasad, R. Nagasai Mohan, C. Rajesh, B. Rohith Kumar, C. Sainath Chowdary, C. Mahesh Babu, B. G.Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Kurnool India

ISBN: (纸本)9798350333602

When combined with the IoT, the capabilities of a MANET greatly enhance data acquisition, processing, and sensing. Although many current efforts leverage MANET-IoT and achieve improved outcomes, these approaches have drawbacks like unreliable connectivity due to data integrity problems, scalability challenges, and excessive energy usage. The proposed model focuses on the potential for disruption in the IoT network due to cooperative assaults at the edge nodes and the many ways in which this may occur. An IoT network may be subject to an internal co-operative assault, in which several devices cooperate together to launch a successful cyberattack. Defenses against cooperative assaults cannot be made at the granular level. As a result, with the aid of Edge computing, a trustworthy, optimization-based environment is created to lessen security risks. The drive of this research is to deliver a Tunicate Swarm Algorithm (TSA) based adaptive metaheuristic algorithm for efficiently solving global optimization problems and locating the malicious nodes in the environment. In each iteration, the proposed Adaptive Tunicate Swarm Algorithm (ATSA) performs two primary tasks: (1) searching the whole search space with a arbitrarily designated tunicate, and (2) refining the search with the location of the finest tunicate. The procedure's examination capacity is enhanced, and it is protected against premature convergence, thanks to this tweak. Because of how reliable the setting is, any hostile actors or coordinated assaults may be quickly identified and stopped. The typical overhead associated with connecting to the cloud will be reduced, as will any resulting delays. The existence of malicious nodes in an IoT network is avoided and isolated using an ATSA-based strategy. At the end, the effectiveness of the suggested trustworthy environment was verified. © 2023 IEEE.

关键词： Internet of things

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Software Testing Combining the Fusion of Surrogate Model and Evolutionary Algorithm

Software Testing Combining the Fusion of Surrogate Model and...

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2023 international conference on Cognitive computing and Complex Data, ICCD 2023

作者： Dang, Xiangying Bao, Wenzheng Qu, Qian Li, Dan Xuzhou University of Technology School of Information Engineering School of Big Data Xuzhou China Yancheng Institute of Technology School of Information Engineering Yancheng China

ISBN: (纸本)9798350306293

The primary challenge is the extensive number of iterations required to converge on suitable solutions in using Genetic Algorithms (GA) for test data generation. Each iteration involves executing candidate solutions and evaluating their fitness values, resulting in prolonged time consumption. This limitation can impede the scalability and efficiency of the evolutionary algorithm, particularly in large-scale or time-critical software testing scenarios. Addressing this issue is crucial for enhancing test data generation efficiency. The method we proposed to mitigate this problem is by fusing surrogate models that approximate the fitness values of candidate solutions instead of executing them on the actual software system. The proposed method typically begins with initial candidate solutions (test data), which along with their fitness values, can be used as a training set to develop the surrogate model. Additional samples may be collected during the evolutionary process to enhance the model's accuracy. Subsequently, the surrogate model is employed to evaluate the fitness values of new candidate solutions. Solutions with high predicted fitness values are then selected as the initial population to execute the program and undergo the genetic operations. The preliminary experimental results show that by utilizing surrogate models, the need for numerous evaluations on the actual system can be significantly reduced, enabling faster convergence and improved efficiency in test data generation. © 2023 IEEE.

关键词： Software testing

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Digital Infrastructure Pivots on Silicon Photonics: An Aspect from the Past, Present, and Future of Optical Communications

Digital Infrastructure Pivots on Silicon Photonics: An Aspec...

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IEEE international conference on Group IV Photonics

作者： Shu Namiki Platform Photonics Research Center The National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Japan

This plenary talk will address that the era of photonics-electronics convergence is beginning from the historical viewpoint of optical communication. It will then describe the activities of AIST, aiming at the realiza... 详细信息

ISBN: (数字)9798350394047

ISBN: (纸本)9798350394054

关键词： Optical switches Optical fiber networks Silicon photonics convergence

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Hybrid particle swarm optimization algorithm for traveling salesman problem based on ternary optical computer 4

Hybrid particle swarm optimization algorithm for traveling s...

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4th international conference on Machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Sun, Shaojiang Tong, Yihui Qi, Baofeng Wang, Zhehe Wang, Xianchao School of Computer and Information Engineering Fuyang Normal University Fuyang236600 China School of Computer Science and Technology Hainan Tropical Ocean University Sanya572000 China

In recent years, as a novel computer, the ternary optical computer (TOC) has attracted many attentions because of its own mega-parallelism. And with the development of optical computing technology, the exploration of applications based on TOC is also advancing. Travel Salesman problem (TSP) is a classical combinatorial optimization problem, which is also a NP problem. How to solve this problem quickly and efficiently is the major research direction. Particle swarm optimization algorithm (PSO) are prone to premature convergence and insufficient local search capability. To deal with the shortcomings of the problem, we promoted a hybrid algorithm that the hybrid particle swarm optimization algorithm (HPSO) to solve the problem. By introducing greedy strategy, the algorithm generates excellent populations. And the improved Metropolis criterion is added to individual learning and population learning to accept inferior solutions with certain probability, which can effectively increase the population diversity and the mutation adopts the 2-opt optimization strategy, which can solve the path crossing problem effectively, enhance the local optimization capability of the algorithm, and find higher quality solutions. At last, the results show that HPSO solves the TSP with fast convergence speed and high quality of the optimal solution. © 2024 The Authors.

关键词： Particle swarm optimization (PSO)

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4th international conference on Recent Trends in Advanced computing - Computer Vision and Machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

4th International Conference on Recent Trends in Advanced Co...

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4th international conference on Recent Trends in Advanced computing - Computer Vision and Machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

ISBN: (纸本)9789811971686

The proceedings contain 30 papers. The special focus in this conference is on Recent Trends in Advanced computing - Computer Vision and Machine Intelligence Paradigms for Sustainable Development Goals. The topics include: Advanced Algorithmic Techniques for Topic Prediction and Recommendation—An Analysis;Implementation of an Automatic EEG Feature Extraction with Gated Recurrent Neural Network for Emotion Recognition;high Performance Classifier for Brain Tumor Detection Using Capsule Neural Network;Mining Suitable Symptoms to Identify Disease Using Apriori and NBC;background Features-Based Novel Visual Ego-Motion Estimation;livspecs: Design and Implementation of Smart Specs for Hearing and Visually Challenged Persons;Self-balancing Robot Using Arduino and PID Controller;a Survey Based on Online Voting System Using Blockchain technology;convergence Perceptual Model for computing Time Series Data on Fog Environment;survey on Collaborative Filtering Technique for Recommender System Using Deep Learning;a Survey on Power Consumption Indicator Using Machine Learning-Based Approach;a Novel Hand Gesture Recognition for Aphonic People Using Convolutional Neural Network;comprehensive Analysis of Defect Detection Through Image Processing and Machine Learning for Photovoltaic Panels;covid Analysis Prediction Using Densenet Method in Deep Learning;Feature Extraction Based on GLCM and GLRM Methods on COVID-19 Dataset;memory Augmented Distributed Monte Carlo Tree Search Algorithm-Based Content Popularity Aware Content Recommendation Using Content Centric Networks;Enhancing Protection Against Scalper Bots with ML;An Enhanced Optimized Abstractive Text Summarization Traditional Approach Employing Multi-layered Attentional Stacked LSTM with the Attention RNN;implementation of Machine Learning Methods on Data to Analyze Emotional Health;SMS Spam Classification Using PSO-C4.5.

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Improved Q-Learning Algorithm for Path Planning of an Automated Guided Vehicle (AGV) 5

Improved Q-Learning Algorithm for Path Planning of an Automa...

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5th IEEE international conference on Artificial Intelligence in Engineering and technology, IICAIET 2023

作者： Guo, Hao Tan, Min Keng Lim, Kit Guan Ee Chuo, Helen Sin Yang, Baojian Kin Teo, Kenneth Tze Universiti Malaysia Sabah Modelling Simulation & Computing Laboratory Faculty of Engineering Kota Kinabalu Malaysia Baotou Light Industry Vocational and Technical College Department of Automation Baotou City China Wuyi University Department of Intelligent Manufacturing Jiangmen China

ISBN: (纸本)9798350304152

With the development of modern industry 4.0, intelligent path planning is an essential research direction of schedule systems for an automated guided vehicle (AGV), which has been widely used in logistics distribution centers of enterprises. This work implements the improved Q-learning algorithm to solve the typical obstacle avoidance problems in path planning. Specifically, the conventional Q-learning algorithm has shortcomings including low operational efficiency and slow learning speed. The improved Q-learning algorithm is successively proposed by adding a learning process based on the original Q-learning algorithm, which enables AGV to find obstacles and target locations within the shortest time, thus improving the efficiency of path planning. Finally, the simulation experiments are carried out in the grid environment with MATLAB. In comparison to the conventional Q-learning algorithm, the improved Q-learning algorithm has faster convergence and higher learning efficiency, improved by 20%. © 2023 IEEE.

关键词： Motion planning

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FNCS: Federated Learning Strategy Based on Cosine Similarity under Resource Constraints 23

FNCS: Federated Learning Strategy Based on Cosine Similarity...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Li, Ruonan Qin, Yang Zang, Lu College of Computer Science and Technology Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9781665494571

Federated learning has been widely applied in healthcare services and real-time object tracking. However, limited by communication resources, such as the server bandwidth, and the impact of client data heterogeneity, the convergence rates and accuracy of federated learning significantly drop. Hence, this study proposes a novel federated normalization learning strategy based on cosine similarity (FNCS). Starting from a new perspective of the relationship between local and global updates of the model, FNCS selects valuable clients to upload updates using cosine similarity. The regularization term is then inserted in the last layer of clients by utilizing cosine distance-based update divergence. Numerous experiments are conducted in PyTorch for accelerated validation. Results show that the high accuracy experiments are conducted on the complex CelebA dataset, and the communication rounds of FNCS are improved by 44.71% and 41.98% compared with FedAvg and FedProx, respectively. © 2021 IEEE.

关键词： Smart cities Wireless networks Medical services Collaborative work Real-time systems Servers Object tracking

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Fuzzy rule-based grey wolf(GW-FIS) in Wireless Body Area Networks 8

Fuzzy rule-based grey wolf(GW-FIS) in Wireless Body Area Net...

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8th IEEE international conference for convergence in technology, I2CT 2023

作者： Sharma, Poonam Saini, Kamaljit Singh Sidhu, Prabhjot Kaur Chandigarh University University Institute of Computing Punjab Mohali India MSIT IT Department GGSIP University New Delhi India

ISBN: (纸本)9798350334012

Wireless body area networks are crucial in many fields, including healthcare, education, and others. Congestion Control Problem is the main issue facing the design of Wireless Body Area Networks. Inconsistent traffic loads, power restrictions, bandwidth restrictions, and many-to-one information architecture all contribute to significant congestion problems in WBAN, which can be managed with effective path ***, this paper proposed Grey Wolf-Fuzzy Inference System (GW-FIS) appproach that utilizes the hunting mechanism of grey wolves and a rules-based approach of fuzzy to attain congestion-aware transmission among wireless body area networks. The hunting mechanism of grey wolves says that in each iteration, omega wolves update the position, and here omega wolves represent the highly congested area and then update the rate of transmission for that node. Moreover, the fuzzy rule defines the congestion level with the randomly distributed grey *** proposed approach select the best path with lowest congestion level to data transmission in WBANs. The proposed approach is simulated using a Network simulator, and performance is computed based on two different scenarios in terms of total energy consumption (TEC), packet delivery ratio (PDR), and end-to-end delay (E2D). The simulated results of first scenario that is based on number of connection are compared with the existing algorithms. It is found that the performance of proposed protocol is improved by 38.6%,6% and 49.5% from GWO respectively in terms of TEC,PDR and E2D. Secondly proposed mechanism are compared with existing mechanism based on second scenario that is speed of nodes and results shows that proposed protocol improved the performance by 47%,6% and 63% from GWO respectively in trem of TEC,PDR and E2D. Though PDR is not much improved still results clearly show that the proposed approach's calculate the best path for data transmission with low congestion level. © 2023 IEEE.

关键词： Energy utilization

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