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共 1031 条记录，以下是121-130 订阅

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Transforming IoT with Federated Multimodal Learning: A Privacy-Centric Approach

Transforming IoT with Federated Multimodal Learning: A Priva...

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Intelligent Technologies (CONIT), international conference on

作者： Ankita Suryavanshi Shiva Mehta Mukesh Kumar Siddhant thapliyal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Suppliers face significant challenges when discussing cloud-based health apps, such as implementing highly secure procedures and rigorous privacy safeguards. this applies to the fact that the acceptance of these applications is limited by the need for such quality of service, which is achieved mainly by reducing latency. this work is devoted to tackling these hurdles through the cutting-edge application of edge computing and model-federated learning aspects. Federated learning, a complex and distributed paradigm for machine learning helps perform computations near the data source and overcomes the constraint problem of a distant data source. Integrating the newest edge computing, our system includes an intelligent instrument that is applied to extract clinical data using Clustered Federated Learning (CFL). the value of this framework is evaluated using the performance metrics of two benchmark datasets, one of which represents different kinds of problems that occur in the day-to-day life of people, which concludes the effectiveness and significance of implementing machine learning at the edge in the health care sector the presented results above strongly suggest the fact that CFL promotes this model results in improvement. the performance indicators of the CFL model showed an appraisal boost with the increments in X-ray and Ultrasound datasets by 16% and 11%, respectively, when compared to those trained conventionally. Hence, edge AI through CFL can achieve the same tasks, and the possible scenarios can also be remarkably superior compared to a centralized data system.

关键词： COVID-19 Training Ultrasonic imaging Federated learning soft sensors Medical services Quality of service

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Robust Statistical Models for Identifying Inauthentic Online Reviews

Robust Statistical Models for Identifying Inauthentic Online...

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soft computing for Security Applications (ICSCSA), international conference on

作者： D.Swapna M.Shanmuga Sundari G.Khadhyothi Sreeja S.Sriya Varma M.Rajeshwari Department of Computer Science and Engineering BVRIT HYDERABAD College of Engineering for Women Hyderabad India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Review authenticity is a key factor in determining trustworthiness in the internet marketplace. However, the threat of fake reviews remains constant, endangering both fair competition and customer confidence. the rise of fake reviews undermines the credibility of these platforms and poses a significant challenge to users seeking genuine feedback. In response, this study proposes a machine learning (ML) approach for detecting fake *** research begins by collecting a dataset comprising both genuine and fake reviews from various online platforms. Features are extracted from the textual content of these reviews, including linguistic characteristics, sentiment analysis, and meta-data such as review length and rating distribution. Several ML models are employed for classification, such as Naive Bayes, Random Forest, and Support Vector Machines. these algorithms are assessed using measures like accuracy, precision, recall, and F1-score after being trained on the dataset. In addition, methods like hyperparameter tuning and cross-validation are used to improve the performance of ***, the findings suggest that ML-based approaches hold promise for mitigating the problem of fake reviews, thereby enhancing the reliability of online review platforms and empowering consumers to make informed decisions.

关键词： Training Support vector machines Sentiment analysis Reviews Biological system modeling Feature extraction Security Fake news Usability Tuning

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Autonomous Robot Navigation in Unknown Terrains using Generative Adversarial Networks

Autonomous Robot Navigation in Unknown Terrains using Genera...

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soft computing for Security Applications (ICSCSA), international conference on

作者： P Chitra K Sridar Sudha Govindan Britto Raj S Akshya. J Mani Deepak Choudhry M. Sundarrajan Department of Artificial Intelligence and Data Science St Joseph's Institute of Technology Chennai Department of Computer Science and Engineering Veerammal Engineering College Dindigul Department of Computer Science and Engineering Sona College of Technology Salem Department of Computer Science and Engineering RRASE College Of Engineering Chennai Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Autonomous Robot Navigation in unknown and dynamic environments is considered one of the grand challenges in robotics. these motions that guide it span from rescue operations to planetary exploration. Classical methods for path planning, including A*, Dijkstra, and RRT, were unable to balance the efficiency and adaptability of motion planning over complex terrain. this may have a powerful solution using Generative Adversarial Networks (GANs) with advanced machine learning techniques to improve the ability of robotic navigation. this study proposes a GAN-based approach for designing an effective self-adaptive path planning and obstacle avoidance system for autonomous robots. Despite performing well in structured environments, traditional techniques normally fail in analysing the unstructured terrains with real-time adjustments and dynamic changes due to the high computational costs and lack of flexibility involved. In this regard, the proposed GAN-based system resolves these issues by rating navigation paths concerning safety and efficiency. Quantitative analysis demonstrates that within a forest, desert, urban, or off-road environment, the computational times by using the developed strategy turn out to be 0.12 seconds, 0.10 seconds, 0.14 seconds, and 0.13 seconds, respectively, behaving much lower than what can be achieved with other algorithms. this approach proves much superior in terms of computational efficiency path smoothness and adaptability under different environmental conditions in applications related to autonomous robot navigation systems.

关键词： Navigation Statistical analysis Machine learning Generative adversarial networks Path planning Real-time systems Generators Safety Computational efficiency Autonomous robots

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Using Reinforcement Learning Algorithms to Explore COVID-19 Spread in South Africa 4

Using Reinforcement Learning Algorithms to Explore COVID-19 ...

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4th international conference on Artificial Intelligence, Big data, computing and data Communication Systems, icABCD 2021

作者： Hanie, Ruan Le Van Rensburg, Jt Janse School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa

ISBN: (纸本)9781728185927

Many learning opportunities for machine learning (ML) exist within the context of how viruses and their spread can be combatted. If the agents can be trained to demonstrate optimal behaviour in a pandemic, their actions can possibly be replicated to improve spread in a real-life scenario. the aim of this research is to train reinforcement learning (RL) agents to survive in a rule-based AI environment that simulates the spread of COVID-19 in South Africa. the RL agents used in the training environment were created using Unity's ML agent SDK. the ML agent SDK supports the usage of two RL-specific algorithms, Proximal Policy Optimization (PPO), and soft Actor-Critic (SAC). this study contributes to the AI space by providing insight into how a virus and its interaction with a population can be modelled using Unity and machine learning. the agents were able to combat COVID-19 effectively and did so by self-Training how to maintain social distance and have regular check-ups at the hospital. It was also observed that susceptible agents pay frequent visits to the hospital without ever being rewarded for doing so. the code will be open-sourced to the Unity machine learning agent's SDK community and discords. © 2021 IEEE.

关键词： COVID-19

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Enhancing Lifetime in Wireless Sensor Networks through Image Processing-Based Clustering with Genetic Algorithm Routing

Enhancing Lifetime in Wireless Sensor Networks through Image...

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soft computing for Security Applications (ICSCSA), international conference on

作者： M. Ilampari M. Amina Begum N Mahesh Babu S. Gopi M. Mythreyee Shanmugavel Deivasigamani Saveetha School of Law SIMATS Department of ECE Oxford College of Engineering Chennai India Department of Information Technology Anurag Engineering College Suryapet Kodad Telangana India Department of CSE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Department of EEE Dr. MGR Educational and Research Institute Chennai Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Chennai India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Wireless Sensor Networks (WSNs) are a very important technology widely used in diverse application domains of environmental monitoring, disaster management, health care, industrial automation etc. these are networks in which spatially distributed sensor nodes monitor physical or environmental conditions and communications data are processed through a base station. Here, we introduced a novel method for enhancing the efficiency and lifetime of WSNs by using the WSN to process images data and genetic programming (GA) to find the best route. A system model was developed to balance a network consisting of homogenous sensor nodes placed randomly in a particular geographical area, representing realistic scenarios. Each sensor node has limited energy resources, hence the operation of the network was partitioned into clustering phase and routing phase to optimal utilization of energy. To improve both the accuracy and efficiency of clustering, the genetic algorithm then identified the optimal path through these clusters using the best combination of pivot nodes for transmission. the proposed method applies image processing techniques to process sensor data, preprocesses sensor data, and then improves the classification process, clustering results, and the genetic algorithm obtains the more energy-efficient paths. Our extensive evaluation results prove substantial optimizations in network performance, energy efficiency and lifetime over known protocols. this method is expected to be effective in such applications as environmental monitoring, disaster management, and other applications that will utilize reliable and sustainable WSNs.

关键词： Wireless sensor networks Protocols Image processing Disaster management Routing Energy efficiency Environmental monitoring Security Reliability Genetic algorithms

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An Empirical Approach of Developing Domain Based Ontology in the Field of Bioinformatics

An Empirical Approach of Developing Domain Based Ontology in...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Devanolla Suresh E.Afreen Banu R. Karthikeyan Pramod Kumar Naik Safaa Hallol Mohammed Tareq Hafdhi Abdtawfeeq Department of Computer Science & Technology Madanapalle Institute of Technology & Science Madanapalle CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Department of CSE Artificial Intelligence and Data Analytics Sri Ramachandra Institute of Higher Education and Research Department of CSE Dayananda Sagar University Bengaluru Collage of Pharmacy National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

From a macro viewpoint, it is essential to comprehend the similarities across human illnesses in order to identify underlying mechanisms at the microscopic level. Previous studies have shown that employing machine learning methods or merging many data sources can improve performance in sickness similarity measurement. However, there is currently a gap in the development of a useful framework for information extraction and integration from a range of biological data using deep learning models. We introduce CoGO, a Contrastive learning system that predicts commonalities in illnesses based on gene networks and ontology structure. Gene ontology (GO) and gene interaction network domain information is integrated into CoGO using graph deep learning models. Graph deep learning models are first used to encode gene and GO word features from independent graph structure data. Gene and GO characteristics are then mapped via a nonlinear projection to a shared embedding space. Next, by using cross-view contrastive loss to increase the agreement of related gene-GO links, meaningful gene representations are generated. Finally, to calculate disease similarity, CoGO computes the cosine similarity of sickness representation vectors derived from related gene embeddings.

关键词： Deep learning Biological system modeling soft sensors Microscopy Merging Ontologies Information retrieval

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Hybrid Fusion Technology of Transportation Big data Based on Deep Learning 4th

Hybrid Fusion Technology of Transportation Big Data Based on...

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4th international conference on Innovative computing, IC 2021

作者： Xu, Xiaohui You, Hao School of Electronics and Internet of Things Chongqing College of Electronic Engineering Chongqing China

ISBN: (纸本)9789811642579

the early urban planning considered a single factor, and it cannot be adapted to the current complex urban development conditions. the rapid urban development speed has brought about problems such as traffic congestion and uneven distribution of transportation capacity. these problems have gradually become the bottleneck of urban development. In order to increase the speed of the city and alleviate traffic congestion, this paper proposes a big data hybrid integration technology. this technology can discover laws from massive data and extract them;through data mining, sorting, and analysis of different targets with massive data, it is found that big data hybrid fusion technology can provide effective decision-making reference for them. Taking a certain area of a city as a case scenario, using big data hybrid technology, we found that it perfectly solves the problems of traffic congestion and insufficient capacity encountered in traffic travel. © 2022, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Traffic congestion

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A Cloud-Edge Collaboration Framework for Cognitive Service 4

A Cloud-Edge Collaboration Framework for Cognitive Service

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4th international conference on Information Management and Machine Intelligence, ICIMMI 2022

作者： Reddy, Tanubuddi Srinivasa Annamalai, Suresh Department of Networking and Communications School of Computing College of Engineering and Technology Srm Institute of Science and Technology SRM Nagar Kattankulathur Chengalpattu Tamil Nadu Chennai603203 India

ISBN: (纸本)9781450399937

Cognitive Radio (CR) is a novel wireless communications paradigm that maximizes the use of available spectrum. Users of cognitive radio can only access the electromagnetic spectrum when the primary user is not making use of it. Spectrum sensing is a crucial part of cognitive radio since it locates unoccupied frequencies for use by CR clients. the local detection method has a number of drawbacks that make it less effective than cooperative spectrum sensing, such as the presence of noise uncertainty, channel uncertainty, the multipath fading effect, and the hidden terminal problem, but cooperative spectrum sensing has proven to be the best method for identifying the available spectrum. With cooperative spectrum sensing, data collected at individual nodes is shared with a centralized hub from which a final judgement can be made. Local sensing results, as well as the fusion rules utilized to combine local sensing findings, impacted the cooperative sensing's detection performance. For local spectrum sensing, we apply two different fusion methods to aggregate the collected data. Both conventional soft decision fusion (SDF) and conventional hard decision fusion (HDF) exist (HDF). In spite of its superior detection performance, conventional soft decision fusion's reporting channel control bandwidth need is too large. Optimization is a field that has many uses in engineering and research, and it relies heavily on mathematical modelling. Spectrum allocation is a challenging problem since it is Non-deterministic Polynomial (NP) in nature. Foraging behaviour, learning, memorizing, and sharing knowledge qualities in the bees have recently emerged as one of the most exciting topics of research in swarm intelligence. Recently, there has been a fad for studying honey bees. It has been proposed that the DRNN method can be used as a swarm intelligence solution to the optimization of mathematical test functions. this method of searching for things in one's immediate vicinity is novel.

关键词： Cognitive radio

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INCS: Design and Development of an Oral Cancer Identification Methodology based on Improved Neural Classification Scheme

INCS: Design and Development of an Oral Cancer Identificatio...

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soft computing for Security Applications (ICSCSA), international conference on

作者： Bhuvaneshwari Karthikeyan Reddi Khasim Shaik V. Balaji Vijayan A V. Allin Geo R. thiagarajan R. Krishnamoorthy Department of Dentistry Symbiosis Medical College for Women Pune India Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India Department of Information Science and Engineering HKBK College of Engineering Bengaluru India Department of Artificial Intelligence and Data Science St. Joseph’s Institute of Technology Chennai India Department of IT Prathyusha Engineering College Chennai India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

the early detection of oral cancer is critical to improving patient outcomes and survival rates. this paper presents the Improved Neural Classification Scheme (INCS) for identifying oral cancer using deep learning techniques. the proposed model integrates MobileNet and EfficientNet, two state-of-the-art convolutional neural networks (CNN), to create an ensemble capable of extracting both fine-grained and high-level semantic features from oral cancer images. the dataset used consists of 500 oral cancer images and 450 non-cancerous images sourced from Kaggle. A comprehensive preprocessing pipeline, including resizing, normalization, and data augmentation, was implemented to optimize the dataset for model training. the INCS model achieved an accuracy of 98.65%, outperforming other models such as MobileNet, EfficientNet, ResNet, and VGG16. the model also achieved high scores in precision (96.89%), recall (97.45%), and F1score (97.17%), demonstrating its robustness in distinguishing between cancerous and non-cancerous lesions. this high accuracy, combined with its low false positive and false negative rates, highlights the potential of INCS for clinical application in oral cancer diagnosis. Future work will focus on expanding the dataset and exploring real-time deployment in clinical settings.

关键词： Deep learning Training Accuracy Semantics Feature extraction Robustness Real-time systems Convolutional neural networks Security Cancer

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Comparison of PPO and SAC Algorithms towards Decision Making Strategies for Collision Avoidance among Multiple Autonomous Vehicles 7

Comparison of PPO and SAC Algorithms towards Decision Making...

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7th international conference on software Engineering and Computer Systems and 4th international conference on Computational science and Information Management, ICSECS-ICOCSIM 2021

作者： Muzahid, Abu Jafar Md Kamarulzaman, Syafiq Fauzi Rahman, Md Arafatur Universiti Malaysia Pahang Faculty of Computing Pahang26600 Malaysia Universiti Malaysia Pahang Faculty of Computing Fellow of Automotive Engineering Center Pahang26600 Malaysia University of Wolverhampton School of Mathematics and Computer Science Senior Lecturer United Kingdom

ISBN: (纸本)9781665414074

Multiple vehicle collision avoidance strategies with safe lane changing strategy for vehicle control using learning base technique are the most crucial concern in autonomous driving system. Statistics shows that the latest autonomous driving systems are usually prone to rear-end collision. Rear-end collisions often result in severe injuries as well as traffic jam and the consequences are much worse for multiple-vehicle collision. Many previous autonomous driving research focused solely on collision avoidance strategies for two consecutive vehicles. this study proposes a centralised control strategy for multiple vehicles using reinforcement learning focused on partner consideration and goal attainment. the system depicted as a group of vehicles are communicate and coordinate each others by a set of rays and maintain a short following move away. In order to address this challenge, a simulation was implemented in the Unity3D game engine and two state-of-the-art RL algorithms PPO (Proximal Policy Optimization) and SAC (soft Actor-Critic) were trained by an agent using Unity ML-Agents Toolkit. In terms of success rate, performance, training speed and stability two algorithms are comparable. the potency of algorithms has been assessed by the traffic flow (1) change in vehicle speed, (2) differ in the vehicle beginning positions, and (3) switch to next lane. the agent performed similarly at a 91% success rate in PPO or SAC applications © 2021 IEEE.

关键词： Reinforcement learning

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