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检索条件"任意字段=3rd International Conference on Computer Science and Computational Intelligence, ICCSCI 2018"

共 3188 条记录，以下是51-60 订阅

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Adaptive Weather Routing for Advanced Ship Navigation: A Comprehensive Review

Adaptive Weather Routing for Advanced Ship Navigation: A Com...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Iyyappan Venkatesan Dharanish M.B S. Muthumari Lakshmi S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Maritime navigation offers a comprehensive approach to optimising ship navigation, in such a way that guarantees the safety and effectiveness of the journey through multiple techniques, like the adaptive weather routing technique, which uses the predictive model and the current weather conditions for efficient navigation. The review mainly emphasizes the basic principles and computational simulation for sustainable navigation in advancing the route defining algorithm. Discusses about the conventional route defining algorithms and dynamic route defining algorithm by adjusting for wind, waves, and currents. A comparison analysis shows how well different algorithms work in various weather conditions, and case studies show how they are used in the actual world, while new trends are being discussed, major problems also concern the need for accurate climate predictions, high-speed data processing, and an efficient response plan. The Advancements in artificial intelligence and composite path forecasting also address issues related to major needs, such as accurate weather forecasts, high-speed data processing, and effective reaction tactics. Through this insights of this review provides on future directions for adaptive weather routing research and development that the sustainable growth of intelligent naval navigation is supported.

关键词： Accuracy Reviews Heuristic algorithms Predictive models Routing Prediction algorithms Satellite navigation systems Real-time systems Safety Meteorology

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Enhanced EEGNet Optimization Using Lightweight Deep Neural Network for Detecting Human Stress Levels from Raw EEG Signals Dataset

Enhanced EEGNet Optimization Using Lightweight Deep Neural N...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： Silabdi Hana Raini Hassan Ahmed Rimaz Faizabadi Abdullah Gubbi Mohammed Zakir Bellary V Manjula Department of Computer Science Kulliyyah of Information & Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Department of Computer Science Kulliyyah of Information & Communication Technology International Islamic University Kuala Lumpur Malaysia Centre for Unmanned Technologies International Islamic University Malaysia Kuala Lumpur Malaysia Department of Electronics and Communication Engineering Bearys Institute of Technolgy (VTU) Mangalore India Department of Artificial Intelligence and Machine learning P.A.College of Engineering(VTU) Mangalore India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Accurate stress detection is essential in neuroscience and healthcare since human stress levels have a substantial impact on general well-being. Although EEG-based stress detection is a popular non-invasive method, real-time applications are limited by the high processing costs of classic feature extraction techniques. This work removes the requirement for intricate feature extraction by presenting an efficient EEGNet framework for real-time stress prediction utilizing raw EEG signals. The suggested approach improves accuracy and efficiency by utilizing Z-score normalization and adjusting batch size. Compared to traditional CNNs, the lightweight EEGNet architecture's depth-wise and separable convolutions ensure computational efficiency by reducing trainable parameters by a factor of ten. The proposed method outperformed current EEGNet-based techniques in stress identification by 5% to 18%. Furthermore, the improved system efficiently handles raw EEG signals, which is suitable for real-time, resource-constrained scenarios. The results shows the effectiveness of model-centric optimization in deep learning and open the door to the creation of more effective stress detection techniques. This work provides a workable solution for real-time non-invasive stress monitoring, highlighting the significance of lightweight frameworks customized for particular applications.

关键词： Deep learning Accuracy Feature extraction Brain modeling Electroencephalography Real-time systems Data models Stress Optimization Monitoring

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Big Data Solutions for Predictive Healthcare Analytics: Enhancing Patient Outcomes and Operational Efficiency

Big Data Solutions for Predictive Healthcare Analytics: Enha...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

作者： Kumar, Nishi Kant Nandhini, J. Vinitha, S. Madhubala, P. Trimukhe, Dipmala Bhagwat Salunkhe, Kalpana Birla Institute of Technology Department of Computer Science and Engineering Mesra Off Campus Jharkhand Deoghar India Jai Shriram Engineering College Department of Ece Tirupur India Jerusalem College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Bharathiyar Institute of Engineering for Women Department of Computer Science and Engineering Salem India Government College of Engineering & Research Department of Instrumentation Avasari Khurd India School of Computational Sciences Jspm University Wagholi Pune India

ISBN: (纸本)9798331542375

The embracing of Big Data solutions in healthcare comes with widely prospect to enhance patient satisfaction and increase organizational effectiveness. The research aims to use Predictive techniques and models coupled with modern data integration systems to improve the health care services. The components of the suggested methodology are deep neural networks for patient monitoring and prognosis, wearable devices, and operational prognosis models for resources use. From the practice of this approach, we have noticed marked increase in the patient monitoring frequency, efficiency of timely intervention, and generalization of health care organization productivity factors such as wait time and other resource utilization. In addition, the Onshape plan carried measurable benefits for the patient results such as satisfaction, reduced mortality, improved chronic disease, and the lesser hospital readmission. The results emphasize that the opportunities that Big Data and predictive analytics offer for healthcare can be indeed immense - offering improved care and more effective outcomes at a lower total cost. This research focuses on how Big Data solutions can help create a better future of healthcare improving the clinical and operational activities in this sphere and, thus, the population's health. © 2025 IEEE.

关键词： Patient treatment

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Squared Fault and Biased Entropy for Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) Image Synthesis Using Firefly Algorithm 3rd

Squared Fault and Biased Entropy for Magnetic Resonance Imag...

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3rd international conference on Machine intelligence and Smart Systems, MISS 2023

作者： Abidi, Nazia Abbas Khalaf, Hameed Hassan Almulla, Ausama A. Hussein, Mustafa Asaad Jawad, Israa Abed Joint Step Consultants Pvt. Limited Jharkhand Ranchi834010 India Department of Medical Laboratories Technology Al-Manara College for Medical Sciences Maysan Iraq Department of Medical Laboratories Technology Al-Hadi University College Baghdad10011 Iraq College of Nursing National University of Science and Technology Dhi Qar Iraq Department of Medical Laboratories Technology AL-Nisour University College Baghdad Iraq

ISBN: (纸本)9783031691140

Multi-modal In recent years, medical image fusion has emerged as an all-encompassing analytic strategy that frequently makes use of a variety of various alteration methods. This trend began in the field of radiology. By integrating a large number of images obtained using a single or many imaging modalities, the purpose of multi-modal medical image fusion is to improve imaging quality while preserving certain properties of the images. Image processing, computer vision, pattern recognition, machine learning, and artificial intelligence are just a few of the many cutting-edge topics that can be categorised as part of the broader category of medical image fusion. In order to better understand the lesion, medical professionals have been making substantial use of a technique that combines a number of different medical imaging modalities. In this article, we present a framework for fusion that is based on a number of learning models that are known together as the firefly algorithm. The objective of this framework is to get rid of the squared error and optimise through weighted entropy. The performance of the suggested workbench is simulated in MATLAB and compared against approaches that are considered to be state-of-the-art in order to determine how effective it is. The peak signal-to-noise ratio, mean square error, correlation, and precision are some of the most important measures. The performance of the suggested workbench is superior according to all criteria, and it also decreases the amount of computational load. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： computerized tomography

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Chronic Kidney Disease: Data Clustering and Prediction Using Various Machine Learning Algorithms

Chronic Kidney Disease: Data Clustering and Prediction Using...

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Disruptive Technologies (ICDT), international conference on

作者： Gaurav Dhuriya Aradhna Saini Prashant Johri Neha Yadav Attiuttama Artificial Intelligence and Machine Learning G. L. Bajaj Institute of Technology and Management Greater Noida Delhi - NCR India Computer Science and Engineering G. L. Bajaj Institute of Technology and Management Greater Noida Delhi - NCR India Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

A major worldwide health concern, chronic kidney disease (CKD) has a high mortality and morbidity rate and can lead to several other illnesses. Since there are no overt signs of infection in the early stages of chronic kidney disease (CKD), patients frequently miss it. Early-stage chronic kidney disease (CKD) patients have a greater chance of receiving prompt therapy to stop the infection from spreading. AI models can successfully help physicians accomplish this aim because of their quick and precise recognition execution. In this work, we propose an AI method for diagnosing chronic kidney disease. The University of California Irvine (UCI) AI collection provided a number of the missing properties in the CKD informative index. KNN ascription, which chooses a limited number of full examples with the most comparable predictions to handle each deficient example's missing data, was used to fill in the gaps. Overall, because patients may miss a few estimations for a variety of reasons, missing qualities are usually seen in clinical settings. Once the inadequate informational index was adequately rounded off, models were constructed using six AI computations: arbitrary woodlands, support vector machine, k-closest neighbor, innocent Bayes classifier, decision tree, and strategic relapse. The choice tree and irregular forests AI models outperformed the others with a 99.00% determination precision. Therefore, it is estimated that this procedure may apply to more complicated clinical data for sickness diagnosis by examining the misconceptions caused by the setup models.

关键词： Support vector machines Philosophical considerations computational modeling Mortality Medical treatment Nearest neighbor methods Chronic kidney disease Data models Bayes methods Indexes

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Anomaly-Based Intrusion Detection Systems by using Machine Learning based Stacking Ensemble Model

Anomaly-Based Intrusion Detection Systems by using Machine L...

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Integrated Circuits and Communication Systems (ICICACS), IEEE international conference on

作者： P R Buvaneswari Madhura G K Heba Alasady K. Alagarraja Mukesh Soni School of Electrical and Electronics Engineering VIT Bhopal University Bhopal India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Science and Engineering University Centre for Research & Development Chandigarh University Mohali India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

In recent years, network intrusion refers to unauthorized access to systems or networks by external attackers or insiders aimed at stealing information or deploy malicious software. Traditional approaches for anomaly-based intrusion detection systems have faced several challenges which include high computational overhead and limited capability in detecting zero-day attacks due to reliance on predefined attack signatures or handcrafted features. To address these limitations, this research proposes a Machine Learning-based Stacking Ensemble Model (ML-SEM) for anomaly-based intrusion detection, effectively reducing computational overhead and enhancing detection of previously unseen attacks. Initially, data is collected from University of Nevada, Reno Intrusion Detection Dataset (UNR-IDD) which consists of network traffic data and various types of attacks. Then, the collected data is preprocessed by using standard scaler normalization which ensures that all features have a mean of zero and a standard deviation of one. After that, the features are selected using Principal Component Analysis (PCA) which reduces dimensionality and enhances model efficiency by eliminating redundant and less significant features. Finally, anomaly-based intrusion detection is done by using proposed ML-SEM. The proposed ML-SEM achieved better results in terms of accuracy (98.6%), precision (95.5%) and recall (98.3%) when compared with existing Random Forest (RF).

关键词： Radio frequency Analytical models Adaptation models Accuracy Stacking Intrusion detection Telecommunication traffic Integrated circuit modeling Standards Principal component analysis

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Multi-Agent Systems for Autonomous IoT Network Management Using Distributed Reinforcement Learning

Multi-Agent Systems for Autonomous IoT Network Management Us...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： Neelamegam G Rajaram Venkatesan Ramya SR R.S. Ramya Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data Science Builders Engineering College Tirupur Tamil Nadu India Department of Computer Science and Engineering Dr NGP Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems.

关键词： Smart cities Reinforcement learning Medical services Throughput Dynamic scheduling Energy efficiency Real-time systems Resource management Optimization Multi-agent systems

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Breaking Language Barriers for Real-Time Translation Solutions in Tourism Using Cloud Computing and AI

Breaking Language Barriers for Real-Time Translation Solutio...

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international Symposium on Advanced Computing and Communication (ISACC)

作者： S. Lourdu Jame C. Shobana Nageswari S. Jayaprakash E. Punarselvam R. Meenakshi M. Muthulekshmi Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electronics and Communication Engineering R.M.D. Engineering College Chennai Tamil Nadu India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

To improve tourism experiences and enable cultural communication in today's worldwide tourism industry, it is essential to communicate effectively across linguistic boundaries. Cloud computing and artificial intelligence (AI) are the focus of this paper to create tourism-specific real-time translation systems. These solutions use cloud computing and advanced AI algorithms to better connect visitors with local businesses and provide accurate and contextually aware translations. Important technical parts include cloud computing for scalability and accessibility, machine learning models for language comprehension, and natural language processing (NLP). It uses empirical research or case study techniques to assess the solutions' practicability, performance, and user acceptability. Through the efficient and effective removal of language barriers, the results highlight the promise of cloud-based AI translation systems to revolutionize the tourist industry. This research investigates real-time translation innovations, highlighting scalability, context awareness, and multilingual support, with implications for future advances in AI and cloud-based tourist applications. Also covered are potential avenues for further study and application, with the growth of the tourist industry's continuous development and use of AI-driven solutions. The proposed approach enhances real-time translation using cloud computing and AI, enabling scalability and context-aware translations. However, there are other challenges, such as overcoming latency and advanced, idiomatic expressions.

关键词： Cloud computing Adaptation models Translation Accuracy Scalability computational modeling Real-time systems Natural language processing User experience Artificial intelligence

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CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

CNN-KNN Model for Assessing Hair Health in Telogen Effluvium

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Gotte Ranjith Kumar Shubham Deepak Banerjee School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The abstract provides a concise overview of a study that focuses on predicting hair breakage levels using a CNN-KNN hybrid model. The study evaluates the model's performance across five Breakage Degrees (BDs) by utilizing precision, recall, and F1-score metrics. Among these degrees, BD2 stands out with precision, recall, and F1-score values of 94.20%, indicating the model's ability to accurately classify hair damage levels between 21% and 40%. The support values, which indicate the number and proportion of instances in each BD, demonstrate a well-balanced distribution across all categories, ensuring the model's robust training. Researchers used a dataset of 10,050 images to reach an impressive 94.02% accuracy in predicting hair breakage levels caused by Telogen Effluvium in the research. Moreover, the model achieves an impressive overall accuracy of 98%, showcasing its effectiveness in distinguishing between various degrees of hair breakage severity. This high accuracy level highlights the model's potential application in clinical settings for dermatological diagnostics and personalized treatment planning in hair care management. The abstract underscores the CNN-KNN hybrid approach as a promising methodology for automated hair health assessment, providing healthcare professionals with a dependable tool to improve diagnostic accuracy and treatment efficacy. By incorporating machine learning techniques, specifically convolutional and k-nearest neighbor algorithms, the study contributes to the advancement of computational methods in dermatology and cosmetology. The results suggest practical implications for enhancing patient care through early detection and intervention of hair-related conditions, ultimately supporting better health outcomes and quality of life for individuals affected by hair breakage concerns.

关键词： Hair Training Measurement Accuracy Dermatology Medical services Machine learning Predictive models Planning Reliability

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Structured and Sparse Principle Component Analysis for Multi-modal Data Fusion Approach 3rd

Structured and Sparse Principle Component Analysis for Multi...

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3rd international conference on Machine intelligence and Smart Systems, MISS 2023

作者： Khalaf, Hameed Hassan Jawad, Israa Abed Almulla, Ausama A. Hussein, Mustafa Asaad Nair, Preeti Sharma Department of Medical Laboratories Technology Al-Manara College for Medical Sciences Maysan Iraq Department of Medical Laboratories Technology AL-Nisour University College Baghdad Iraq Department of Medical Laboratories Technology Al-Hadi University College Baghdad10011 Iraq College of Nursing National University of Science and Technology Dhi Qar Iraq Bansal College of Engineering Bhopal India

ISBN: (纸本)9783031691140

The field of image processing has paid a lot more attention to pixel-level picture fusion over the past two decades. Taking an average of each pixel in the source photos is the simplest method for fusing images at the pixel level. In, we see an example of this type of method where two medical images are averaged together to get a single coherent image. While the averaging approach is straightforward to put into practise, it has a number of downsides, including decreased contrast that can result in a significant defeat of data. The authors of this manuscript report deriving a novel fusion model in terms of an unsupervised classification model called principle component analysis, which takes into account the structure and sparse restrictions present in multi-modality images. Eigen value and Eigen vector correlation and covariance analyses make image fusion more adaptable and precise. CT images are ideal for studying rigid objects like bones and implants because of the minimal distortion they introduce, but CT scans are incapable of picking up physiological shifts. Soft tissue can be more clearly seen in an MR image. To measure the effectiveness of the proposed workbench, its performance is simulated in MATLAB for state-of-the-art methods. Key metrics include PSNR (peak signal-to-noise ratio), MSE (Mean square error), association, and precision. All metrics point to the suggested workbench's superior performance, and it also reduces computational load. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Mean square error

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