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检索条件"任意字段=International Conference on Learning and Teaching in Computing and Engineering"

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Revolutionizing Healthcare Intelligence Multisensory Data Fusion with Cutting-Edge Machine learning and Deep learning for Patients' Cognitive Knowledge

Revolutionizing Healthcare Intelligence Multisensory Data Fu...

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2024 international conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Swamy, Samatha R. Nandini Prasad, K.S. Dept. of Ise Bengaluru India Dept. of Ise Bengaluru India

ISBN: (纸本)9798350359688

The study explores the recent advancements in healthcare that have witnessed an unprecedented integration of multisensory data, ranging from wearable devices to medical imaging and electronic health records. This paper explores the transformative impact of cutting-edge machine learning algorithms and deep learning algorithms in processing and interpreting this multisensory data, offering unparalleled insights into patients' cognitive well-being. The review delves into the types and challenges of multisensory data, evaluating performance of supervised and supervised machine learning models for disease detection and pattern recognition respectively. Additionally, it scrutinizes the importance of deep learning applications that are based on convolutional neural networks as well as recurrent neural networks for medical image classification. The paper also mentions the importance of the integration of multisensor data fusion with these advanced algorithms, emphasizing real-time monitoring systems and ethical considerations. Summaries from research data shows effectiveness of remote patient care and predictive analysis and personalized treatment plans. The exploration of future directions and opportunities underscores the prime importance for further advancements in explainable AI, collaborative learning, and edge computing, consolidating the vision of a healthcare paradigm revolutionized by intelligent data fusion and cutting-edge technologies. © 2024 IEEE.

关键词： Electronic health record

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ACO-Based Hyperparameter Tuning of a DL Model for Lung Cancer Prediction 5

ACO-Based Hyperparameter Tuning of a DL Model for Lung Cance...

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5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Dilip, Kumar Ghantasala, G. S. Pradeep Rao, D. Nageswara Rathee, Manisha Bathla, Priyanka Alliance University Department of Computer Science and Engineering Bengaluru India Jawaharlal Nehru University School of Computer & Systems Sciences New Delhi India Chandigarh University Mohali Punjab Gharuan India

ISBN: (纸本)9798350383522

Machine learning models could provide human intelligence to perform complex tasks effectively. Among them, deep learning models have successfully been applied in several domains. However, that efficacy can be achieved only through effective design and training of deep learning models, which is a complex task to achieve and requires a lot of expertise in the domain. Finding the optimal hyperparameter values is crucial for high-performance machine learning models, especially tree-based models and neural network-based models. Estimation of the parameters such as the weight of neural networks can be done using data sets and these are internal model parameters, however the hyperparameters such as number of layers, size of layers, learning rates etc. are external to the model. The values of hyperparameters can either be determined intuitively by domain experts or by manually and randomly trying various possible configurations until we arrive at some acceptable values. Both have severe limitations for machine learning models with a lot of hyperparameters. Several attempts have been made in the direction of automatic hyperparameter optimization to save human efforts in designing machine learning models. Nature-inspired computing has gained a lot of popularity in this direction in recent years. In this paper, we have applied ant colony optimization, a nature-inspired approach for hyperparameter optimization for deep learning models for the prediction of lung cancer. © 2024 IEEE.

关键词： Ant colony optimization combinatorial optimization Deep learning model Hyperparameter optimization Metaheuristic

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Intelligent Malware Detection System Based on Behavior Analysis in Cloud computing Environment

Intelligent Malware Detection System Based on Behavior Analy...

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2023 international conference on Circuit Power and computing Technologies, ICCPCT 2023

作者： Samuel, Jibu K Jacob, Mahima Thankam Roy, Melvin Sayoojya, P.M. Joy, Anu Rose Amal Jyothi College of Engineering Department of Computer Engineering Kerala India

ISBN: (纸本)9798350333244

IBMD(Intelligent Behavior-Based Malware Detection) aims to detect and mitigate malicious activities in cloud computing environments by analyzing the behavior of cloud resources, such as virtual machines, containers, and applications. The system uses different machine learning methods like deep learning and artificial neural networks, to analyze the behavior of cloud resources and detect anomalies that may indicate malicious activity. The IBMD system can also monitor and accumulate the data from various resources, such as network traffic and system logs, to provide a comprehensive view of the behavior of cloud resources. IBMD is designed to operate in a cloud computing environment, taking advantage of the scalability and flexibility of the cloud to detect malware and respond to security incidents. The system can also be integrated with existing security tools and services, such as firewalls and intrusion detection systems, to provide a comprehensive security solution for cloud computing environments. © 2023 IEEE.

关键词： Virtualization

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Integration of Deep learning Models and Explanatory Models to Detect Anomalous Periods in Vital Signs of Mining Truck Engines 31

Integration of Deep Learning Models and Explanatory Models t...

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31st IEEE international conference on Electronics, Electrical engineering and computing, INTERCON 2024

作者： Lima, Ines Katia Huaman Gladys Cutipa Arapa, E. Professional School of Computer and Systems Engineering National University of San Antonio Abad de Cusco Cusco Peru

ISBN: (纸本)9798350378344

'Latex The Peruvian mining industry is beginning to adopt IoT technologies. The increasing complexity of modern engines makes it crucial to implement predictive maintenance systems to ensure reliability and performance optimization. This study is based on data obtained from an open-pit copper mine in southern Peru. The use of advanced machine learning models is proposed, evaluating between Long Short-Term Memory with Autoencoders (LSTM-AE) and Artificial Neural Network (ANN) for anomaly detection in multivariate time series for diesel engine health monitoring. This project addresses three issues of the current analysis methodology: first, point anomalies are used to monitor potential failures, generating a high rate of false alarms, while the proposed method identifies abnormal time periods;second, the threshold for alerts is manually calculated and requires expert intervention periodically, whereas the proposed methodology calculates the threshold in an unsupervised manner. Third, deep learning models are often considered 'black box' models since it is not easy to interpret how the model reached a conclusion, which is why model explainability is important for its adoption within the industry. The objective is to develop a predictive model that identifies potential failures early, allowing for the planning of preventive maintenance and reducing unplanned downtime. The results show that the combination of these models significantly improves the accuracy in failure prediction, facilitating timely and precise interventions. © 2024 IEEE.

关键词： Predictive maintenance

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20th international conference on computing in Civil and Building engineering, ICCCBE 2024

20th International Conference on Computing in Civil and Buil...

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20th international conference on computing in Civil and Building engineering, ICCCBE 2024

ISBN: (纸本)9783031842238

The proceedings contain 34 papers. The special focus in this conference is on computing in Civil and Building engineering. The topics include: Data-Based Assessment of the Resilience of Municipal Road Infrastructure as a Component of Sustainable Asset Management;Where Project Governance and Sustainability Meet? A Review of Literature and Discussion on IPD Governance Structure;analysis of Software Solutions to Find the Most Suitable Project Delivery Model;Multi-source Transfer learning Method for Enhancing the Deployment of Deep Reinforcement learning in Multi-Zone Building HVAC Control;2D and 3D Thermal Transition Presentation of Temperature Spikes in Net-Zero Energy Residential Test Facility;a Driver Business Model: Triple Layer Business Canvas Model for Integrating Drivers of the Energy Efficiency Market in Europe;a Knowledge Graph-Based Approach for Construction Safety Hazards Management and Rectification Measures Intelligent Recommendation;Hazards Identification and Impact Reduction for At-Risk Construction Activities Using 3D BIM and VR;recurrent Neural Network-Based Workers’ Posture and Workload Estimation on Building Construction Sites;implementation of Wearable Technologies for Enhancing Safety Monitoring at Construction Jobsites: A Trend Analysis;unveiling the Spectrum of Personal Heat Stress Responses in Buildings;analyzing the Effect of Wind on Construction Schedules;image-Based Traffic Data Collection in Traffic-Related Air Pollution Analysis;prediction of Expansion Joint Life in Concrete Pavement;asset Management of Municipal Road Infrastructure;impact of Municipal Referencing Basis on Methods of Road Condition Assessment;employing the Policy Gradient Approach for Strategic Decision-Making in Road Network Infrastructures;classification of Physical Fatigue Levels in Shoveling Using Heart Rate, Electrodermal Data, and Skin Temperature.

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Machine learning Based Hardware Accelerated Cobot Grasping in the Food Industry 11

Machine Learning Based Hardware Accelerated Cobot Grasping i...

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11th international conference on Electrical, Electronic and computing engineering, IcETRAN 2024

作者： Ivačko, Nikola Ćirić, Ivan Ćojbašić, Žarko Milošević, Maša Jevtić, Dušan University of Nis Faculty of Mechanical Engineering Nis18000 Serbia Fazi doo Belgrade11000 Serbia

ISBN: (纸本)9798350386998

In food industry automation, the integration of machine learning with robotic systems can introduce improved efficiency and precision in various tasks such as fruit and vegetable handling and manipulation. This paper introduces an innovative approach to robotic grasping, specifically customized for the challenge of identifying and handling apples and oranges within a dynamic and unstructured environment. Utilizing the YOLO (You Only Look Once) object detection algorithm, the system consists of a camera mounted on the robot's end effector, enabling real-time identification and localization of fruits and vegetables. Implemented on the NVIDIA Jetson platform, this solution presents the combination of machine learning techniques with hardware acceleration, ensuring optimal performance in real-time object recognition and manipulation tasks. Our experimental setup tests the robustness of our object recognition and localization approach under varying lighting and orientation scenarios and evaluates the system's overall performance in a real-world, cluttered environment typical of the food industry. The findings of this study underscore the potential of integrating ML-powered vision systems with hardware-accelerated robotics for food handling and manipulation applications. By demonstrating high levels of accuracy in recognition, classification and grasping, our research contributes to the broader field of robotic automation, offering insights into the scalability and adaptability of such systems across different sectors of the food industry. © 2024 IEEE.

关键词： Fruits

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GeoAI: A Systematic Review on Change Detection of Geospatial Data with Different Methodologies 6

GeoAI: A Systematic Review on Change Detection of Geospatial...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Nandhakumar, C. Pradeep, D. M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

Geospatial data science offers significant insights derived from spatial information. It is employed to examine various locations within urban areas, facilitating advancements in transportation, healthcare, disaster management, and urban planning. Geospatial information encompasses both spatial and temporal data obtained from satellite imagery and street view images. A diverse array of spatial data can be input into the system to identify valuable patterns. This research study presents a comprehensive review of geospatial data science, detailing its methodologies and applications. Image processing serves as a crucial technique for segmenting regions within satellite images. Additionally, deep learning algorithms are utilized to develop models that analyze the data, yielding efficient information. © 2025 IEEE.

关键词： Satellite imagery

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Connecting Cloud computing and Machine learning through Functional Situation-awareness: A User-centric Smart Monitoring Application

Connecting Cloud Computing and Machine Learning through Func...

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1st IEEE international conference on Software Services engineering (IEEE SSE)

作者： Millan, Miguel Liu, Ruochen Ming, Hua Oakland Univ Dept Comp Sci & Engn Rochester MI 48307 USA

ISBN: (纸本)9798350340754

For many parents, the time between their children leaving home and the ensuing arrival at school is a grey area with uncertainty. The need of being aware of the highly critical safetyness situations there- fore arises. Situation-aware computing serves as a promising technique, both theoretically and pragmatically, to bring smart monitoring to the field. It seamlessly integrates the benefits offered by machine learning techniques and cloud computing technologies with an emphasis on user centric situations. In this paper we present a real world smart monitoring application: its underlying model, its human computer interactive design and design principles, its support from the theory and practice of situation-awareness computing, as well as its implementation details and viability test. This application also serves the purpose of elaborating important attributes of safety-aware situation computing, with a strong linkage to safety critical human computer interaction. The goal of this application is to accurately recognize and identify the faces of children, under relevant environmental contexts, when they enter or exit a bus, and then promptly alert their parents. Two main components, one being a distributed bus subsystem and the other being a central cloud-based subsystem, are built into this application highlighting lightweight to effectively serve the users. The novelty of our approach lies in the fact that we deeply embrace functional programming paradigm to tackle the specification of the two subsystems at the design level with rigorous computational semantics, which in turn contributes to the quality implementation of the lower level details.

关键词： Situation Situation-awareness Human Computer Interaction Intelligent Monitoring Machine learning Cloud computing

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Accelerating Sustainability through Leveraging Machine learning to Analyze CSR Spending in the Indian Automobile Industry 7

Accelerating Sustainability through Leveraging Machine Learn...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Yogi, Kottala Sri Dankan Gowda, V. Pati, Anwesha Srinivas, Ved Aghav, Santosh Abdullah, Ibrahim International University Symbiosis Institute of Business Management Hyderabad Symbiosis Department of Operations Pune India BMS Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Symbiosis Institute of Business Management Hyderabad India Thiagarajar School of Management Department of Human Resources Tamil Nadu Madurai India Symbiosis Law School Hyderabad Pune India Al-Turath University College Department of Computer Science Baghdad Iraq

ISBN: (纸本)9798350372816

This paper uses machine learning to assess Corporate Social Responsibility spending trends on the Indian automobile sector between 2016 and 2021. Simply put, the aim of the research is to recognize trends and patterns in sustainability expenditures. More specifically, the study examines how businesses allocate money across the three important aspects of the sustainability report: People, Planet, and Profit Purcell, 2020. The research is based on a largely industry-based database obtained from public companies' records. The use of advanced statistical techniques, such as machine learning and random forest modeling, enables one to examine the impact of financial allocations on sustainability achievements. Increments in CSR spending are related to enhanced company sustainability performance in terms of correlation outcomes. The paper also recognizes areas where cost cuts have the best returns, as well as shortfalls in the existing CSR approach, offering a strategy for possible development's. © 2024 IEEE.

关键词： CSR Impact Sustainability Analysis Indian Automobile Industry learning Machine Social Responsibility Environmental Spending Sustainability Investments Corporate

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Enhanced Lung Cancer Detection Using CNNs for Accurate and Efficient CT Image Classification 1

Enhanced Lung Cancer Detection Using CNNs for Accurate and E...

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1st international conference on computing, Sciences and Communications, ICCSC 2024

作者： Bdair, Mohammad Ruby, E D Kanmani Juyal, Prachi Taseen, Rakheeba Abirami, A. Soujanya, A. School of Architecture Computing & Engineering University of East London London United Kingdom Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of ECE Tamil Nadu Chennai India Uttarakhand Dehradun India School of computer science and Engineering Presidency university Bangalore India Sona College of Technology Department of Mathematics Tamil Nadu Salem India Ibrahimpatnam India

ISBN: (纸本)9798350353648

Lung cancer must be detected as early as possible, but it may be difficult with existing methods since they often depend on human judgment and outdated image processing. These techniques take a lot of time and are prone to errors, which produces uneven outcomes. The research provides a unique deep learning-based method for classifying lung nodules from CT images using convolutional neural networks (CNNs). By automating feature extraction and categorization, the suggested technique greatly increases diagnostic precision. The result shows superior performance over existing systems is demonstrated by evaluation measures such as AUC-ROC, accuracy, sensitivity, and specificity. With a training duration of 12 hours, an AUC-ROC of 0.95, accuracy of 92%, sensitivity of 94%, specificity of 90%, and inference speed of 0.5 seconds per scan, the results demonstrate notable improvements in diagnostic efficiency and accuracy. Deep learning has the potential to revolutionize the diagnosis of lung cancer and enhance patient care, as demonstrated by the study. © 2024 IEEE.

关键词： Lung cancer

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