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Day Driving and Night Driving Behavior Detection Using Deep learning Models 11

Day Driving and Night Driving Behavior Detection Using Deep ...

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11th international conference on Information and Communication Technology, ICoICT 2023

作者： Bin Ishak, Muhammad Firdaus Zaman, Fadhlan Hafizhelmi Kamaru Mun, Ng Kok Makhtar, Ahmad Khushairy Universiti Teknologi MARA Vehicle Intelligence and Telematics Lab School of Electrical Engineering College of Engineering Selangor Shah Alam40450 Indonesia

ISBN: (纸本)9798350321982

the detection of driving behavior is critical for road safety and driver assistance systems. Comparing the performance of driving behavior detection during day and night provides insight into the strengths and weaknesses of these systems under different lighting conditions. this comparison can encourage further work to improve the accuracy and reliability of the systems and ensure they always function optimally. this study aims to compare the performance of driving behavior detection using a digital video recorder (DVR) camera during day and night driving. the study uses a dataset of video footage recorded by a DVR camera on a driving simulator during both day and night driving. Several Convolutional Neural Network (CNN) models have been developed to detect instances of distracted driving behaviors. the study, conducted at VITAL Laboratories at UiTM Shah Alam, used MobileNetV2, GoogleNet, InceptionV3, and Resnet50 to model the data from 44 participants showing 7 different driving behaviors: normal driving, yawning, nodding off, texting, calling, talking to passengers, and using car features. Based on the experiments, the best CNN model for both sessions is InceptionV3 with the overall accuracy for driving behavior detection during day driving is 92.2% and during night-time is 76.9%. the results show that driving behavior detection performs better during the day driving session than at night driving session by a difference of 15.3%. © 2023 IEEE.

关键词： Cameras

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ECG Signal Classification and Anomaly Detection using LSTM and HMM Models 11

ECG Signal Classification and Anomaly Detection using LSTM a...

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11th IEEE international conference on Advances in Computing and Communications, ICACC 2024

作者： Karthik, Garikipati Muppala, Tejaswi Vinitha Chowdary, A. Jayan, Sarada Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science & Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Mathematics Bengaluru India

ISBN: (纸本)9798350379136

Accurate detection and classification of electrocardiogram (ECG) signals is required for cardiac condition diagnosis and timely medical intervention. However, capturing the complex temporal dependencies in ECG data is difficult and hence may provide less effective performance comparatively. the project proposes an innovative approach to detecting and classifying the anomalies in ECG using LSTM and HMM. Preprocessing and normalization of the ECG data are done. After that, the sequence is prepared for input in LSTM, which builds a strong LSTM model with reinforced regularization techniques designed for feature learning from the ECG data. the features extracted by LSTM were concatenated with the original features and applied to train the HMM for anomaly detection based on estimating the likelihood of the log probabilities. thresholding the log probabilities will locate anomalies. the next step is the implementation of HMM to classify ECG signals. the proposed method shows the framework comprehensively for effective detection of anomaly and classification, combining the strong points of both LSTM and HMM. the results obtained are employed to illustrate and prove the efficiency of the proposed combined approach and emphasise the importance of the combination of deep learning and statistical models for further analysis of ECG signals. © 2024 IEEE.

关键词： Markov processes

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thyroid Disorder Classification using Machine learning 11

Thyroid Disorder Classification using Machine Learning

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11th international conference on Emerging Trends in engineering and Technology - Signal and Information Processing, ICETET - SIP 2023

作者： Dixit, Rituraj Tayal, Madhuri A. Bedi, Sarabjeetsingh Saxena, Shailesh MJP Rohilkhand University Bareilly India Shri Ramdeobaba College of Engineering and Management IT Department Nagpur India SRMS CET Bareilly India

ISBN: (纸本)9798350348422

Machine learning algorithms and data mining techniques are crucial for handling the enormous amount of data and information that exists today, especially in the health system. Nowadays thyroid disorder can lead to a number of health issues according to the level of disorder. In order to improve thyroid disorder procurement, the current study focuses on categorizing thyroid disorders using machine learning approaches. this study's major goal is to divide this condition into eight groups: hyperthyroidism, hypothyroidism, normal and five other classes. this paper proposes a Multi-layer Neural Network model that classifies the thyroid disorder data set using advanced machine learning techniques, customized loss functions, and feature extraction. For the better classification of disease proposed model utilizes eight class classifiers. In proposed model, different variants of model based on Tensor Flow functional API using exotic feature engineering techniques was applied, its performance with parametric models like SVM and over the data set of 9172 samples with 30 features are compared. © 2023 IEEE.

关键词： Network layers

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Sensor Automation for Industrial Applications and Prediction of Energy Level for Home Appliances Using Machine learning Algorithm 9

Sensor Automation for Industrial Applications and Prediction...

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9th international conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Selvakumari Jeya, I Jasmine Niranjani, V. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

In recent years wireless sensor networks have helped with automation in many industry domains. Wireless technology cuts cable costs, deployment time and information to transfer and process from source to destination. To make good intelligent and highly automated systems, sensors are crucial. they make it possible to detect, analyze, measure, and process a variety of alterations that occur in industrial equipment's location, length, height, appearance, displacement, etc. this paper described the industrial standardization of different WSN protocols like Zig-Bee, Wireless HART, UWB, IETF 6LowPAN, ISA100, Bluetooth and Wi-Fi. the work represented a few applications of WSNs in industry and its implementation challenges like safety, security, and availability. the work collected the energy consumption data of different home appliances. We applied the LightGBM machine learning algorithm to find a better energy consumption and prediction result. © 2023 IEEE.

关键词： Energy utilization

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Deep learning-Based Breast Cancer Subtype Classification from Whole-Slide Images: Leveraging the BRACS dataset 11th

Deep Learning-Based Breast Cancer Subtype Classification fro...

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11th international Work-conference on Bioinformatics and Biomedical engineering (IWBBIO)

作者： Hernandez, Nerea Carrillo-Perez, Francisco Ortuno, Francisco M. Rojas, Ignacio Univ Granada Dept Comp Engn Automat & Robot Granada 18014 Spain Stanford Univ Stanford Ctr Biomed Informat Res BMIR Sch Med Stanford CA 94304 USA

ISBN: (纸本)9783031646355;9783031646362

Breast cancer is one of the global leading causes of death in women. Accurate diagnosis and effective classification of breast cancer subtypes are critical elements in guiding the treatment and care of patients to improve survival rates. For subtype classification, whole slide imaging (WSI) is becoming increasingly widespread as it allows high-resolution digital capture of tissue samples and facilitates more efficient storage, access, and analysis. However, due to the complexity and variability of breast tissue samples, as well as the inherent subjectivity of human interpretation, there is a need to develop automated and reliable approaches. In our work, we have addressed the challenge of breast cancer type and subtype classification using advanced Deep learning techniques based on Convolutional Neural Networks (CNN). the central problem concerns the correct identification and categorization of distinct breast tissue lesions from images of interest regions of hematoxylin and eosin (H&E) stained slides extracted from the BReast Carcinoma Subtyping data Set (BRACS). Advanced techniques such as fine-tuning with pre-trained CNNs, data augmentation, and dropout are employed to enhance the algorithm's generalization and prevent overfitting. Our experimental results demonstrate outstanding accuracy in breast cancer subtype classification, achieving accurate classifications in 3 and 7 classes at the Region of interest (RoI) level. Our study aims to advance research in the field of AI applied to breast pathology, providing a valuable tool for future research and clinical applications, to ultimately benefit the treatment and care of breast cancer patients.

关键词： Breast Cancer Subtyping Deep learning Convolutional Neural Networks (CNN) Hematoxylin and Eosin (H&E) Whole-Slide Images (WSIs) Regions of Interest (RoIs) Classification

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Prediction and Analysis of Container Terminal Logistics Transportation Time Based on Simulation Interactive Modeling: A Case Study of Ningbo Port: Simulation Modeling of Container Terminal Logistics Time: Ningbo Port Case 23

Prediction and Analysis of Container Terminal Logistics Tran...

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15th international conference on Computer Modeling and Simulation, ICCMS 2023

作者： Wang, Ruoqi Li, Jiawei Bai, Ruibin University of Nottingham Ningbo China School of Computer Science Zhejiang Wanli University Information and Intelligent Engineering College China

ISBN: (纸本)9798400707919

this study aims to analyze and predict the arrival time and influencing factors of container transportation in container yards using simulation interactive modeling technology. the data from the control center of the yard is used to establish a model and perform the driving analysis of the transfer data of container terminals. the economic benefit index is determined by expert consultation and the actual operating parameters of the automated terminal. the feasibility of the evaluation model is verified by an automated container terminal simulation model. the study fills the gaps in the literature on container arrival time and provides a useful tool for improving the efficiency of container transportation. © 2023 ACM.

关键词： Containers

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the Design of Water Resources Cooperation Expert database System based on Talent Portrait 11

The Design of Water Resources Cooperation Expert Database Sy...

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11th international conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Hu, Chengfang Ye, Song Sun, Lin Wei, Yinzhen Chen, Beiqing Xia, Yu Wuhan Vocational College of Software and Engineering Hubei Wuhan430205 China Changjiang River Scientific Research Institute Hubei Wuhan430010 China HuangGang Normal University Hubei Huanggang438000 China

ISBN: (纸本)9798350303513

Experts information database system can help HR quickly find the needed professionals, However, at present, the existing water resource experts database system lacked the support of rich data sources, lacked of cross-professional connections, lacked intelligent recommendation function. this paper puts forward the method of designing the framework of expert database in the field of water resources cooperation based on Talent Portrait *** paper constructs the sharing information portrait model of water conservancy expert, portrait labels architecture consists of five parts: basic information,Work experience, professional ability, water resources cooperation area, and spatial-temporal information. the paper also design the intelligent recommendation function of talent based on water resources cooperation. According to different dimensions of talent portraits, intelligent correlation searching and the feedback mechanism is carried out to improve the search *** construction of the Water Resources Cooperation Experts Portrait database helps to quickly find experts in specific fields of water resources cooperation, provides an information display platform for water resource experts, establishes a channel for close communication between experts in cross fields, and provides human resource information support for government decision-making. © 2023 IEEE.

关键词： database systems

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the Role of Cloud Computing in Real-Time Big data Analytics for Smart Cities and Infrastructure Automation 15

The Role of Cloud Computing in Real-Time Big Data Analytics ...

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15th international conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sarpal, Sumeet Singh Kumar, V. Mathan Pimplapure, Vivek Patil, Shrinivas Marathe, Vijay Henrietta, H. Mary Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Dr. Ambedkar Institute of Management Studies And Research Department of Management Nagpur India School of Mangement - Pg Karnataka Bangalore India Prince Shri Vishwakarma Institute of Technology Department of Electrical Pune India Saveetha Engineering College Department of Mathematics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350370249

With the enhancement of Big data and Smart Cities, Cloud Computing has emerged as an advanced technology. Innovative city operations require real-time processing of vast volumes of data from various sources like sensors, IOT devices, or social media so that it can be analyzed and acted on in real-time with better automation for its infrastructure against different parameters. this article examines the potential of cloud computing to enable real-time big data analytics for smart cities and infrastructure automation. the cloud allows for real-time big data analytics, collecting input from disparate sources across a business ecosystem and analyzing it immediately for actionable insights. the need for large amounts of data combined with the nature of many Iota devices necessitates processing and storage at scale, a task uniquely suited to cloud computing due to its ability to provide scalability, flexibility, and lower cost essential qualities in building intelligent cities or automated infrastructure. Cloud computing offers a space for shared data and collaborative analysis, allowing the city government to open up its vast browser-based datasets so that other agencies (both within government and outside it), researchers, and even citizens can access this information, in turn driving innovation or assisting with strategic planning. Moreover, the evolution not just in cloud-based machine learning and artificial intelligence algorithms but also device-side AI/ML allows for predictive analytics, which can be helpful to preemptively discern potential failures to prevent them pro-actively, thus augmenting city operations efficacy as well as resource utilization besides operational efficiency of infrastructural management. Cloud computing is integral for real-time and big data analytics and innovates cities, organizations, and infrastructure. © 2024 IEEE.

关键词： Automation Infrastructure intelligent Organizations Parameters

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Towards a Cloud-Controlled Heterogeneous Robot System Based on Microservices 9

Towards a Cloud-Controlled Heterogeneous Robot System Based ...

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9th international conference on Control Science and Systems engineering, ICCSSE 2023

作者： Cao, Feng Fang, Chenkai Qin, Xiaoju Jin, Xiaowei Shu, Feng Fudan University Academy for Engineering and Technology Shanghai China Fuyun Intelligent Technology Co. Ltd. Shanghai China

ISBN: (纸本)9798350339055

With the widespread application of robot technology in automated manufacturing, extra-Terrestrial operations, unmanned search, and other fields, the demand for robots in commercial scenarios has become increasingly complex, involving numerous different scenarios. the concept of cloud robots makes it easier to analyze and display robot data, achieving cloud-based sharing. However, due to differences in human-machine interaction, operational logic, access forms, and data structures between various equipment manufacturers, equipment maintenance and data analysis have become challenging. To address this issue, this paper presents a cloud-controlled heterogeneous robot system based on microservices, featuring a four-layer Robot as a Service (RaaS) architecture, combined with Remote Procedure Call (RPC) communication and microservice architecture, aimed at addressing the integration challenges of heterogeneous robots from different suppliers external to the system, as well as the internal incorporation of various services within the system, achieving data sharing and transparent access control, reducing the learning and management costs for maintenance personnel, and improving the efficiency of data analysis and presentation. this design solution has been validated through practical application, demonstrating its feasibility, versatility, scalability, and performance in large-scale development and deployment. © 2023 IEEE.

关键词： Information analysis

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EEG-Based Detection of Pornography Addiction: Deep learning Models with Attention Mechanism for Unveiling Neurocognitive Patterns 11

EEG-Based Detection of Pornography Addiction: Deep Learning ...

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11th international Japan-Africa conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Mahmoud, Abdelrahman Mohamed, Youssef Anter, Ahmed M. Zaky, Ahmed B. Alexandria21934 Egypt Beni-Suef University Faculty of Computers and AI Benisuef62511 Egypt Benha University Faculty of Engineering at Shoubra Egypt

ISBN: (纸本)9798350369328

While field of addiction is evolving, lack of validated diagnostic tools and biomarkers for behavioral addictions has created a need for innovative approaches. this research explores the use of Electroencephalography (EEG) and deep learning techniques to detect pornography addiction. Four distinct deep learning models were implemented, each with a unique architecture, including combinations of Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Attention mechanism. these models were designed to capture temporal dependencies in EEG data and analyze sequential patterns associated with pornography addiction. To assess their performance, the models were evaluated using Leave-One-Out (LOO) and train-test split (TTS) techniques. LOO preserved the temporal integrity of EEG sequences, and simulate real-life scenario, while TTS provided standard evaluation results. the proposed models demonstrated promising results, with an average accuracy of 94 % -97 % using TTS, and 45% to 49% for LOO, outperforming baseline models. this research contributes for laying the foundation for timely diagnosis and intervention. © 2023 IEEE.

关键词： Long short-term memory

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