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Exploiting Predictability of Random Vector Functional Link Networks in Forecasting Quality of Service (QoS) Parameters of IoT-Based Web Services Data

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International Journal of Sensors, Wireless Communications and Control 2023年第2期13卷 57-70页

作者： Lenka, Stitapragyan Pradhan, Sateesh Kumar Nayak, Samaleswari Prasad Nayak, Sarat Chandra Department of Computer Science Application Utkal University Odisha Bhubaneswar India Department of Computer Science and Engineering Silicon Institute of Technology Odisha Bhubaneswar India Department of Computer Science Yonsei University Seoul South Korea

Background: QoS parameters are volatile in nature and possess high nonlinearity, thus making the IoT-based service and recommendation process challenging. Methods: An efficient and accurate forecasting model is lacking in this area and needs to be explored. Though an artificial neural network is a prominent option for capturing such nonlinearities, its effi-ciency is limited by the structural complexity and iterative learning method. The random vector functional link network (RVFLN) significantly reduces the time complexity by randomly assigning input weights and biases without further modification. Only output layer weights are calculated iteratively by gradient methods or non-iteratively by least square methods. It is an efficient algorithm with low time complexity and can handle complex domain problems without compromising accuracy. Motivat-ed by these characteristics, this article develops an RVFLN-based model for forecasting QoS parameter sequences. Results: Two real-world IoT-enabled web service dataset series are used in developing and evaluating the effectiveness of RVFLN-based forecasts in terms of three performance metrics. Conclusion: Experimental results, comparative studies, and statistical tests are conducted to establish the superiority of the proposed approach over four other similar forecasting techniques. © 2023 Bentham Science Publishers.

关键词： artificial neural network IoT QoS parameter forecasting random vector functional link network time series forecasting web service search

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Multimodal Interaction Fusion Network for Cloth-Changing Person Re-Identification 22

Multimodal Interaction Fusion Network for Cloth-Changing Per...

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22nd IEEE International Symposium on Parallel and Distributed Processing with applications, ISPA 2024

作者： Yuan, Caihong Xu, Yuanchen Guan, Zhijie Chen, Xiaopan Zhu, Xiaoke Liang, Wenjuan Henan University School of Computer and Information Engineering Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan Province Spatial Information Processing Engineering Technology Research Center Kaifeng China

ISBN: (纸本)9798331509712

Cloth-Changing Person Re-Identification (CC-ReID) refers to the technology that can identify the same individual in surveillance scenarios, even after changing their clothes. The core of CC-ReID is to exact cloth-irrelevant identity features. Existing methods usually extract them from body contours, skeletons, human faces, etc. However, information extracted from a single modality is often insufficient. Therefore, we propose a novel Multimodal Interaction Fusion (MIF) framework that could facilitate the deep integration of multimodal information, thereby enhancing the generalization ability of CC-ReID. Specifically, we design a triple-interaction collaborative module that strategically integrates features captured from body posture, original RGB images, and clothes-erased images, maximizing the advantages of each modality and overcoming their limitations. Additionally, we develop an Adapt-Blend Pool (ABP) module that effectively improves pooling methods to enhance the model's ability to process images with complex backgrounds and varied features. Following training, our framework effectively harnesses a variety of modal inputs to capture identity representations independent of clothing accurately. Extensive experimental results on PRCC, LTCC, and VC-Clothes datasets demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Image enhancement

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A Comprehensive Study of a Conversational AI-Driven Healthcare Bot with Pose Estimation for Precision Fitness Monitoring and Personalized Guidance

A Comprehensive Study of a Conversational AI-Driven Healthca...

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2024 IEEE International Conference on Big Data and Machine Learning, ICBDML 2024

作者： Sinha, Anurag Kumar, Biresh Kundu, Roumo Sharma, Amit Sharma, Mukesh Raza, Asif ICFAI University ICFAI Tech School Computer Science Department Jharkhand Ranchi India Amity University Department of Computer Engineering and Application Jharkhand India Amity Institute of Information Technology Amity University Jharkhand Jharkhand Ranchi India Lovely Professional University Department of Computer Science Phagwara India Amity University Department of Master of Computer Application Jharkhand Ranchi India

ISBN: (纸本)9798350374100

This research investigates a new healthcare paradigm through the analysis of a conversational AI-powered healthcare bot that has posture estimation capabilities for accurate fitness tracking and customized recommendations. User motions during workout routines are accurately and thoroughly assessed thanks to the incorporation of powerful posture estimation technology. With the help of individualized exercise advice, this thorough study explores the capabilities of the system and shows how it may transform wellness practices. The healthcare bot provides an innovative method of fitness tracking by using conversational AI and accurate posture prediction to customize recommendations based on user requirements. This novel combination promises to enhance wellbeing and provide consumers with a more efficient and customized workout experience. The study emphasizes this point. © 2024 IEEE.

关键词： Health care

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MDNN:Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning

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computer Modeling in engineering & Sciences 2023年第7期136卷 381-401页

作者： Yi Chen Jin Zhou Qianting Gao Jing Gao Wei Zhang School of Computer Science Central China Normal UniversityWuhan430079China Computer Science School of ScienceRensselaer Polytechnic InstituteTroy12180USA National Engineering Laboratory of Educational Big Data Application Technology Central China Normal UniversityWuhan430079China

Prediction of students’engagement in aCollaborative Learning setting is essential to improve the quality of *** learning is a strategy of learning through groups or *** cooperative learning behavior occurs,each student in the group should participate in teaching *** showed that students who are actively involved in a class gain *** behavior and facial expression are important nonverbal indicators to reveal engagement in collaborative learning *** studies require the wearing of sensor devices or eye tracker devices,which have cost barriers and technical interference for daily teaching *** this paper,student engagement is automatically analyzed based on computer *** tackle the problem of engagement in collaborative learning using a multi-modal deep neural network(MDNN).We combined facial expression and gaze direction as two individual components of MDNN to predict engagement levels in collaborative learning *** multi-modal solution was evaluated in a real collaborative *** results show that the model can accurately predict students’performance in the collaborative learning environment.

关键词： Engagement facial expression deep network gaze

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Research on Model Predictive Control-Based Feedback Strategies for Autonomous Driving 6

Research on Model Predictive Control-Based Feedback Strategi...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Li, Ting Xu, Xianxia Liu, Xuejuan Liu, Jie Guangxi Vocational Normal University School of Computer and Information Engineering Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning China

ISBN: (纸本)9798350316520

To address the issues of control stability and safety in autonomous vehicles, this study focuses on research related to Model Predictive Control (MPC) algorithms and algorithm optimization. By leveraging MPC control algorithms and system dynamics techniques, we have achieved stable autonomous driving. Simulation experiments based on Model Predictive Control have been conducted to validate the feasibility and effectiveness of MPC algorithms for feedback control in autonomous driving. Two distinct simulation scenarios have been designed to verify the MPC algorithm's performance under different road conditions. The results indicate that the MPC algorithm effectively enhances the control stability and safety of autonomous driving vehicles. © 2024 IEEE.

关键词： Model predictive control

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IoT based Smart wheelchair For Disabled Person

IoT based Smart wheelchair For Disabled Person

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Lalit Kushwaha, Ajay Shriram Sherawat, Kartikeya Siddique, Zishan IILM University School of Computer Scienceand Engineering Department of Computer Application Uttar Pradesh Greater Noida India Sharda University Sharda School of Engineringand Technology Department of Computer Scienceand Applications Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350378092

Smart wheelchairs are revolutionizing mobility for disabled individuals, offering enhanced independence and improved quality of life. These advanced devices incorporate cutting-edge technologies like obstacle avoidance systems, voice control, and self-navigation capabilities, transforming the traditional wheelchair into an intelligent mobility solution. Obstacle avoidance systems utilize sensors to detect and avoid obstacles, ensuring safe navigation in complex environments. Voice control enables users to operate the wheelchair hands-free, providing greater convenience and control. Self-navigation capabilities allow the wheelchair to autonomously navigate to pre-programmed destinations, reducing user burden and enhancing independence. Smart wheelchairs also offer connectivity features, enabling remote monitoring and control by caregivers. This provides an added layer of safety and support for users, ensuring timely assistance when needed. The integration of these advanced technologies into smart wheelchairs is transforming mobility for disabled individuals, empowering them with greater independence, safety, and control over their movement. As smart wheelchair technology continues to evolve, the possibilities for enhanced mobility and improved quality of life for disabled individuals are limitless. © 2024 IEEE.

关键词： Disabled persons

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Double-Edged Agriculture 4.0: Hodiernal Expedient Technologies and Cyber-Security Challenges 6

Double-Edged Agriculture 4.0: Hodiernal Expedient Technologi...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Arya, Swati Tripathi, Shubham Srivastava, Archana Aggarwal, Shruti Soni, Nupur Ansar, Syed Anas School of Computer Application Babu Banarasi das University Lucknow India Thapar Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350304480

Digitalization and intelligent systems have engulfed all dimensions and vicinity of existence. The intense fusion of hodiernal information technology and archaic agriculture has ushered in the epoch of agriculture 4.0, invariably reputed as smart agriculture. Variegated operational or forthcoming technologies reckon in agriculture 4.0, e.g., algae feedstock, automation, hydroponics, nanotechnology, genome editing, cognitive computing, blockchain, and machine learning, may have far-reaching implications for future agriculture and food systems and radical rudimentary potential. Although the profusion benefits of this Fourth Revolution abound, many unresolved issues and stumbling blocks must be conquered to make this paradigm shift feasible. Future solutions will coerce data availability and accuracy as vital factors to assist farmers, also security will become indispensable for constructing vigorous and efficacious systems. Security deals with difficulties like compatibility, scarce funds, and gargantuan data as smart agriculture incorporate a huge gamut and volume of resources. In this frame of reference, the authors opine that the inclusion and exclusion impact of Agriculture 4.0 technologies require ancillary scrutiny. This paper articulates the core technologies of agriculture 4.0 with an explicit focus on its applications in veritable contexts whilst delving into security issues (chiefly cyber-security). This research is envisaged to have a pragmatic influence on agriculture 4.0, emphasizing paradigms and security measures to aid farm stakeholders to navigate the firm's transition to digitization. © 2023 IEEE.

关键词： Agriculture 4.0 Cloud Computing Cyber Security Data Analytics Internet of Things Sensor

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Automated Detection of Abnormalities in Ultrasound Images Using Deep Learning

Automated Detection of Abnormalities in Ultrasound Images Us...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Kumar, Amit Bhuvana, J. Kharbas, Vikas Kumar College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

Deep getting to know has been utilized in scientific imaging studies to explore challenges with diagnosing ailments and detecting anomalies. This examination focused on the automated detection of bizarre tissues in ultrasound pix. Pics must adhere to a homogeneous, properly-described, predictable anatomy for hit-deep getting-to-know applications. It looks at the proposed artificial neural network (ANN) and lengthy quick-time period memory (LSTM) based version referred to as ALunoNet to detect anomalies in ultrasound pictures. The models were skilled on a 10,000 M-mode and B-mode ultrasound pictures dataset. After ten epochs, their checking out accuracy was eighty three.16%. They look at comparing the performance of ALunoNet with other popular CNN and LSTM models. The observed effects demonstrate that ALunoNet performs higher than the same old fashions and that its overall performance is consistent even on datasets with constrained information. It has a look at highlights the capability of deep-gaining knowledge to automatize the detection of strange tissues in ultrasound pics. © 2024 IEEE.

关键词： abnormal automatized homogeneous performance ultrasound

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Anomaly Detection in Medical Images Using Deep Reinforcement Learning 2

Anomaly Detection in Medical Images Using Deep Reinforcement...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rastogi, Divya Sharma, Aparna Yadav, R.K. Varshney, Neeraj Saraswat, Laxmi Computer Science and Application Sharda University UP Greater Noida India Electronics & Communication Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India Gla University Department of Computer Engineering and Applications UP Mathura India Abes Engineering College Department of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350371055

Anomaly detection in clinical pix is vital to identify and diagnose diseases. Deep reinforcement gaining knowledge is a practical gadget-mastering approach implemented for more than a few medical obligations. This technique applies a synthetic intelligence version to research by revealing an iterative procedure to discover a set of top-of-the-line parameters for ultimate selection-making. The cause of this examination is to use deep reinforcement mastering for anomaly detection in medical pix. This approach is used to identify strange areas in clinical pix, including tumors, mental diseases, and other abnormalities. Deep reinforcement mastering is mixed with Convolutional Neural Networks (CNNs) to extract capabilities from the medical pictures and then classify them consistent with their deviation from normal. The detection accuracy is evaluated using a novel assessment metric, referred to as the precision-recollect (PR) curve, which is designed to degree the version's accuracy. The results show that deep reinforcement learning can gain high-performance anomaly detection accuracy compared with conventional methods. © 2024 IEEE.

关键词： Accuracy Anomalous Evaluated Evaluation Measure

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Research on Design Method of FPGA Reliability Self-diagnosis Function 5

Research on Design Method of FPGA Reliability Self-diagnosis...

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5th IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2023

作者： Gao, Yankun Hao, Yutao Shen, Wei Beijing Institute of Computer Technology and Application Beijing China Beijing Institute of Technology School of Aerospace Engineering Beijing China

ISBN: (纸本)9798350310603

The FPGA is widely used in aerospace, nuclear power and other highly reliable fields. It realizes the core and key functions of information systems, such as communication, data processing, control, and so on. According to the functional requirements of FPGA self-diagnosis in high-reliability systems. Two typical FPGA self-diagnosis methods, the look-up table method, and the redundant method, are compared and analyzed. Combined with the redundant method based on state machine theory, an FPGA reliability self diagnosis function design method is proposed to diagnose and analyze the soft errors and local permanent errors in FPGA, through the statistical analysis of self diagnosis coverage, it is to prove that the method can achieve high self diagnosis coverage and improve the overall safety level of key equipment. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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