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IEEJ Transactions on Electronics, Information and Systems 2024年第1期144卷 15-20页

作者： Ashida, Yoichiro Katayama, Masaru Department of Electrical Engineering and Computer Science National Institute of Technology Matsue College 14-4 Nishi-Ikuma Shimane Matsue690-8518 Japan

Intelligent-PID (i-PID) control proposed by Fliess is a simple control algorithm. The controller is designed based on ultra-local model, and consisted of PID type controller and derivatives of reference signal and controlled variables. Authors have considered discrete i-PID controller and its properties, and one of the result was that PD controller was sufficient in discrete time domain. In discrete i-PID controller, PD type controller is still required to ensure convergence of controlled error to zero, and PD type parameters affects convergence property, fast-response property and stability. In this paper, predictive functional control (PFC) based on an ultra-local model is proposed. By introducing of PFC, controlled variable converge to reference value without PD type controller. User-specified parameters of the proposed controller is smaller than the conventional i-PID controller. Characteristics of the proposed controller are compared with discrete i-PID controller by numerical examples. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Controllers

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AsPeer: Method to Self and Peer Assessment in Large Online Design Classes

International Journal of Emerging Technologies in Learning

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International Journal of Emerging Technologies in Learning 2023年第10期18卷 19-38页

作者： Sankaran, Hariesh K. Shanmugam, Raju Shah, Bhavesh Ahmad, Sultan Abdeljaber, Hikmat A. M. Nazeer, Jabeen Karnavati University Gujrat Gandhinagar India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan

Peer and self-assessment open opportunities to scale assessments in online classrooms. This article reports our experiences of using AsPeer-peer assessment system, with two iterations of a university online class. We observed that peer grades highly correlated with staff assigned grades. It was recorded that, the peer grade of all student submissions within range of instructor grade averaged to 21.0% and that within the next 2 ranges was 49.0%. We performed three experiments to improve accuracy of peer grading. First, we observed grading bias and introduced a data driven feedback mechanism to inform peers of it. Students aided by feedback were mindful and performed grading with better accuracy. Second, we observed that the rubric lacked efficiency in translating intent to students. Simplified guiding questions improved accuracy in assessment by 89% students. Third, we encouraged peers to provide personalized qualitative feedback along with rating. We provided them with feedback snippets that addressed common issues. 64% students responded that the snippets helped them to critically look at submissions before rating © 2023, International Journal of Emerging Technologies in *** Rights Reserved.

关键词： Students

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A Recurrent Neural Network for Multimodal Anomaly Detection by Using Spatio-Temporal Audio-Visual Data

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computers, Materials & Continua 2024年第11期81卷 2493-2515页

作者： Sameema Tariq Ata-Ur-Rehman Maria Abubakar Waseem Iqbal Hatoon S.Alsagri Yousef A.Alduraywish Haya Abdullah AAlhakbani Department of Electrical Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Electrical Engineering National University of Science and TechnologyNational University of Sciences and TechnologyIslamabad24090Pakistan Department of Business and Computing Ravensbourne University LondonRavensbourne UniversityLondonSE100EWEngland Electrical and Computer Engineering Department College of EngineeringSultan Qaboos UniversityMuscat123Oman College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11673Saudi Arabia

In video surveillance,anomaly detection requires training machine learning models on spatio-temporal video ***,sometimes the video-only data is not sufficient to accurately detect all the abnormal ***,we propose a novel audio-visual spatiotemporal autoencoder specifically designed to detect anomalies for video surveillance by utilizing audio data along with video *** paper presents a competitive approach to a multi-modal recurrent neural network for anomaly detection that combines separate spatial and temporal autoencoders to leverage both spatial and temporal features in audio-visual *** proposed model is trained to produce low reconstruction error for normal data and high error for abnormal data,effectively distinguishing between the two and assigning an anomaly *** is conducted on normal datasets,while testing is performed on both normal and anomalous *** anomaly scores from the models are combined using a late fusion technique,and a deep dense layer model is trained to produce decisive scores indicating whether a sequence is normal or *** model’s performance is evaluated on the University of California,San Diego Pedestrian 2(UCSD PED 2),University of Minnesota(UMN),and Tampere University of technology(TUT)Rare Sound Events datasets using six evaluation *** is compared with state-of-the-art methods depicting a high Area Under Curve(AUC)and a low Equal Error Rate(EER),achieving an(AUC)of 93.1 and an(EER)of 8.1 for the(UCSD)dataset,and an(AUC)of 94.9 and an(EER)of 5.9 for the UMN *** evaluations demonstrate that the joint results from the combined audio-visual model outperform those from separate models,highlighting the competitive advantage of the proposed multi-modal approach.

关键词： Acoustic-visual anomaly detection sequence-to-sequence autoencoder reconstruction error late fusion regularity score

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Energy Efficient Distributed Cloud Load Balancing Using Virtual Machines 6th

Energy Efficient Distributed Cloud Load Balancing Using Virt...

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6th International Conference on Intelligent Computing and Optimization, ICO 2023

作者： Afridi, Sabbir Mahmud Hossain, Akmal Rafsan, Md Mehdi Ahsan Reza, Ahmed Wasif Arefin, Mohammad Shamsul Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (纸本)9783031501500

Load balancing is a crucial element of any distributed system because it serves to spread workloads equally throughout numerous computing resources aiming to optimize speed and efficiency. However, traditional load-balancing techniques often consume a significant amount of energy, which can lead to increased operating costs and negative environmental impacts. We propose a dynamic round-robin approach for cloud load balancing in this paper to alleviate the strain on wireless networks. Our algorithm achieves 8% more efficiency compared to the traditional round-robin model. Additionally, our load-balancing virtual machine architecture exhibits an improvement in resource usage efficiency ranging from 15 to 42% compared to the current model. Overall, our model significantly increases the efficiency of the current load-balancing method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Virtualization

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An Enhanced Equilibrium Optimizer for Solving Optimization Tasks

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computers, Materials & Continua 2023年第11期77卷 2385-2406页

作者： Yuting Liu Hongwei Ding Zongshan Wang Gaurav Dhiman Zhijun Yang Peng Hu School of Information Science and Engineering Yunnan UniversityKunming650500China Department of Electrical and Computer Engineering Lebanese American UniversityByblos1401Lebanon University Centre for Research and Development Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali140413India Department of Computer Science and Engineering Graphic Era(Deemed to be University)Dehradun248002India Research and Development Department Youbei Technology Co.Ltd.Kunming650500China

The equilibrium optimizer(EO)represents a new,physics-inspired metaheuristic optimization approach that draws inspiration from the principles governing the control of volume-based mixing to achieve dynamic mass *** its innovative foundation,the EO exhibits certain limitations,including imbalances between exploration and exploitation,the tendency to local optima,and the susceptibility to loss of population *** alleviate these drawbacks,this paper introduces an improved EO that adopts three strategies:adaptive inertia weight,Cauchy mutation,and adaptive sine cosine mechanism,called ***,a new update formula is conceived by incorporating an adaptive inertia weight to reach an appropriate balance between exploration and ***,an adaptive sine cosine mechanism is embedded to boost the global exploratory ***,the Cauchy mutation is utilized to prevent the loss of population diversity during *** validate the efficacy of the proposed SCEO,a comprehensive evaluation is conducted on 15 classical benchmark functions and the CEC2017 test *** outcomes are subsequently benchmarked against both the conventional EO,its variants,and other cutting-edge metaheuristic *** comparisons reveal that the SCEO method provides significantly superior results against the standard EO and other *** addition,the developed SCEO is implemented to deal with a mobile robot path planning(MRPP)task,and compared to some classical metaheuristic *** analysis results demonstrate that the SCEO approach provides the best performance and is a prospective tool for MRPP.

关键词： Metaheuristic algorithms equilibrium optimizer Cauchy mutation robot path planning

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VitalLink: NodeMCU IoT Solution for Vital Signs Surveillance 7

VitalLink: NodeMCU IoT Solution for Vital Signs Surveillance

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Ramitha Rajeswari, J. Chamanthi, Reddy Mary Posonia, A. Albert Mayan, J. Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Chennai India Sathyabama Institute of Science And Technology School of Computing Chennai India

ISBN: (纸本)9798350359299

This research study proposes the development of comprehensive vital sign surveillance designed for real-time patient assessment. The system integrates multiple sensors, including the LM35 temperature sensor, Heartbeat Rate (HBR) and Blood Oxygen (SpO2) Sensor, DHT11 humidity and temperature sensor, OLED display, and gyroscope sensor, to provide a holistic approach to vital signs. The LM35 sensor is employed for accurate temperature measurement, providing crucial data for monitoring the patient's thermal status. This information is essential for detecting fever or abnormal temperature variations, aiding in the early identification of potential health issues. The HBR and SpO2 sensor plays a vital role in cardiovascular vital signs. The system can assess the patient's cardiac and respiratory well-being by measuring the heart rate and blood oxygen levels. Abnormalities in these parameters can trigger timely alerts, enabling swift medical intervention. The DHT11 sensor enhances the system's capabilities by monitoring ambient temperature and humidity. This additional environmental data contributes to a more comprehensive understanding of the patient's surroundings, ensuring that external factors are considered in the health assessment process. Integrating an OLED display serves as an intuitive user interface, providing real-time feedback on the measured parameters. This ensures that patients and healthcare providers can easily interpret and respond to the health data presented by the system. The display can showcase temperature, humidity, heart rate, blood oxygen levels, and other relevant information in a user-friendly format. Furthermore, the gyroscope sensor adds a layer of sophistication to vital sign surveillance by enabling the assessment of body movement and posture. This feature is particularly beneficial for patients with mobility issues or those prone to falls. The gyroscope data can be analyzed to detect sudden movements or abnormal postures, triggering alerts

关键词： Heart

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Efficient Radon Levels Detection in Residential Areas using Cloud-Enabled Decision Trees for Real-Time Analysis 8

Efficient Radon Levels Detection in Residential Areas using ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Lenin, J. Rohit, V.Robin Nisha, F. Vasudevan, B. Sivakumar, S. Murugan, S. Alliance College of Engineering and Design Alliance University Department of Computer Science & Engineering Bangalore India Presidency University Computer Science & Engineering School of Computer Science & Engineering and Information Science Bengaluru India Cmr University School of Engineering and Technology Computer Science & Engineering Bangalore India Srm Institute of Science and Technology Department of Electronics and Communication Engineering Kattankulathur SRM Nagar Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

This research introduces a new approach to radon detection in homes utilizing Decision Trees (DTs) enabled by the cloud in real-time. High radon levels, a natural radioactive gas, are dangerous to human health. Quick evaluation and mitigation efforts are hindered by the long processes required by traditional detection systems. Using DTs, a machine learning methodology, the proposed method sorts of radon levels according to variables, including building materials, ventilation systems, and geographic location. These trees may handle both numerical and categorical data and provide interpretability. Fast processing of massive data sets and real-time analysis across varied residential areas are made possible by integrating cloud computing, increasing efficiency and scalability. Stakeholders may remotely access crucial information due to the solution's accessibility and flexibility. Results from controlled experiments demonstrate that this method can accurately identify radon levels in the air in real time, allowing for preventative actions to lessen potential health hazards. People living in areas prone to radon may benefit from this practice in terms of their health. © 2024 IEEE.

关键词： Indoor air pollution

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A Novel Approach on Deep Reinforcement Learning for Improved Throughput in Power-Restricted IoT Networks 1

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International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Sweety Bakyarani, E. Singh, Navneet Pratap Shekhawat, Jyoti Bhardwaj, Saurabh Chaku, Shweta Singh, Jagendra Department of Computer Science SRM Institute of Science and Technology Kattankulathur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Vivekanand Global University Rajasthan Jaipur India Department of Information Technology Raj Kumar Goel Institute of Technology AKTU Lucknow Ghaziabad India Department of Computer Science Engineering Inderprastha Engineering College AKTU Lucknow Ghaziabad India

ISBN: (数字)9789819986613

ISBN: (纸本)9789819986606

The rapid expansion of the Internet of Things (IoT) has stressed the importance of energy-efficient communication protocols, particularly in networks operating under power constraints. This paper presents a unique approach for managing communication in energy-limited IoT networks using a deep reinforcement learning (DRL)-based communication protocol. By integrating Sparse Code Multiple Access (SCMA), Code Division Multiple Access (CDMA) techniques, and the Combined Experience Replay Deep Deterministic Policy Gradient (CER-DDPG) algorithm, we developed a novel protocol to improve the throughput of power-constrained sensors in an IoT network. Through comprehensive simulations, we compared the proposed protocol’s performance with benchmark systems like traditional DDPG and stochastic algorithms. The results reveal superior energy efficiency and throughput with the proposed protocol, establishing its potential to significantly enhance the performance of energy-constrained IoT networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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International Journal of computers and Applications 2023年第5期45卷 379-390页

作者： Liu, J. Shao, H.J. Deng, X. Jiang, Y.T. School of Computer Jiangsu University of Science and Technology Zhenjiang China Department of Electrical and Computer Engineering University of Nevada Las VegasNV United States

The paper discusses the high computational costs associated with convolutional neural networks (CNNs) in real-world applications due to their complex structure, primarily in hidden layers. To overcome this issue, the paper proposes a novel channel pruning technique that leverages the correlation topology of feature maps generated by each CNNs layer to construct a network with fewer nodes, reducing computational costs significantly. Redundant channels exhibit a high degree of topological similarity and tend to increase as the number of network layers rises. Removing the channel corresponding to highly correlated feature maps allows retrieval of the ‘base’ set of characteristics needed by subsequent layers. The proposed channel pruning technique provides a promising approach to reducing the computational costs of deep convolutional neural networks while maintaining high performance levels. By designing a network structure optimized for specific input data types, the method results in more efficient and effective machine learning models. The pruning operation requires fine-tuning to optimize network performance, and experiments using X-ray, chest CT, and MNIST images show that the pruned network can eliminate approximately 80% of redundant channels with minimal performance deterioration (maintaining original CNNs performance at 99.2%). © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Topology

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Butterfly Optimized Feature Selection with Fuzzy C-Means Classifier for Thyroid Prediction

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Intelligent Automation & Soft Computing 2023年第3期35卷 2909-2924页

作者： S.J.K.Jagadeesh Kumar P.Parthasarathi Mehedi Masud Jehad F.Al-Amri Mohamed Abouhawwash Department of Computer Science and Engineering Kathir College of EngineeringCoimbatore641062India Department of Computer science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401TamilnaduIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.BOX 11099Taif21944Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura 35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

The main task of thyroid hormones is controlling the metabolism rate of humans,the development of neurons,and the signiﬁcant growth of reproductive *** medical science,thyroid disorder will lead to creating thyroiditis and thyroid *** two main thyroid disorders are hyperthyroidism and *** research works focus on the prediction of thyroid *** improve the accuracy in the classiﬁcation of thyroid disorder this paper pro-poses optimization-based feature selection by using differential evolution with the Butterﬂy optimization algorithm(DE-BOA).For the classiﬁer fuzzy C-means algorithm(FCM)is *** proposed DEBOA-FCM is evaluated with para-metric metric measures of sensitivity,speciﬁcity,and *** this work,the thyroid disease dataset collected from the machine learning University of Cali-fornia Irvine(UCI)database was *** accuracy rate for the Differential Evo-lutionary algorithm got 0.884,the Butterﬂy optimization algorithm got 0.906,Fuzzy C-Means algorithm got 0.899 and DEBOA+Focused Concept Miner(FCM)proposed work 0.943.

关键词： Fuzzy butterﬂy differential evolution thyroid hyperthyroid

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