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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Subburaj, Venkateshwari Pandiyarajan, Pandiselvam School of Computing Department of Computer Application Kalasalingam Academy of Research and Education TamilNadu Krishnankoil India School of Computing Department of Computer Science and Engineering Kalasalingam Academy of Research and Education TamilNadu Krishnankoil India

ISBN: (纸本)9798331518097

Fetal brain anomaly prediction is important for fetal medicine, as well as for prenatal health care. Fetal anomalies are classified into two types, anomalies in the fetus' body parts including heart, lung, and kidney anomalies in facial features. Thus, to predict the anomalies in the fetal brain, various deep-learning models are used in this paper. The LeNet, AlexNet, ResNet, VGGNet, GoogLeNet and ZFNet were used. From the results obtained, the ResNet method achieved 99% accuracy with the given 3D ultrasound images of the fetal brain compared with other models and benchmark algorithms like SVM, Logistic regression, and KNN. The results of this paper are useful to identify fetal anomalies. Hence, this approach will be useful for improving outcomes for individuals and society as much as possible. © 2024 IEEE.

关键词： Prediction models

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Enhancing Agricultural IoT with Predictive Analytics and Reinforcement Learning Models 2

Enhancing Agricultural IoT with Predictive Analytics and Rei...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Krishnan, R. Santhana Raj, J. Relin Francis Christopher, V. Bibin Nandini, N. Muthu, A. Essaki Puvanadevi, C. SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Ssveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Electronics and Communication Engineering Chennai India Faculty of Engineering and Technology SRM Institute of Science and Technology Department of Computing Technologies School of Computing College of Engineering and Technology Kattankulathur Chennai India RajaRajeswari College of Engineering Department of Electrical and Electronics Engineering Bangalore India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Adhiyamaan College of Engineering Department of Information Technology Hosur India

ISBN: (纸本)9798350317558

This research explores the augmentation of Agricultural Internet of Things (IoT) systems through the integration of advanced predictive analytics and reinforcement learning models. A novel algorithm, termed "CropQL,"is proposed to optimize crop yield prediction and resource allocation in precision agriculture. CropQL amalgamates the strengths of the CropNet IoT model with Q-Learning for reinforcement learning and Random Forest for predictive analytics. Simulation analyses are conducted to evaluate the performance of CropQL against established algorithms, employing diverse metrics such as precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The proposed CropQL algorithm exhibits superior predictive accuracy, demonstrating a significant enhancement in crop yield forecasting when compared to baseline algorithms. Precision and recall metrics reveal the algorithm's efficacy in minimizing false positives and negatives, ensuring precision agriculture systems make informed decisions. F1 score indicates the balanced optimization of precision and recall, reinforcing the algorithm's robustness. AUC-ROC analysis further corroborates the superior discriminative power of CropQL in distinguishing between positive and negative instances. In comparison to existing algorithms, including AgriSensNet, PrecisionCropNet, and IoTFarmGuard, CropQL consistently outperforms in predictive accuracy and resource optimization. The simulation results underscore the efficacy of the proposed algorithm in enhancing the efficiency of agricultural IoT systems, providing a promising avenue for sustainable precision farming. © 2024 IEEE.

关键词： Agricultural IoT CropQL Precision Agriculture Predictive Analytics Reinforcement Learning Sustainable Farming

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Early Detection of Amyotrophic Lateral Sclerosis with Time Collection Evaluation 5th

Early Detection of Amyotrophic Lateral Sclerosis with Time C...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Mehar, Palavancha Tirumala Krishna Agarwal, Trapty Suneetha, K. Ahmad, Naseem School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India

ISBN: (纸本)9789819780426

This paper discusses the usage of time series analysis for the early detection of Amyotrophic Lateral Sclerosis (ALS). A time series approach was used to expand a set of rules that may detect the early stages of ALS via analyzing symptomatic changes in signs and symptoms consisting of muscle energy, taking walks potential, speech, swallowing, and different bodily motion. This technique become examined on a retrospective dataset, and proven if you want to come across changes in the circumstance of ALS in a well-timed way. The outcomes suggest that this technique has ability as a tool for early detection in ALS sufferers. The proposed algorithm ought to potentially be used to improve the present day standard of care and help lead to in advance analysis and higher scientific effects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motion Potential Swallowing

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Comparative Analysis of Bitcoin Price Prediction Models: LSTM, BiLSTM, ARIMA and Transformers 15

Comparative Analysis of Bitcoin Price Prediction Models: LST...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Chennupati, Anjali Prahas, Bhamidipati Ghali, Bharadwaj Aaditya Jasvitha, Bommisetty Durga Murali, K. Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Mathematics Bengaluru India

ISBN: (纸本)9798350370249

The proposed work explores the significance of Bitcoin in today's financial landscape and its role as a decentralized store of value and hedge against economic uncertainty. The diverse forecasts for Bitcoin prices are proposed and the importance of accurate prediction models. Specifically, it emphasizes the effectiveness of Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) models in capturing the complex dynamics of cryptocurrency markets, offering insights for traders, investors, and researchers. The growing importance of Deep Neural Networks (DNNs) is highlighted by analyzing historical market data and forecasting future price movements. The proposed work concludes by underscoring the evolution of Bitcoin price prediction methodologies from traditional models like ARIMA to advanced techniques like Bi-LSTM, and Auto-regressive EncoderDecoder Transformer, enhancing financial security and decision-making in the cryptocurrency market. The Bi-LSTM worked by providing a 0.9832 R2 score. © 2024 IEEE.

关键词： Decentralized finance

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Grape Guard:A YOLO-based mobile application for detecting grape leaf diseases

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Journal of Electronic science and Technology 2025年第1期23卷 60-75页

作者： Sajib Bin Mamun Israt Jahan Payel Md.Taimur Ahad Anthony S.Atkins Bo Song Yan Li Department of Computer Science and Engineering Daffodil International UniversityDhaka1207Bangladesh Department of Computer Science University of Southern QueenslandToowoomba4350Australia Faculty of Digital TechnologyInnovationand BusinessStaffordshire UniversityStoke-on-TrentST42DEUnited Kingdom School of Engineering University of Southern QueenslandToowoomba4350Australia School of Mathematics Physics and ComputingUniversity of Southern QueenslandToowoomba4350Australia

Grape crops are a great source of income for *** yield and quality of grapes can be improved by preventing and treating *** farmer’s yield will be dramatically impacted if diseases are found on grape *** detection can reduce the chances of leaf diseases affecting other healthy *** studies have been conducted to detect grape leaf diseases,but most fail to engage with end users and integrate the model with real-time mobile *** study developed a mobile-based grape leaf disease detection(GLDD)application to identify infected leaves,Grape Guard,based on a TensorFlow Lite(TFLite)model generated from the You Only Look Once(YOLO)v8 model.A public grape leaf disease dataset containing four classes was used to train the *** results of this study were relied on the YOLO architecture,specifically YOLOv5 and *** extensive experiments with different image sizes,YOLOv8 performed better than ***8 achieved 99.9%precision,100%recall,99.5%mean average precision(mAP),and 88%mAP50-95 for all classes to detect grape leaf *** Grape Guard android mobile application can accurately detect the grape leaf disease by capturing images from grape vines.

关键词： Bacterial diseases Grape Guard Mobile-based application YOLOv5 YOLOv8

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Innovative Image Processing Techniques for Automating Cosmetic Inspection 5th

Innovative Image Processing Techniques for Automating Cosmet...

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5th International Conference on Data science, Machine Learning and Applications, ICDSMLA 2023

作者： Panwar, Govind Singh Murthy, D.H.R. Agarwal, Trapty Jaison, Feon Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Progressive photograph processing techniques were developed for automating the technique of beauty inspection. These techniques make use of the wealthy, unique image records of the inspected objects to hastily become aware of diverse product defects. Specifically, object features including coloration, texture, size, shape, and location are first extracted from the photograph, and every function is then evaluated to come across cosmetic defects. Following the detection technique, the extracted capabilities are used to at once alter the manufacturing technique, enabling faster production turnaround times and progressed product quality. The strategies are designed to make certain rapid and accurate product inspection and also provide a bendy adjustment of parameters for various types of cosmetic merchandise. With these techniques, producers are capable of reduce cosmetically based totally product defects and supply improved great merchandise in a shorter time frame. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Inspection

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A hybrid machine-learning model for solar irradiance forecasting

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Clean Energy 2024年第1期8卷 100-110页

作者： Ameera M.Almarzooqi Maher Maalouf Tarek H.M.El-Fouly Vasileios E.Katzourakis Mohamed S.El Moursi Constantinos V.Chrysikopoulos Department of Industrial and Systems Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE Electrical Engineering and Computer Science Department Khalifa University Abu DhabiUAE Department of Civil Infrastructure and Environmental Engineering Khalifa UniversityAbu DhabiPO Box 127788UAE School of Chemical and Environmental Engineering Technical University of CreteChaniaGreece

Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic(PV)power *** plants face operational challenges and scheduling dispatch difficulties due to the fluctuating nature of their power *** the generation capacity within the electric grid increases,accurately predicting this output becomes increasingly essential,especially given the random and non-linear characteristics of solar irradiance under variable weather *** study presents a novel prediction method for solar irradiance,which is directly in correlation with PV power output,targeting both short-term and medium-term forecast *** proposed hybrid framework employs a fast trainable statistical learning technique based on the truncated-regularized kernel ridge regression *** proposed method excels in forecasting solar irradiance,especially during highly intermittent weather periods.A key strength of our model is the incorporation of multiple historical weather parameters as inputs to generate accurate predictions of future solar irradiance values in its scalable *** evaluated the performance of our model using data sets from both cloudy and sunny days in Seattle and Medford,USA and compared it against three forecasting models:persistence,modified 24-hour persistence and least *** on three widely accepted statistical performance metrics(root mean squared error,mean absolute error and coefficient of determination),our hybrid model demonstrated superior predictive accuracy in varying weather conditions and forecast horizons.

关键词： solar power generation kernel ridge regression hybrid model forecasting

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MOALF-UAV-MEC: Adaptive Multi-Objective Optimization for UAV-Assisted Mobile Edge computing in Dynamic IoT Environments

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IEEE Internet of Things Journal 2025年第12期12卷 20736-20756页

作者： Al-Bakhrani, Ali A. Li, Mingchu Obaidat, Mohammad S. Amran, Gehad Abdullah Dalian University of Technology School of Software Dalian116024 China Albaydha University Al Bayda Yemen Jiangxi Normal University School of Computer and Information Engineering Jiangxi China School of Software Technology Dalian University of Technology Nanchang Dalian China University of Science and Technology School of Computer and Communication Engineering Beijing100083 China SRM University School of Computing Department of Computational Intelligence TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India Dalian University of Technology Department of Management Science and Engineering Dalian116024 China

The proliferation of Internet of Things (IoT) devices and computation-intensive applications has led to unprecedented demands on network resources and computing capabilities. This paper presents MOALF-UAV-MEC, a novel Multi-Objective Adaptive Learning Framework for UAV-Assisted Mobile Edge computing (MEC) tailored for dynamic IoT environments. The framework integrates Multi-Objective Reinforcement Learning (MORL), Model Predictive Control (MPC), Adaptive Particle Swarm Optimization (APSO), and Lyapunov Optimization to optimize UAV trajectories, dynamic resource allocation, and system stability. MOALF-UAV-MEC addresses critical challenges in UAV-assisted MEC, including multi-objective optimization, adaptive resource allocation, energy efficiency, scalability, and quality of service guarantees. Our approach employs a unique burst mode feature for UAVs, enabling temporary performance boosts in high-demand situations. Extensive simulations demonstrate the framework's efficiency in enhancing task completion rates, energy efficiency, and long-term system sustainability. Results show a task completion rate of 94.50%, significantly outperforming existing approaches, with an average of 1,890 completed tasks per UAV and a load balancing efficiency of 96%. The framework exhibits robust adaptive behavior, achieving a 38% reduction in UAV route optimization and a 55% increase in task completion during high-load periods. This research contributes to the advancement of edge computing in IoT environments, offering a scalable and adaptive solution for deploying computational resources in areas with limited infrastructure, during temporary events, or in emergency situations. © 2014 IEEE.

关键词： Model predictive control

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The propagation of Quasi-static region during granular impact

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Particuology 2023年第12期83卷 1-7页

作者： Moyu Yang Ran Li Yingnan Xiu Qiang Zeng Xiaoyan Ye Hui Yang School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Center for Particle-Laden Turbulence Key Laboratory of Mechanics on Disaster and Environment in Western China attached to the Ministry of Education of ChinaDepartment of Mechanics and Engineering ScienceSchool of Civil Engineering and MechanicsLanzhou UniversityLanzhou730000China

In this paper,through experiments and DEM simulations,it is found that there is a ring-shaped region named Quasi-static Region between the particles expanding outward and the particles collapsing inward during the impact ***-static Region is always generated from the impact point at the same time,and then spreads out at a uniform *** the propagation of Quasi-static Region,the velocity of the particles in the particle extend region varies linearly in space and *** the change rate is only related to the properties of the particle *** simulation results show that the particle flow in Quasi-static Region is elastic-inertial flow,while the particle flow expanding outward and collapsing inward is inertial flow.

关键词： Impact Quasi-staticregion Propagation Shearflow

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Generating animatable 3D cartoon faces from single portraits

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虚拟现实与智能硬件（中英文） 2024年第4期6卷 292-307页

作者： Chuanyu PAN Guowei YANG Taijiang MU Yu-Kun LAI Department of Electrical Engineering and Computer Sciences University of California BerkeleyBerkeley CA 94704USA Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China School of Computer Science and Informatics Cardiff UniversityCardiff CF244AGUK

Background With the development of virtual reality(VR)technology,there is a growing need for customized 3D ***,traditional methods for 3D avatar modeling are either time-consuming or fail to retain the similarity to the person being *** study presents a novel framework for generating animatable 3D cartoon faces from a single portrait *** First,we transferred an input real-world portrait to a stylized cartoon image using *** then proposed a two-stage reconstruction method to recover a 3D cartoon face with detailed *** two-stage strategy initially performs coarse estimation based on template models and subsequently refines the model by nonrigid deformation under landmark ***,we proposed a semantic-preserving face-rigging method based on manually created templates and deformation *** Compared with prior arts,the qualitative and quantitative results show that our method achieves better accuracy,aesthetics,and similarity ***,we demonstrated the capability of the proposed 3D model for real-time facial animation.

关键词： 3D reconstruction Cartoon face reconstruction Face rigging Stylized reconstruction Virtual reality

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