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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Jayahariprabhu, M. Hindusthan College of Engineering and Technology Department of Computer Science and Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798350371055

The paper suggests an ANFIS, or adaptable neuro-fuzzy inference system, to fix AGV navigation issues. Two ANFIS controllers regulate the AGV's left and right angular velocities to move it to the target location, while the other two controllers optimize the heading adjustment to avoid obstacles. That's the whole system. All three sensor readings-front distance (FD), correct distance (RD), and left distance (LD)are sent to the two velocity controllers for low-level motion control. Using the angle difference (AD) between the AGV's heading and the angle to the target, two heading controllers can determine the best course of action. The viability of the suggested ANFIS technique has been investigated via simulation tests run under two distinct situations. Using the MATLAB software package, the simulated results demonstrate ANFIS's ability to navigate and design paths effectively in a workspace containing static barriers. © 2024 IEEE.

关键词： AD AGV ANFIS FD LD

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Energy Efficient Transmitter Creation by Consuming Free Energy in Molecular Communication

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IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2025年第2期11卷 292-303页

作者： Jing, Dongliang Li, Linjuan Cheng, Zhen Lin, Lin Eckford, Andrew W. Northwest A and F University College of Mechanical and Electronic Engineering Yangling China Ministry of Agriculture and Rural Affairs Key Laboratory of Agricultural Internet of Things Yangling China Zhejiang University of Technology School of Computer Science and Technology Hangzhou310023 China Tongji University College of Electronics and Information Engineering Shanghai China York University Department of Electrical Engineering and Computer Science TorontoON Canada

Information molecules play a crucial role in molecular communication (MC), acting as carriers for information transfer. A common approach to get information molecules in MC involves harvesting them from the environment;however, the harvested molecules are often a mixture of various environmental molecules, and the initial concentration ratios in the reservoirs are identical, which hampers high-fidelity transmission techniques such as molecular shift keying (MoSK). This paper presents a transmitter design that harvests molecules from the surrounding environment and stores them in two reservoirs. To separate the mixed molecules, energy is consumed to transfer them between reservoirs. Given limited energy resources, this work explores energy-efficient strategies to optimize transmitter performance. Through theoretical analysis and simulations, we investigate different methods for moving molecules between reservoirs. The results demonstrate that transferring higher initial concentration molecules enhances transmitter performance, while using fewer molecules per transfer further improves efficiency. These findings provide valuable insights for optimizing MC systems through energy-efficient molecule transfer techniques. © 2015 IEEE.

关键词： Transmitters

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Research on pressure control system of evaporation tank based on adaptive fuzzy PID 2

Research on pressure control system of evaporation tank base...

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2nd International Conference on Physics, Photonics, and Optical engineering, ICPPOE 2023

作者： Xiong, Qi Huang, Wei Wang, Duanlian Wen, Zhiyuan College of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (纸本)9781510674684

For the field of fiber optic prefabricated rod manufacturing, raw material evaporation tank pressure control system of nonlinearity, hysteresis and other issues, this paper proposes to combine PID control algorithm and fuzzy control algorithm, designed based on the adaptive fuzzy PID evaporation tank pressure control system. The simulation results in Matlab show that, through the traditional PID and adaptive fuzzy PID control algorithm comparison analysis, obtained adaptive fuzzy PID control system response speed, small overshooting, good stability and other characteristics, can well meet the requirements of the control of evaporation tank pressure system. © COPYRIGHT 2024 SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Fuzzy control

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A GCN-based Model for Next POI Recommendation with Fusion of Global and Local Information

A GCN-based Model for Next POI Recommendation with Fusion of...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Luan, Wenjing Liu, Kaixuan Qi, Liang Liu, Kun Guo, Xiwang Shandong University of Science and Technology College of Computer Science and Engineering Qingdao266590 China College of Computer and Communication Engineering Liaoning Petrochemical University Fushun113001 China

ISBN: (纸本)9781665410205

Point of interest (POI) recommendation is a hot research topic. Current researches mainly focus on the analysis of personal check-in trajectories to obtain user preferences. However, a user's check-in data is generally sparse, and it is difficult to make accurate recommendation by only using the user's local information. Additionally, the public's check-in behavior may exhibit common visiting patterns, and incorporating global check-in information is beneficial for enhancing the learning of individual user preferences. Therefore, we propose a GCN-based model with Fusion of Global and Local information (GFGL) for the next POI recommendation. The model obtains global information such as spatial distance, social relationships., and transition probabilities from all users' visit trajectories, and utilizes graph convolution network (GCN) for learning of multi-dimensional global information. Next, we fuse global information with user local information through the user context information embedding module. Besides., a long short-term memory (LSTM) model and transformer model are used to learn the relationship between the user's sequential preference and non-adjacent visits in trajectories. Extensive experiments on two real-world datasets demonstrate the superiority of GFGL against state-of-the-art methods in the next POI recommendation. © 2024 IEEE.

关键词： Convolution

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Monkeypox Disease Classification from Skin Lesion Images using Deep Convolution Neural Network 3

Monkeypox Disease Classification from Skin Lesion Images usi...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Thangaraj, Rajasekaran Vadivel, Balasubramaniam Kumar, S. Manoj Sadesh, S. Karunakaran, P. Prakash, P. Nandha Engineering College Department of Computer Science and Engineering Erode India Sri Ramakrishna Institute of Technology Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Velalar College of Engineering and Technology Department of Artificial Intelligence and Data Science Erode India Nandha Engineering College Department of Artificial Intelligence and Data Science Erode India Velalar College of Engineering and Technology Department of Information Technology Erode India

ISBN: (纸本)9798331523893

Recently monkeypox outbreak has raised concerns due to its increasing number of cases and diverse dermatological symptoms in 2024, which can complicate early diagnosis due to similarities with other viral infections such as measles and chickenpox. To enhance diagnostic accuracy, transfer learning models and artificial intelligence (AI) have been explored as effective tools. Pre-trained models such as VGG16, VGG19, ResNet50V2, and MobileNetV2 have been employed for monkeypox detection, each with distinct trade-offs in terms of computational efficiency and diagnostic precision. MobileNetV2 offers superior efficiency, but this comes at the cost of reduced accuracy. VGG models provide higher accuracy but require much more computational *** these models, DenseNet121 stands out by achieving 99% accuracy while requiring 37% less computational power, making it the model optimized choice for efficient and accurate monkeypox classification. © 2025 IEEE.

关键词： Transfer learning

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Simulation and Control of Slope Bottlenecks Based on Cellular Automata in Mixed Traffic Flow

Simulation and Control of Slope Bottlenecks Based on Cellula...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhang, Fengqi Qi, Liang Luan, Wenjing Yang, Ruiping Guo, Xiwang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Communication Engineering Liaoning Petrochemical University Fushun113001 China

ISBN: (纸本)9781665410205

Traffic congestion frequently occurs on slope segments of highways, which is a typical bottleneck. With the development of connected and autonomous vehicle (CAV) technology, there will be a scene that CAVs and human driven vehicles (HDVs) co-exist. This work studies a slope bottleneck on highway in mixed traffic scenarios and proposes a traffic flow model for slope bottlenecks incorporating CAV platooning based on cellular automata. A novel traffic flow control strategy for slope bottlenecks is proposed based on variable speed limit (VSL) and vehicle platooning. Firstly, it divides the upstream section of the slope bottleneck into two zones for implementing VSL and vehicle platooning, respectively. Via speed restrictions within the VSL zone, the inflow of vehicles into the vehicle platooning zone is effectively mitigated to create low traffic density. In the vehicle platooning zone, a hybrid vehicle platooning method for mixed scenarios is proposed. The experimental results demonstrate that our strategy effectively enhances traffic flow of the slope bottleneck, thereby mitigating traffic congestion. © 2024 IEEE.

关键词： Traffic congestion

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Harnessing Ridge Regression and SHAP for Predicting Student Grades: An Approach Towards Explainable AI in Education 3rd

Harnessing Ridge Regression and SHAP for Predicting Student ...

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3rd International Semantic Intelligence Conference, ISIC 2023

作者： Katkar, Vijay Kadam, Swapnil Mulla, Juber Nadaf, Niyaj Department of Computer Science and Engineering Annasaheb Dange College of Engineering and Technology Maharashtra Sangli India

ISBN: (纸本)9789819773558

This paper presents a comparative analysis of several regression techniques for predicting student academic performance, with a particular focus on Ridge Regression and its interpretability through SHAP, a method of Explainable Artificial Intelligence (XAI). The models evaluated include Linear Regression, Ridge Regression, Lasso Regression, Elastic Net, Decision Tree Regression, Random Forest Regression, AdaBoost, Gradient Boosting, Bagging, XGBoost, and K-Nearest Neighbors. Using a dataset encompassing student family background, personal information, and recent academic grades, we trained and evaluated these models to predict future academic performance. Experimental results demonstrated that Ridge Regression outperformed all other models in predictive accuracy with the highest r2 score of 0.9036 and lowest RMSE of 1.4623. Furthermore, we employed SHAP values to interpret the predictions made by the Ridge Regression model, revealing key feature contributions to the model’s output. This research underscores the potential of Ridge Regression and SHAP in building predictive and interpretable models in the educational domain. This fusion of predictive accuracy and interpretability provides invaluable insights for educators and policy-makers alike in understanding and enhancing student academic achievement. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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Improved Soil Quality Prediction Model Using Deep Learning for Smart Agriculture Systems

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computer Systems science & engineering 2023年第5期45卷 1545-1559页

作者： P.Sumathi V.V.Karthikeyan M.S.Kavitha S.Karthik Department of Information Technology SNS College of EngineeringCoimbatoreTamilnaduIndia Department of Electrical and Electronics Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia

Soil is the major source of infinite lives on Earth and the quality of soil plays significant role on Agriculture practices all ***,the evaluation of soil quality is very important for determining the amount of nutrients that the soil require for proper *** present decade,the application of deep learning models in many fields of research has created greater *** increasing soil data availability of soil data there is a greater demand for the remotely avail open source model,leads to the incorporation of deep learning method to predict the soil *** that concern,this paper proposes a novel model called Improved Soil Quality Prediction Model using Deep Learning(ISQP-DL).The work considers the chemical,physical and biological factors of soil in particular area to estimate the soil ***,pH rating of soil samples has been collected from the soil testing laboratory from which the acidic range has been categorized through soil test and the same data has been taken as input to the Deep Neural Network Regression(DNNR)***,soil nutrient data has been given as second input to the DNNR *** utilizing this data set,the DNNR method is used to evaluate the fertility rate by which the soil quality has been *** training and testing,the model uses Deep Neural Network Regression(DNNR),by utilizing the *** results show that the proposed model is effective for SQP(Soil Quality Prediction Model)with efficient good fitting and generality is enhanced with input features with higher rate of classification *** results show that the proposed model achieves 96.7%of accuracy rate compared with existing models.

关键词： Soil quality classification accuracy deep learning neural network soil features training and testing

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A GCN-Based Trip Recommendation Method Incorporating Reverse Effect

A GCN-Based Trip Recommendation Method Incorporating Reverse...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Luan, Wenjing Wang, Xueyao Qi, Liang Liu, Kun Guo, Xiwang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Communication Engineering Liaoning Petrochemical University Fushun113001 China

ISBN: (纸本)9781665410205

In location-based services (LBS), trip recommendation accuracy is challenged by diverse user preferences and complex transfer behaviors. Previous studies overlook the reverse effect of following POIs on previously visited ones. To address this, we propose a Graph-based Double-layer Bidirectional Trip Recommendation (GDB-TR) model. This model uses a heterogeneous graph to model user check-in trajectories with spatial and temporal information. Subgraphs are extracted from the heterogeneous graph, and an adjacency matrix is built for each subgraph. These matrices are fused through a neural network to obtain vector representations for POIs and POI categories. GDB-TR's core is a double-layer bidirectional neural network: one layer describes POIs, the other POI categories. Bidirectional computation captures the influence of preceding POIs on following ones and vice versa. Experiments on five real-world datasets demonstrate GDB-TR's superiority over baseline models, measured by F1 and pairs-F1 metrics. © 2024 IEEE.

关键词： Undirected graphs

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Stress Analysis Prediction for Coma Patient Using Machine Learning 5th

Stress Analysis Prediction for Coma Patient Using Machine Le...

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5th International Conference on Data and Information sciences, ICDIS 2023

作者： Alwin Infant, P. Charulatha, J. Sadhana, G. Ragavendra, K. Computer Science and Engineering Panimalar Engineering College Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India

ISBN: (纸本)9789819969050

Today’s working IT professionals frequently struggle with stress issues. The patient is now more likely to experience stress due to changing lifestyle and workplace cultures. Even while many companies and sectors deliver welfare programs and also make efforts to enhance work environments, the issue remains off from grasp. In this paper, we will utilize various techniques to analyze the stress patterns of working people and identify the variables that exert a massive effect on the system's distress. By utilizing machine learning techniques to forecast models to predict the threats of stressor encountered prognosis through using machine learning, we hope to streamline this procedure. In the working class, mental health illnesses linked to stress are not unusual. Concerns about the same have previously been highlighted by several researchers. In order to do this, information from coma patient answers from working medical experts’ mental health data was taken into consideration. After data extraction and preprocessing, various machine learning approaches were employed to supervise our framework. In this case, the mechanism can apply the machine learning techniques such as decision tree, random forest, KNN, logistic regression, and Naive Bayes. Results of the experiments demonstrated the system’s improved performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

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