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

作者： Pallavi, M. Mishra, Awakash Kudari, Jayashree M. Belwal, Ankit School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Acute disseminated encephalomyelitis (ADEM) is an extraordinary immune-mediated disease that influences the mind and spinal cord, central to the unexpected onset of neurologic symptoms. Time series evaluation can be used to become aware of ADEM in patients. Mainly, statistical analysis of the temporal styles of mind imaging and immunological parameters has proven effective for diagnosing ADEM in medical settings. Such material styles offer essential clues about disease development, which can be leveraged to diagnose ADEM patients appropriately. Acute disseminated encephalomyelitis (ADEM) is a critical degenerative sickness of the imperative nervous device, characterized by a rapid onset of neurologic signs and significant infection of the mind and spinal twine. Early analysis of ADEM is critical to initiate appropriate remedies and reduce long-term incapacity. In this look, we advocate the usage of time collection analysis to discover neurological patterns related to ADEM from the brain indicators of sufferers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Time series

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Exploring the Use of Mobile Ad-hoc Networks for Real-Time Automotive Applications 5th

Exploring the Use of Mobile Ad-hoc Networks for Real-Time Au...

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

作者： Raghavendra, R. Negi, Bichitra Singh Manasa, C.M. Mishra, Awakash Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala 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

ISBN: (纸本)9789819780426

This paper investigates using cell networks (MANETs) for real-time automotive packages. The car industry increasingly seeks to benefit from the latest technology to improve its merchandise, and cellular ad hoc networks can revolutionize the enterprise. This paper outlines the use of MANETs within the automobile industry and uses simulations to explore the benefits and constraints of implementing MANETs inside the automotive space. Additionally, the paper explores the safety and privacy concerns of MANETs and how automobile applications can use the era comfortably and competently. The findings of this observation provide the perception of how MANETs may be applied for automobile programs and highlight the potential of cell ad-hoc networks as a crucial technology for the industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mobile ad hoc networks

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A Novel Approach for Exploring Data-Driven Nutritional Insights Using Clustering and Dimensionality Reduction Techniques

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SN computer Science 2024年第8期5卷 1-22页

作者： Garg, Nandini Dwivedi, Pulkit Apex Institute of Technology (CSE) Chandigarh University Mohali India School of Computer Science and Engineering IILM University Greater Noida India

The analysis of high-dimensional datasets poses significant challenges, particularly in big data analytics where extracting meaningful insights is crucial. Current techniques often struggle with maintaining a balance between preserving global data structures and capturing local relationships. In this study, we address these challenges by integrating Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) for dimensionality reduction, with a dedicated focus on the domain of nutrition. Our research specifically targets pressing health concerns among the youth, such as obesity and nutritional deficiencies, by simplifying the analysis of extensive nutrition datasets. We propose a methodology that enhances the selection of nutritious food alternatives through effective data simplification and analysis. The efficacy of our approach is demonstrated through comprehensive experimental results, which include detailed comparisons with state-of-the-art methods, and evaluations based on clustering accuracy, computational efficiency, and visualization quality. Additionally, we optimize the performance of clustering algorithms using hyperparameter tuning techniques, specifically the Elbow Method and the Silhouette Coefficient. Our findings highlight the significant role of dimensionality reduction in improving data analysis and machine learning processes. This study offers valuable insights for researchers and practitioners, contributing to a deeper understanding of how dimensionality reduction techniques can unlock latent knowledge within vast datasets. Ultimately, our research aims to facilitate more informed decision-making and drive innovation in the era of big data analytics, with practical applications extending across diverse domains, particularly in nutrition and health. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Clustering Dimensionality reduction Machine learning Nutrition PCA t-SNE

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Leveraging Exponential Smoothing for Time Series Analysis of Wireless Sensor Networks 5th

Leveraging Exponential Smoothing for Time Series Analysis of...

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

作者： Alam, Intekhab Ojha, Ananta Verma, Tushar K. Venkatachalam, Amirtha Preeya Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Manduwala Dehradun Navgaon India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

The abstract should summarize the contents of the paper in short terms, i.e. 150–250. This paper explores the applications of exponential smoothing techniques in evaluating time collection records generated using Wireless Sensor Networks (WSNs). Empirical results demonstrate that exponential smoothing outperforms conventional time series models, supplying users with extra accurate analysis and forecasting of WSNs. The essential blessings of this method encompass decreased errors in predicting destiny values, progressed reliability, and stepped forward performance in developing forecasts. This paper affords a comprehensive review of exponential smoothing and its packages in WSNs, including an assessment of the outcomes of applying those techniques and the capability of future research avenues. In addition, it gives hints for practitioners trying to leverage exponential smoothing for time series analysis of WSNs. Exponential smoothing is a critical time series forecasting method that may be applied to evaluate Wi-Fi sensor Networks (WSN). It is miles primarily based on the belief that the fashion in a time collection is a clean continuation of past values. The rules work by calculating an exponentially weighted shifting average, wherein facts points are exponentially weighted and given more importance the closer they are to the present-day time point words. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： exponentially smoothing weighted

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Design of Haptic Sensing Snake Robot 7

Design of Haptic Sensing Snake Robot

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7th International Conference on Robotics, Control and Automation engineering, RCAE 2024

作者： Zhang, Yang Wu, Hao Li, Peng Bai, Deen Guo, Shuai Tang, Chaoquan China University of Mining and Technology School of Mechanical and Electrical Engineering Xuzhou China Harbin Institute of Technology Shenzhen School of Mechanical Engineering and Automation Shenzhen China Zhengzhou University School of Computer Science and Artificial Intelligence Zhengzhou China

ISBN: (纸本)9798350355642

Snake robots, a category of bionic robots, have garnered significant interest due to their capacity to adapt autonomously to complex unstructured environments. This is achieved through the flexibility and versatility of their bodies and gaits, as well as the coordinated movements of their bodies. However, in unknown or complex environments with obstacles, snake robots are unable to plan their next locomotor actions based solely on the sensory information obtained from the camera they carry. The incorporation of haptics is anticipated to facilitate the snake robot's perception of the interaction between its body and the external environment, thereby enabling more precise and effective guidance for efficient movement. This paper presents the design of a flexible tactile sensor based on the principle of photoconductivity, which has been developed to enable a snake robot to perceive and interact with its environment through tactile sensing. The sensor is intended to enhance the robot's autonomous movement capabilities in complex environments where visual recognition or human observation alone is insufficient. © 2024 IEEE.

关键词： flexible haptic sensor motion control obstacle-assisted motion sinuous motion snake robot

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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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Assessments of Channel Coding Performance in Data Compression and Source Coding 5th

Assessments of Channel Coding Performance in Data Compressio...

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

作者： Savita Bargavi, Manju Panwar, Rahul Shyam, Gopal Krishna Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Electrical Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India

ISBN: (纸本)9789819780426

Channel coding is a technique used to boom the performance and reliability of records transmission. It is able to be used for facts compression and source coding, that are the procedures of reducing the amount of records that desires to be despatched across a *** of channel coding performance can be performed with the aid of analyzing the performance of the records compression and source coding techniques. This can be executed thru diverse measures, such as compression ratio, supply coding charge, mistakes detection and correction, and bit blunders rate. With the intention to appropriately verify the performance of the channel coding approach, all of these measures must be considered. The aim of channel coding is to reap the highest viable fee of data compression with minimal errors detection and correction, and bit blunders rates as little as possible. This guarantees that the records is transmitted securely and efficaciously. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Coding errors

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Investigating the Advantages and Limitations of Math Modeling for Wireless Sensor Networks 5th

Investigating the Advantages and Limitations of Math Modelin...

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

作者： Zaidi, Taskeen Ansari, Tabish Sindhuri, Bonda Prema Alam, Intekhab Karnataka Bangalore India Department of Civil Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

This paper investigates the blessings and obstacles of math modeling for wireless Sensor Networks (WSNs). WSNs may be immediately evaluated on a performance basis or analyzed to address protection and reliability troubles. However, mathematical models ignore the sensors’ heterogeneity and the networks’ distributed nature. Similarly, the mission is to validate the results to ensure actual performance. Other WSNs that need attention and mathematical modeling include their management algorithms, optimizations, MAC protocols, power management, and *** comparison, sure advantages exist in math modeling, understanding complex problems, coming across new solutions and situations, and the capacity to expect effects. This paper aims to review the advantages and obstacles of math modeling for WSNs and concludes by providing hints on how WSNs can gain from mathematical modeling. Math modeling holds the splendid capacity for wireless Sensor Networks (WSNs) because it comprehensively clarifies complex actual-global troubles. Math modeling offers an established framework to conceptualize, examine, and endorse answers for diverse WSN deployments. Its relative predictability and simulation accuracy can assist in perceiving hidden insights and generate reliable outcomes. However, it has some critical boundaries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： modeling Sensor simulation

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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 23

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Wu, Xiaoqiang Zhu, Qingling Lin, Qiuzhen Chen, Weineng Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Reinforcement learning

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Multi-modal Physiological Signal Fusion for Emotion Classification: A Multi-Head Attention Approach 5

Multi-modal Physiological Signal Fusion for Emotion Classifi...

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2023 5th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Bai, Xuemei Tan, Jiaqi Hu, Hanping Zhang, Chenjie Gu, Dongbing School of Electronic Information Engineering Changchun University of Science and Technology Changchun130012 China School of Computer Science and Technology Changchun University of Science and Technology Changchun130012 China School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom

In this essay, a model-level fusion technique of multi-modal physiological signals using Multi-Head Attention is studied. A framework that utilizes multi-model physiological signals for the task of emotion classification is proposed. First, the GCRNN model, which combines the Graph Convolutional Network (GCN) and the Long and Short Term Memory (LSTM), captures the unique features of electroencephalogram (EEG) signals. The spatial and temporal information that makes up impulses from the EEG can be captured precisely by such a technique. The CCRNN model, which combines the Convolutional Neural Network (CNN) integrated with the Channel-wise Attention and the LSTM, is used for peripheral physiological signals. The model can extract useful features from peripheral physiological signals and automatically learn to weigh the importance of various channels. Finally, Multi-head Attention is employed to fuse the output of the GCRNN and CCRNN methods. The Multi-head Attention can automatically learn the relevance and importance of different modal signals and weigh them accordingly. Emotion classification is implemented by adding a component of Softmax to map what the model produced to discrete emotion categories. The DEAP dataset was utilized in this study for experimental verification, and the results indicate that the method using multi-modal physiological signal fusion is substantially greater in precision than the technique using simply EEG signals. Additionally, the Multi-head Attention fusion method performs better than previous fusion techniques. © Published under licence by IOP Publishing Ltd.

关键词： Electroencephalography

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