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DES-KNORA Model: Developing an Intelligent System for Automatic Power Mode Adjustment in Electric Bicycles 15

DES-KNORA Model: Developing an Intelligent System for Automa...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Mani, S. Dutt, Varun Indian Institute of Technology Mandi School of Computing and Electrical Engineering Himachal Pradesh Kamand175005 India

ISBN: (纸本)9798350370249

Electric bicycles (e-bikes) have gained considerable popularity due to their environmentally friendly nature and suitability as a mode of transportation. However, they face challenges related to manual switches for power mode changes. A potential solution to this issue could be the application of machine learning to improve their performance and safety. Previous research has explored the use of machine learning for gear shifting in bicycles, but there remains a gap in the specific application of ML for automating power mode changes in electric bikes. In order to address this gap, the study proposes the design and evaluation of an intelligent system based on machine learning that can automatically adjust electric bike power modes for users. The research team utilized a Hero Lectro C7+ electric bike and designed and tested a novel ensemble model, the dynamic ensemble selection approach model (DES-KNORA), against several machine learning techniques to predict the need for power mode changes. The DES-KNORA ensemble model fit the measured data the best, with an F1-score of 0.9995 and an accuracy of 99.95%. The study's results show that the suggested method works in real life, making the ride smoother and less annoying for riders. © 2024 IEEE.

关键词： bio signal DES-KNORA dynamic ensemble selection electric bike machine learning terrain data

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Comparative Analysis of Machine learning Models for Water Quality Prediction 4

Comparative Analysis of Machine Learning Models for Water Qu...

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4th international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Krushna, Bode Vamsi Sasikala, D. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

Water Quality plays an essential role in assessing environmental health, which has profound implications for both ecosystems and human well-being. In this work, various machine learning models including Logistic Regression, Decision Trees, Gaussian Naive Bayes, Random Forest, Support Vector Machine (SVM), and XGBoost were employed to analyze and predict water quality. The dataset used for the analysis encompasses diverse water quality parameters such as hardness, pH levels, solids, chloramines, turbidity, sulfate among others. Class imbalances in the dataset is addressed with Synthetic Minority Over-sampling Technique(SMOTE). The research findings highlight the significant improvement in classification accuracy of underrepresented data points due to SMOTE, with SVM and XGBoost achieving a training accuracy of around 78% and 75%, respectively. The study used K-fold cross-validation techniques and strategies to handle convergence warnings, enhancing the credibility and adaptability of the predictions. This study not only contributes to the growing body of knowledge in water quality analysis but also provides practical insights for researchers and practitioners seeking to employ machine learning approaches in environmental monitoring, water contamination. These findings highlight the critical role of machine learning in facilitating informed decision-making for long-term water management, ensuring the health and longevity of communities and ecosystems. © 2024 IEEE.

关键词： Decision Trees Gaussian Naive Bayes Logistic Regression Machine learning Random Forest SVM Water quality management XGBoost

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A Back Propagation Based Optimized Regression learning Algorithm for Load Estimation in Power Systems

A Back Propagation Based Optimized Regression Learning Algor...

引用

2024 international conference on Sustainable Power and Energy, ICSPE 2024

作者： Shakya, Kapil Deep Jain, Preeti Jabalpur Engineering College Department of Electrical Engineering India

ISBN: (纸本)9798350366051

The strengths of soft computing and machine learning based algorithms are being harnessed globally to improve and optimize power system performance. One such application happens to be load forecasting in power systems, with the objective of minimizing the difference between demand and generation of electrical power. However, it is a nontrivial challenge as electrical load exhibits high volatility owing to several variables such as date, time, time of the year, socio economic factors, atmospheric conditions etc. Hence it is extremely difficult to estimate future trends in electrical load with high accuracy. Previous, conventional statistical techniques have been used in conjugation with existing load curves to estimate future load trends. However with emerging machine learning and deep learning algorithms rendering high accuracy of pattern recognition, forecasting electrical load employing machine learning and deep learning algorithms has become an active area of research. This paper presents a combination of data preprocessing and back propagation based optimized regression learning for electrical load forecasting. It has been shown that the proposed approach outperforms existing baseline approaches in the domain and attains a mean absolute percentage error (MAPE) of only 1.3% and an R wedge2 value of 0.97. © 2024 IEEE.

关键词： Back Propagation Load Forecasting Machine learning Mean Absolute Percentage Error (MAPE) Power Systems Optimization Regression

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Machine learning Approach for Human Brain Counselling 15

Machine Learning Approach for Human Brain Counselling

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Palkar, Jayashri D. Jain, Chetana Hasmukh Kashinath, Kendre Priyanka Vaidya, Anusha Omkar Borate, Vishal Kisan Mali, Yogesh Kisan Trinity Academy of Engineering Wagholi Pune India Dr D Y Patil College of Engineering & Innovation Talegaon Pune India G.H Raisoni College of Engineering and Management Wagholi Pune India

ISBN: (纸本)9798350370249

An innovative step in treating the mental health issues common to college students is the PEACE web tool. With the use of cutting-edge machine learning (ML) algorithms, this platform tailors counselling advice to each student's unique needs. By means of its creative methodology, PEACE not only pinpoints the unique needs of students, but also pairs them with counsellors who have specialized knowledge, which increases the counselling process' effectiveness. PEACE seeks to remove obstacles standing in the way of students seeking mental health assistance by offering a private and convenient online platform for counselling services. By streamlining the matching process, the application's machine learning algorithm guarantees that kids are matched with counsellors who are qualified to manage their challenges. This strategy not only helps to lessen the stigma associated with mental health issues, but it also makes support services more easily accessible, which eventually creates an atmosphere that is better for students' mental health in general. Furthermore, the PEACE application is a noteworthy development in the field of mental health support in addition to providing a prompt resolution to the urgent problems encountered by college students. PEACE advances the field of mental health care by incorporating state-of-the-art technologies into the counselling process, opening the door for more effective and individualized therapies. As such, the application is a ray of hope, providing a revolutionary method for resolving mental health issues among college students. ©2024 IEEE.

关键词： Adversarial machine learning

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Impact of Cloud computing on Agricultural Advancement using Data Mining Algorithms 2

Impact of Cloud Computing on Agricultural Advancement using ...

引用

2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Saravanan, S.K. Nisha, F. Robin Rohit, V. Lenin, J. Selvam, P.D. Rajmohan, M. Department of BigData and Network Security Saveetha School of Engineering SIMATS Tamil Nadu Chennai India School of Engineering and Technology Computer Science & Engineering CMR University Karnataka Bengaluru India Computer Science & Engineering School of Computer Science & Engineering and Information Science Presidency University Karnataka Bengaluru India Department: Computer Science & Engineering Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India Department of Electronics and Communication Engineering Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Tamil Nadu Chennai India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

Incorporating Internet of Things (IoT) algorithms, Ensemble learning, and Explainable AI (XAI) into the Reinforcement learning framework provides a comprehensive solution. The framework's dynamic paradigm for precision agriculture increases output while decreasing resource consumption. The system adapts to its environment in real-time based on what it has learned via its previous experiences, thanks to Reinforcement learning. By incorporating IoT algorithms, agricultural machinery may more easily gather and exchange data with cloud servers, improving precision and flexibility. By merging many models to provide reliable results, Ensemble learning allows us to significantly enhance our predictive abilities. The usage of XAI, which provides context and justification for computations, has the potential to increase public confidence in AI-powered agricultural systems. It demystifies decision-making and arms participants with relevant information. In order to take use of the scalability and processing capacity of cloud resources, agricultural algorithms may now be readily installed and controlled with the help of this framework. Data mining algorithms have the potential to change agriculture by fostering sustainable practices and addressing challenges of global food security, as this research elucidates. © 2023 IEEE.

关键词： Agricultural Innovation Cloud computing Data Mining Algorithms Ensemble learning Explainable AI (XAI) Internet of Things (IoT) Precision Agriculture Reinforcement learning Resource Optimization Sustainable Practices

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Deep Reinforcement learning offloading for Computation Cost Minimization of Wireless Powered Mobile Edge computing 24

Deep Reinforcement Learning offloading for Computation Cost ...

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2024 international Academic conference on Edge computing, Parallel and Distributed computing, ECPDC 2024

作者： Yu, Hang Yang, Jun School of Information Engineering Ningxia University Ningxia Yingchuan750021 China

ISBN: (纸本)9798400718168

The development of the Internet of Things has increased the demand for real-time communication and computing in user equipments (UEs), which is a challenge for the limited battery capacity and computing power of UEs. Mobile edge computing (MEC) is a solution for UEs to achieve low delay and low energy consumption by offloading compution-intensive tasks to edge servers. The combination of wireless power transfer (WPT) and MEC further extends the battery lifetime of UEs. Conventional offloading methods are inadequate in adapting to environmental variations and achieving the long-term performance of the wireless powered MEC system with random task requirements and wireless channel states of the UEs. We introduce a deep reinforcement learning framework that takes into account the wireless powered MEC system's long-term average computing cost. We utilize the deep deterministic policy gradient approach to effectively acquire knowledge on optimal task schedule and runtime of the edge server. Additionally, we optimize energy transmission power, offloading time, offloading ratio, and local computation time simultaneously to assess the performance of actions. To enhance exploration of actions, we introduce the differential evolution algorithm which expands and variegate the action generated by the policy network while also accelerating convergence. The simulation outcomes indicate that our proposed scheme outperforms four benchmark strategies in terms of reduced computing costs and accelerated convergence rate. © 2024 ACM.

关键词： Computation offloading

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Structuring Formal Methods into the Undergraduate Computer Science Curriculum 16th

Structuring Formal Methods into the Undergraduate Computer S...

引用

16th international Symposium on NASA Formal Methods (NFM)

作者： Ramnath, Sarnath Walk, Stephen St Cloud State Univ Dept Comp Sci & Informat Technol St Cloud MN 56301 USA St Cloud State Univ Dept Math & Stat St Cloud MN 56301 USA

ISBN: (纸本)9783031606977;9783031606984

There is an urgent need to emphasize and integrate Formal Methods into the undergraduate curriculum in Computer Science in the United States. We are entering an age defined by a highly interconnected, ubiquitous computing environment, with a large AI component. Knowing the precise capability of our systems is particularly vital to safeguard safety-critical and mission-critical applications. In such an environment, the lack of a well-structured exposure to formal methods is a serious shortcoming in our computing curricula. We examine the curricular, pedagogical, and organizational challenges involved in bringing Formal Methods into the mainstream of computing disciplines, note efforts made to address the challenges, and suggest further initiatives to help address them.

关键词： Computer Science Formal Methods Software engineering Curriculum Development teaching and learning

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A Comprehensive Analysis of Underwater Image Processing based on Deep learning Techniques 5

A Comprehensive Analysis of Underwater Image Processing base...

引用

5th international conference on Control, Communication and computing, ICCC 2023

作者： Sree Vidhya, K.S. Deepthi, P.S. Computer Science and Engineering LBS Institute of Technology for Women Kerala Thiruvananthapuram India APJ Abdul Kalam Technological University Kerala Thiruvananthapuram India

ISBN: (纸本)9798350334128

Underwater image processing has been an active research topic over the past few years as interest in marine observation and the use of ocean resources has increased. Different from conventional images, marine ecosystems are frequently subjected to challenging conditions such as underwater turbulence, low contrast, and high colour distortion as a result of the light's non-uniform attenuation as it passes through the water. To overcome these challenges, a good amount of work in conventional and deep learning based underwater image processing has been published over a period of time. Deep learning has demonstrated excellent performance improvement than the conventional approaches on the challenging vision tasks. In this survey, important underwater image processing methods using deep learning have been discussed. The major underwater metrics, common datasets, and challenges are also presented. © 2023 IEEE.

关键词： Generative adversarial networks

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Improving Time-Series Forecasting Performance Using Imputation Techniques in Deep learning

Improving Time-Series Forecasting Performance Using Imputati...

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2024 international conference on Smart computing, IoT and Machine learning, SIML 2024

作者： Utama, Agung Bella Putra Wibawa, Aji Prasetya Handayani, Anik Nur Irianto, Wahyu Sakti Gunawan Aripriharta Nyoto, Amat Universitas Negeri Malang Department of Electrical Engineering and Informatics Malang Indonesia Universitas Negeri Malang Department of Mechanical Engineering and Industry Malang Indonesia

ISBN: (纸本)9798350364101

This study investigates the effectiveness of various missing data imputation techniques on the performance of deep learning models for time-series forecasting using the Beijing PM2.5 dataset. Imputation methods including Mean, Mode, Median, Multiple Imputation by Chained Equations (MICE), Last Observation Carried Forward (LOCF), and K-Nearest Neighbors (KNN) are compared across different deep learning architectures: Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), Bidirectional LSTM Networks (Bi-LSTM), and Gated Recurrent Units (GRU). Performance is evaluated using metrics such as Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). Results indicate that the KNN imputation method consistently outperforms other techniques across all models, achieving lower MAPE (0.186) and RMSE (18.495) values. Furthermore, KNN-imputed models exhibit higher R2 values, indicating better model fit than other imputation methods. These findings underscore the importance of preprocessing in time-series forecasting and highlight the potential of KNN imputation to enhance predictive accuracy in real-world applications. Robust imputation strategies like KNN promise to improve forecasting accuracy and reliability, particularly in domains where accurate predictions are crucial for decision-making. Future research may focus on refining and integrating imputation techniques with advanced deep-learning methodologies to enhance forecasting capabilities in diverse application scenarios. © 2024 IEEE.

关键词： Forecasting

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Research on AGV Path Planning Based on Reinforcement learning 8

Research on AGV Path Planning Based on Reinforcement Learnin...

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8th international conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Zhang, Le Yin, Gang Li, Jiayang Jiang, Jiaqi Shenyang University College of Intelligent Science and Engineering Liaoning Shenyang China College of Information and Engineering University Liaoning Shenyang China

ISBN: (纸本)9798350302455

This article is based on reinforcement learning theory, constructs a complete real-time environment map, and designs a dynamic path planning method for Automated Guide Vehicle(Hereinafter referred to as AGV) in mixed flow workshops. In view of the problems of traditional reinforcement learning algorithm in path planning, this paper introduces the theory of prior knowledge, designs a heuristic reward function, proposes an action selection strategy with harmonic function, and constructs an improved reinforcement learning algorithm for AGV path planning, thus strengthening the effectiveness of AGV motion path exploration. Comparative simulation shows that the improved reinforcement learning algorithm converges faster, improves the reachability of environment map edges, and shortens the path finding time. © 2023 IEEE.

关键词： Motion planning

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