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E-learning: Research and Applications 14

E-Learning: Research and Applications

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14th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Sharma, Shantanu Gautam, Shreeyanshi Agarwal, Ayushi ABES Engineering College Uttar Pradesh Ghaziabad India

The goal of educational institutions should be to find effective ways to provide new and efficient learning opportunities based on their environment, student characteristics, teacher preparation, economic crisis, and advancing technology in an effort to make learning more efficient, equitable, and innovative in higher education. This study paper identifies the need for and possibility for developing new online courses in order to engage and motivate students in accordance with their demands (e-learning, blended learning, mobile learning). This paper presents the insights and harmonizes the acquired knowledge since the implementation of a mobile learning solution, reviews a few definitions of e-learning, explores the motivating factors behind its development, and provides an overview of the circumstances in which e-learning is a perfect option, as well as the main e-learning component types. It is concluded that e-learning is a useful tool for the growth of the Indian educational sector. Considering the idea of e-learning and examining the different types of e-learning are the key objectives of this research work. © Grenze Scientific Society, 2023.

关键词： E-learning

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Performance Evaluation and Analysis of Meta-Heuristic Techniques in Cloud computing 10

Performance Evaluation and Analysis of Meta-Heuristic Techni...

引用

10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer engineering, UPCON 2023

作者： Sharma, Sanjeev Rawat, Pradeep Singh School of Computing Dit University Uttarkhand Dehradun India

ISBN: (纸本)9798350382471

Cloud computing has emerged as a transformative technology, offering on-demand access to scalable computing resources. However, ensuring optimal performance while managing costs remains a challenging task. This research focuses on evaluating and analyzing the performance of various meta-heuristic techniques in cloud computing environments, with a specific emphasis on execution time and cost as critical performance metrics. The study begins by introducing the concept of meta-heuristic techniques, which are intelligent optimization algorithms that have gained prominence in solving complex, dynamic, and computationally expensive problems. These techniques, such as Base Genetic Algorithms, teaching learning base optimization, Elephant herding optimization, and Harmony search optimizer have the potential to optimize resource allocation in cloud computing, improving average execution time of tasks, and resource cost efficiency. The analysis of the results demonstrates the effectiveness of meta-heuristic techniques in reducing execution time and managing costs. The findings also highlight the trade-offs between different meta-heuristic approaches, helping cloud administrators make informed decisions when selecting the most suitable technique for specific use cases. In conclusion, this research contributes to the understanding of how meta-heuristic techniques can enhance the performance of cloud computing systems by optimizing resource allocation. By utilizing execution time and cost as key performance metrics, it provides valuable insights for cloud service providers and organizations seeking to improve the efficiency and cost-effectiveness of their cloud-based services. Ultimately, this work aims to facilitate the development of more efficient and cost-conscious cloud computing solutions in an increasingly dynamic and competitive digital landscape. Results shows the performance indicators with four key optimizer in which Elephant herding optimizer outperforms the base GA,

关键词： Allocation Cloud computing Cost efficiency Meta-Heuristics Optimization Virtual Machine

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Deep learning-based ECG signal processing algorithms and their applications in cardiac health monitoring 4

Deep learning-based ECG signal processing algorithms and the...

引用

4th international conference on Advanced Algorithms and Signal Image Processing, AASIP 2024

作者： Zhang, Zhiguo Zhonghuan Information College Tianjin University of Technology Tianjin China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

This paper explores deep learning-based algorithms for electrocardiogram (ECG) signal processing and their applications in cardiac health monitoring. Initially, we provide an overview of the fundamental principles of ECG signals and the suitability of deep learning for ECG signal processing. Subsequently, we discuss data preprocessing techniques and the selection and construction of deep learning models aimed at enhancing the accuracy of ECG signal processing. Finally, the focus is on the application of these algorithms in the detection of arrhythmias and the diagnosis of myocardial ischemia, validated through experimental results. The findings of this study offer new technological approaches for cardiac health monitoring and foster the application of deep learning in the field of ECG signal processing. © 2024 SPIE.

关键词： Electronic health record

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Spike Sorting and Event Detection in Neuromorphic computing 10

Spike Sorting and Event Detection in Neuromorphic Computing

引用

10th international conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Vaishnavi, Vakada G. Sai Sree Bhowmik, Biswajit National Institute of Technology Karnataka Ishwarchandra Vidyasagar Ait Lab Brics Laboratory Department of Computer Science and Engineering Mangalore Surathkal India

ISBN: (纸本)9798350384369

Neuromorphic computing is a cutting-edge field of research that focuses on designing and developing computer systems and hardware architectures inspired by the structure and functioning of the human brain. The main objective of neuromorphic computing is to create computational systems that imitate the parallel processing, learning, and energy efficiency observed in biological neural networks. Two essential tasks in the analysis of brain data from extracellular recordings are spike sorting and event detection. Spike sorting involves identifying and categorizing discrete neural spikes, while event detection focuses on recognizing specific patterns or occurrences within the spiking activity of neuromorphic networks. Typically, each neuron generates spikes with a characteristic shape, and these clusters correspond to the activity of different neurons. Here, the outcome of spike sorting is the identification of which spike corresponds to which neuron. In-depth analysis of the spike sorting and event detection which are two critical processes in interpreting the intricate neuronal activity that underpins brain function are provided. © 2024 IEEE.

关键词： Neurons

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teaching learning-Based Optimization for Solving CEC2014 Test Suite: A Comparative Study 4th

Teaching Learning-Based Optimization for Solving CEC2014 Tes...

引用

4th international conference on Innovative Manufacturing, Mechatronics and Materials Forum, iM3F2023

作者： Musa, Zulkifli Ibrahim, Zuwairie Shapiai, Mohd Ibrahim Faculty of Electrical and Electronics Engineering Technology Universiti Malaysia Pahang Pekan26600 Malaysia Faculty of Manufacturing Universiti Malaysia Pahang Pekan26600 Malaysia Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia

ISBN: (纸本)9789819988181

This is a comparative study of teaching learning-Based Optimization (TLBO) as a human-based algorithm against other types of metaheuristic algorithm: single-agent finite impulse response optimizer (SAFIRO), simulated Kalman filter (SKF), particle swarm optimization algorithm (PSO), black hole algorithm (BH), and genetic algorithm (GA), in solving CEC2014 test suite. The TLBO algorithm is inspired by the process of teaching and learning in a classroom. The advantages of TLBO are it only has two main tasks: teaching phase and learning phase and has no parameter setting. The TLBO performance provides a balance between exploration and exploitation. Statistical analysis is then carried out to rank the TLBO results to those obtained by other type of metaheuristic algorithm. The experimental result show that the TLBO algorithm is a promising approach and comparative to SAFIRO and SKF and has better than PSO, BH, and GA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Genetic algorithms

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Innovative Solar Ironing System: Combining Renewable Energy with Advanced Deep learning Techniques 4

Innovative Solar Ironing System: Combining Renewable Energy ...

引用

4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Priyadharshini, P. Presskila, X. Arogya Krishnan, R. Santhana Rajkumar, G. Vinoth Kumar, V. Vinoth Rekha, G. Psna College of Engineering and Technology Department of Information Technology Dindigul India Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Jp College of Engineering Department of Electronics and Communication Engineering Tenkasi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi Chennai India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331540364

This research study introduces an innovative solar-based ironing system that integrates renewable energy with advanced deep-learning techniques to optimize energy consumption in household ironing operations. The system harnesses solar panels to collect solar energy, ensuring efficient ironing during daylight hours. In cases of insufficient solar generation, the system automatically switches to a conventional power supply, guaranteeing uninterrupted functionality. A key feature of this system is incorporating a Long Short-Term Memory (LSTM) model, which predicts solar power output based on historic data and real-time climate information sourced from the OpenWeatherMap API. This predictive capability enhances energy management by providing timely insights into expected solar energy availability, allowing users to plan ironing tasks effectively. Data preprocessing techniques, including imputation of missing values using the median and normalization, ensure the quality and reliability of the input data for training the LSTM model. The model is assessed using various performance metrics and compared to traditional forecasting methods, including ARIMA, Gated Recurrent Units (GRU), and Feedforward Neural Networks (FNN). The results demonstrate that the LSTM model significantly outperforms these alternatives in predicting solar energy generation. This research contributes to sustainable living by promoting renewable energy usage in everyday tasks and illustrates the potential of combining deep learning with solar technologies for improved energy efficiency in household applications. © 2024 IEEE.

关键词： Clean energy

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Parameter-efficient Prompt learning for 3D Point Cloud Understanding

Parameter-efficient Prompt Learning for 3D Point Cloud Under...

引用

IEEE international conference on Robotics and Automation (ICRA)

作者： Sun, Hongyu Wang, Yongcai Chen, Wang Deng, Haoran Li, Deying Renmin Univ China Sch Informat Dept Comp Sci Beijing 100872 Peoples R China

ISBN: (纸本)9798350384581;9798350384574

This paper presents a parameter-efficient prompt tuning method, named PPT, to adapt a large multi-modal model for 3D point cloud understanding. Existing strategies are quite expensive in computation and storage, and depend on time-consuming prompt engineering. We address the problems from three aspects. Firstly, a PromptLearner module is devised to replace hand-crafted prompts with learnable contexts to automate the prompt tuning process. Then, we lock the pre-trained backbone instead of adopting the full fine-tuning paradigm to substantially improve the parameter efficiency. Finally, a lightweight PointAdapter module is arranged near target tasks to enhance prompt tuning for 3D point cloud understanding. Comprehensive experiments are conducted to demonstrate the superior parameter and data efficiency of the proposed method. Meanwhile, we obtain new records on 4 public datasets and multiple 3D tasks, i.e., point cloud recognition, few-shot learning, and part segmentation. The implementation is available at https://***/auniquesun/PPT.

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Efficient Skin Lesion based Classification System for Monkeypox Detection using VGG16 and Ensemble learning 10

Efficient Skin Lesion based Classification System for Monkey...

引用

17th INDIACom;2023 10th international conference on computing for Sustainable Global Development, INDIACom 2023

作者： Pabbi, Varinder Khullar, Vikas Angurala, Mohit Ramgarhia Institute of Engineering and Technology Department of Computer Applications Phagwara India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789380544472

Monkeypox is a recently emerged disease outbreak that has impacted the health of many individuals. This study focuses on developing a skin lesion-based classification system for monkeypox disease. Many recent works suggest that the direct implementation of machine learning (ML) and deep learning (DL) approaches for this task has not resulted in high accuracy. Therefore, this paper proposes an efficient technique that employs a hybrid of VGG16-based feature engineering, class balancing, and ensemble learning for the detection of monkeypox skin lesions. Specifically, DL-based feature extraction is applied, but the classification is carried out through ensemble learning techniques. The results demonstrate that the random forest, extra tree, and histogram gradient descent methods achieved better performance in terms of different performance metrics. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Feature extraction

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Survival Time Prediction of Patients with Soft Tissue Tumor Using Biopsy Pathological Images 13

Survival Time Prediction of Patients with Soft Tissue Tumor ...

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Joint 13th international conference on Soft computing and Intelligent Systems and 25th international Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Takahashi, Kota Morita, Kento Hagi, Tomohito Nakamura, Tomoki Asanuma, Kunihiro Sudo, Akihiro Wakabayashi, Tetsushi Graduate School of Engineering Mie University Department of Information Engineering Japan Graduate School of Medicine Mie University Department of Orthopedic Surgery Japan

ISBN: (纸本)9798350373332

Malignant soft tissue tumor are rare cancers and uncommon diseases. Ideally, rare cancers should be diagnosed by a pathologist who specializes in that organ, but there is a chronic shortage of pathologists in Japan. Pathological diagnosis by artificial intelligence (AI) is required to improve the reliability of diagnosis and to support pathological diagnosis of rare diseases. There are several studies to predict the prognosis of cancer patients using pathology images, but most of them requires a large dataset and capable machine learning model such as convolutional neural networks (CNN). Since soft tissue tumors are rare diseases, large datasets cannot be collected, and existing approaches would not perform enough for clinical diagnosis. Therefore, this paper proposes a survival time prediction method using unsupervised machine learning algorithms that extracts pathological image feature by autoencoder, and K-means clustering and K Nearest Neighbor (KNN) regression perform the survival time prediction based on the extracted image features. The experiments on 27 soft tissue tumor patients showed that the proposed method predicted the survival time with an average error of 17.89 months. © 2024 IEEE.

关键词： Biopsy

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Detecting Counterfeit Indian Currency A Comparative Analysis of Machine learning Algorithms and Image Processing 15

Detecting Counterfeit Indian Currency A Comparative Analysis...

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

作者： Vyshnavi, M.R.V. Varshitha, N.L. Harshith Kumar, Konda V. S. Singh, Tripty Sharma, Mansi Chatterjee, Suman Amrita School of Computing Department of Computer Science Engineering Bengaluru India Dehradun India Kongju National University Department of Mechanical Engineering Cheonan Korea Republic of

ISBN: (纸本)9798350370249

The degree of secured currency is one of the key areas that makes its contribution in solving the problem of economic insecurity, highlights the most important. Even as it can be quite strained to actually identify which precise time frame this fake process was prepared in, complex fakes render the use of validation techniques quite challenging. This paper has aimed at displaying and evaluating the overall outcome of the different strategies of image, and the acquired classifiers in the identification of the fake FI currency. The following are the subsequent of the other operation that was applied in order to achieve the objectives of the intended work;Image capture This is whereby the images to be used are taken by the appropriate camera or scanner. Others used include the Naïve Bayes, Support Vector Classifier and Gradient Descent and Artificial Neural Network in this paper. As in the conclusion, with the help of the mathematical formulation, it can be confirmed that in fact, within an experiment, Neural Networks do have a higher level of optimization, much more than an average image processing if it is provoked to identify fake notes. In other words if used and implemented with the best of the machine learning approach and best of the image processing method then solitaire for detection of the counterfeit currency can be made more dependable than all the combined methods stated above. The objective of this study is to build and compare the performance of different machine learning algorithms and image processing methodologies for the identification of fake Indian currency notes. © 2024 IEEE.

关键词： Counterfeit Detection Gradient Descent Image Processing Machine learning Mathematical Formulation Naïve Bayes Neural Networks Support Vector Classifier

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