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Addressing the role and opportunities of machine learning utilization in brain tumor detection

Procedia computer science 2024年 245卷 869-878页

作者： Vallerie Delia Lesmana Holly Agustine Irma Kartika Wairooy Brilly Andro Makalew Computer Science Department School of Computer Science - Binus University Jakarta 11480 Indonesia Mobile Application & Technology Program Computer Science Department - Binus University Jakarta 11480 Indonesia

This research aims to develop a brain tumor detection model by utilizing the machine learning techniques and Convolutional Neural Network (CNN). A significant matter to address is revolving around early detection and the proper handling regarding the brain tumor. This research's methodology consists of collecting the dataset, identifying the tools and language to use, prepare and preprocessing the data, data augmentation, splitting and label encoding, building the model architecture, compiling the model, training, and evaluating the model, predicting the model, and comparing it with other models. Dataset consists of 7022 MRI images, divided into training and testing subsets; and four classes: glioma, meningioma, pituitary, and no tumor. There are four different CNN models that have been built and evaluated, namely VGG16, InceptionV3, ResNet50, and DenseNet121. The result gained shows VGG16 with the best performance achieving an accuracy rate of 96.43%, followed by DenseNet121 (94,96%), InceptionV3 (92,40%), and ResNet50 (78,69%). Although there is still room for improvement regarding overfitting and increasing the models’ overall performance, this result is promising enough to enhance early diagnosis and offer an appropriate and effective treatment for patients.

关键词： Machine Learning Deep Learning CNN Brain Tumor Detection MRI VGG16 InceptionV3 ResNet50 DenseNet121

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Aspect-based Sentiment Analysis using Long Short-term Memory Model for Leveraging Restaurant Service Management

Aspect-based Sentiment Analysis using Long Short-term Memory...

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2023 International Conference on computer science, Information technology and Engineering, ICCoSITE 2023

作者： Heryadi, Yaya Wijanarko, Bambang Dwi Fitria Murad, Dina Tho, Cuk Hashimoto, Kiyota BINUS Graduate Program-Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Computer Science Department Jakarta11480 Indonesia BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Prince of Songkla University Interdisciplinary Graduate School or Earth System Science and Andaman Natural Disaster Management Phuket83120 Thailand

ISBN: (纸本)9798350320954

In general, the hospitality industry has been acknowledged as a major sector that gives a high contribution to economic development in many countries including Indonesia. For that reason, many initiatives have been implemented to help the growth of the hospitality industry in many countries including Indonesia to rebound from the harsh impact of the Covid-19 Pandemic. One such initiative is improving restaurant services as the main sector of the hospitality industry. This paper presents empirical results of sentiment analysis as a means to assess the quality of restaurant services as the first step to improving service quality. In particular, this study explores the aspect-based sentiment analysis method to identify some aspects of restaurant service which need improvement by learning the polarity of customers toward the restaurant services without having to meet the customers directly. By using the aspect-based sentiment analysis method, the customer sentiments comprising opinions, sentiments, evaluations, attitudes, and emotions from restaurant service can be analyzed using customers' online reviews as input. The main experiment findings showed that the Long Short-term Memory model can achieve high performance in predicting aspect polarization in restaurant service reviews. Other findings suggest that Sigmoid as an activation function achieved 0.97 average training accuracy and 0.69 average testing accuracy giving a better performance to the model in comparison to ReLU, Tanh, and ELU activation functions. © 2023 IEEE.

关键词： Sentiment analysis

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Restaurant Customer Feedback Sentiment Analysis using Aspect Embedding Long Short-term Memory Model

Restaurant Customer Feedback Sentiment Analysis using Aspect...

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2023 International Conference on computer science, Information technology and Engineering, ICCoSITE 2023

ISBN: (纸本)9798350320954

In the past ten years, there has been a proliferation of unstructured textual data from a variety of fields, allowing for the analysis of the sentiment polarity of the text authors in each of those datasets. To analyze sentiment polarity from unstructured data, machine learning tasks such as aspect-based sentiment analysis are used. The public dataset of restaurant patron feedback was used as the input for this study's empirical results of aspect-based sentiment analysis utilizing the Aspect Embedding Long Short-Term Memory model. The primary experiment's results show that the aspect embedding long short-term memory model can predict aspect sentiment polarization from customer feedback to restaurant service and aspect text representation (aspect text embedding) as input with excellent performance. For example, the Aspect Embedding Long Short-term Memory model, which uses ReLU as its activation function in the Dense layer tends to achieve higher performance (0.97 average accuracies) than the same model equipped with Sigmoid, and Tanh functions which achieve 0.954 and 0.956 average training accuracy respectively. © 2023 IEEE.

关键词： Sentiment analysis

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Estimation of Circadian Rhythms through EEG Signals by Temporal-Scale-Specific Fractal Dimensions 4

Estimation of Circadian Rhythms through EEG Signals by Tempo...

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4th IEEE Global Conference on Life sciences and Technologies, LifeTech 2022

作者： Tran, Anh Tu Iinuma, Yuta Nobukawa, Sou Chiba Institute of Technology Graduate School of Information and Computer Science Narashino Japan Chiba Institute of Technology Department of Computer Science 275-0016 Japan

ISBN: (纸本)9781665419048

Circadian rhythms are involved in many human behaviors and biological signals. The critical role of circadian rhythm in maintaining homeostasis is well established, in which disturbances in circadian rhythms affect health. Various methods using markers of body movement can indirectly detect circadian rhythms. Direct capture of these rhythms requires use of the electroencephalogram (EEG) method. Recently, studies on circadian rhythm have used Higuchi's fractal dimension (FD) to indicate the degree of complexity of brain activity. Our recent study showed that circadian rhythms reflect different temporal-scale complexities. In this context, we hypothesized that the relevant range of parameters focusing on temporal-scale dependency in Higuchi's FD might improve the detection accuracy of circadian rhythms. To prove this hypothesis, we evaluated the optimized parameters to maximize the difference in FD during the day. © 2022 IEEE.

关键词： Electroencephalography

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Overview of Parallel Computing for Meta-Heuristic Algorithms

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第3期7卷 656-681页

作者： Sun, Ying Chu, Shu-Chuan Hu, Pei Watada, Junzo Si, Mingchao Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology No.579 Qianwan’gang Road Shandong Qingdao266590 China Graduate School of Information Production and Systems Waseda University Kitakyushu808-0135 Japan College of Computer Science and Engineering Shandong University of Science and Technology China Chaoyang University of Technology Taiwan Taiwan Fujian University of Technology China

The meta-heuristic algorithm is used in the research of various complex problems. Due to the limitations of the original meta-heuristic algorithm, many improved meta-heuristic algorithms have been proposed, such as compact, adaptive, multi-objective and parallel schemes. Among them, the parallel strategy may get a significant improvement for related applications. This paper mainly studies the application of parallel computing in meta-heuristic algorithms. There are two main types of parallelism: one is absolute parallelism, using multiple processors which can solve optimization problems with high computational costs and improve execution efficiency. The other is virtual parallelism (multi-grouping), which decomposes the population into multiple sub-populations, and each sub-population communicates between species to generate better solutions. In addition, the combination of parallel computing and meta-heuristic algorithms can solve a wide variety of application problems: path planning, engineering design, large-scale optimization, image segmentation, neural networks and prediction problems, etc. This paper presents a comprehensive study and systematic survey of parallel meta-heuristics. © 2022, Journal of Network Intelligence. All rights reserved.

关键词： Motion planning

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Improving Employee Performance Through Digitalization: Designing a Web Based Human Resource Management 7

Improving Employee Performance Through Digitalization: Desig...

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7th International Conference on Information Management and technology, ICIMTech 2022

作者： Tommy, Ray Kurniawan, Christian Niccosan Makalew, Brilly Andro Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Mobile Application & Technology Program Jakarta11480 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

The purpose of this research is to develop a web based human resource management application to help in monitoring and improving overall employees' performance from small to medium sized companies with online and remote direct access. The research methods consist of data collecting using literature study and comparison of similar applications, and design using UML Diagrams, the application is expected to assist small to medium sized companies in monitoring and improving overall employee performance with the evaluation results that can be applied to improve application effectiveness. © 2022 IEEE.

关键词： Human resource management

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Composition of Musical Piece Suited to Natural Sound by Interactive GA using User's EEG as Fitness 12

Composition of Musical Piece Suited to Natural Sound by Inte...

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12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022

作者： Gan, Haroran Fukumoto, Makoto Fukuoka Institute of Technology Graduate School of Computer Science and Engineering Fukuoka Japan Fukuokainstitute of Technology Dept. of Computer Science and Engineering Fukuoka Japan

ISBN: (数字)9781665497558

ISBN: (纸本)9781665497558

Everyone needs to be relaxed in some way. Previous studies show that listening to the combination of musical pieces and natural sounds effectively provide listeners with a relaxed feeling. In this study, we advocate an Interactive Genetic Algorithm (IGA) that composes musical piece suited to natural sound. IGA is a method to create media content that fits users' feelings based on their evaluation. Considering the fatigue and burden brought by subjective evaluation of users, the ratio of alpha-wave power in Electroencephalography (EEG) is used as fitness in this study. Based on the proposed IGA, we built a system taking musical notes, entire volume, and tempo as variables to adjust. With the IGA system, a listening experiment was carried out to investigate the fundamental efficiency of the proposed IGA. As a result, a marginal increase in the mean fitness was observed between the initial and final generations. However, no significant difference was observed in the subjective evaluation step after the search step. © 2022 IEEE.

关键词： Electroencephalography

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A Stable Diffusion Based Image Generation Method for Few-Shot Emerging Pest Detection in the Wild 25

A Stable Diffusion Based Image Generation Method for Few-Sho...

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25th IEEE International Conference on Industrial technology, ICIT 2024

作者： Wang, Xiaodong Wang, Rujing Wu, Shilian Ma, Xiao Du, Jianming Gao, Junjie Institute Of Intelligent Machines Hefei Institutes Of Physical Science Chinese Academy Of Sciences Intelligent Agriculture Engineering Laboratory Of Anhui Province Hefei230031 China Graduate School Of University Of Science And Technology Of China Science Island Branch Hefei230026 China University Of Science And Technology Of China Hefei230026 China School Of Computer Science And Engineering Nanjing University Of Science And Technology Nanjing210094 China Shanghai Advanced Research Institute Chinese Academy Of Sciences Shanghai201210 China

ISBN: (纸本)9798350340266

Detecting few-shot emerging pests in the wild presents a significant challenge in the field of agricultural computer vision. However, there has been minimal research focused on generating emerging pest images with different natural scenarios that align with the authentic pest living environments. With the advent of the controllable generative stable diffusion model recent years, we proposed an innovative image generation method based on stable diffusion techniques as well as introducing text prompt and coordinate information into the vanilla model. Both qualitative and quantitative experimental results demonstrate that our proposed approach can generate anthropomorphic images which are both visually recognized by human experts and various image quality assessment performance metrics. © 2024 IEEE.

关键词： Diffusion

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Biotech-Enhanced Online Education: Reshaping Learning and Physical Health

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Journal of Commercial Biotechnology 2023年第5期28卷 108-121页

作者： Wang, Zhaojie Saluba, Dennis J. Shi, Hong College of Teacher Education Taiyuan Normal University Jinzhong030600 China Graduate School Adamson University Manila10004 Philippines College of Computer Science and Technology Taiyuan Normal University Jinzhong030600 China

The landscape of education is undergoing a transformative shift with the increasing adoption of online learning, further accelerated by the recent global pandemic. This study explores the intersection of biotechnology and online education, particularly focusing on its implications for student learning and physical health. Amidst the COVID-19 crisis, Romanian universities rapidly transitioned to online platforms, providing a unique opportunity to assess the effectiveness of such methods over an 8-year period. This research highlights the role of biotechnological tools in enhancing online learning experiences and addressing the physical health challenges posed by the new educational environment. We conducted a web-based survey using a semi-structured questionnaire to evaluate student engagement, information assimilation, and health outcomes in the context of online learning. The study included 548 students, comprising 401 from traditional classroom settings and 147 engaged in online learning. The research aimed to compare the effectiveness of biotech-enhanced online education against conventional methods, particularly in terms of student performance and health. Our analysis extended to variations in performance across genders and class rankings, assessing if biotechnological interventions in online education had differential impacts across these groups. The findings indicate that while there was no significant difference in academic performance between online and face-to-face learners, the integration of biotechnological tools in online education platforms showed promising results in managing and improving students' physical health. This study underscores the potential of biotech-enhanced online education in not only maintaining educational standards but also in fostering better health outcomes for students in a digital learning landscape. © 2023 ThinkBiotech LLC. All rights reserved.

关键词： Students

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EEG-Based Motion Intention Detection for Robotic Rehabilitation: Evaluating Classification and Regression Algorithms

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SN computer science 2024年第8期5卷 1057页

作者： Khan, Abdul Manan Khan, Fatima Dwivedi, Anshu Kumar Semwal, Vijay Bhaskar Martha, Sheshikala Bijalwan, Vishwanath Department of Aerospace Engineering University of Bristol Bristol United Kingdom Graduate School of Cancer Science and Policy National Cancer Center Ilsan South Korea School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of CSE Maulana Azad National Institute of Technology Bhopal India School of Computer Science & amp Artificial Intelligence SR University Warangal Warangal India Department of Electrical and Electronics Engineering University of West London London United Kingdom

Rehabilitation robots have shown significant efficacy in restoring lost motor functions in stroke survivors by providing compliant assistance during rehabilitation tasks. However, accurately understanding the desired motion intention (DMI) of the subjects is a significant challenge in providing effective robotic assistance. This work aims to determine the desired motion intention (DMI) for compliant robotic assistance through the analysis of upper limb motions using electroencephalography (EEG) signals. A group of fifteen individuals who were in good health carried out six distinct exercises. These movements were categorized using decision trees, random forest, and deep learning algorithms. Using deep learning, we achieved the highest accuracy rates, reaching 82% for movement-to-movement classification and 90% for movement-to-rest classification. The random forest method achieved superior performance compared to the other algorithms in regression tests, with an accuracy rate of 83%. The results demonstrate the effectiveness of our proposed EEG-based classification and regression techniques in detecting DMI for robotic help in *** study was conducted with a small sample size, exclusively involving healthy subjects, which is a critical factor to consider. This may limit the generalizability of the results, particularly when populations with motor impairments are taken into account. However, the study offers valuable insights into the potential of the EEG-based models for real-time rehabilitation applications despite this limitation. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： C-RNN Deep learning Desired motion intention EEG signals Machine learning Rehabilitation

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