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Investigation of VGG 16 Transfer Learning and CNN Integrated Brain Tumour Detection

Investigation of VGG 16 Transfer Learning and CNN Integrated...

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2024 International Conference on computer, electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Dept. of Electronics and Engineering Dehradun India

ISBN: (纸本)9798350388534

This study had conducted an intensive exploration of the possibilities of VGG-16 and VGG-19 Convolutional Neural Network models for picture categorisation. Using a thorough method comprising substantial training and severe testing, we may observe both steady loss decrease and significant model accuracy improvements. It is assumed that the models may appropriately keep track of subtle variations in data to which they are adversarial while identifying regular patterns they may generalize. The VGG models' architecture is quite sophisticated, but as seen above, they give strong classification accuracy, with performance in nearly ideal 99 percent in many trials. This integration proves that extremely advanced deep learning instruments are competent to handle and complete even very sophisticated picture recognition assignments. Additionally, our experiments identify a few essential aspects of model adjustment and tuning identifying important techniques considerably enhancing CNN outcomes in application scenarios. This study is pioneering for the automatic picture identification discipline since it provided a strong understanding of the generality and model quicker than many real-world models. Additionally, it exhibits that the CNN structures enhance artificial intelligence's power to recognize visual input and could improve many sectors like the surveillance and health systems. The findings defined here may now open the door to more progress in deep learning and picture process analyses while reiterated the importance of CNN in modern technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Waste Management Using Microservices and Container-based Virtualization 27

Smart Waste Management Using Microservices and Container-bas...

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27th Pan-Hellenic Conference on Progress in Computing and informatics with International Participation, PCI 2023

作者： Karagiannakis, Stilianos Theodoropoulos, Ioannis Anagnostopoulos, Apostolos Mamalis, Basilis University of West Attica Dept. of Informatics and Computer Engineering Greece

ISBN: (纸本)9798400716263

One of the most significant issues associated with smart city applications is solid waste management, which has a negative impact on our society's health and the environment. In this paper, a smart waste bin management approach with novel architectural design is proposed, based on modern container-based virtualization and microservices technologies. Its objective is twofold;first, efficient monitoring/tracking of waste bins, analyzing their data and taking further actions in case of out-of-limits values (i.e. with respect to fire danger, fill level etc.) for improving citizens health and daily life, and second, building a flexible application that can easily be deployed and managed in a distributed manner in any relevant environment, with use of modern orchestrators. Beyond the detailed architectural design, several implementation issues of our approach are thoroughly analyzed and discussed, as well as further extensions and initial performance evaluation. © 2023 Owner/Author.

关键词： Internet of things

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Exploring Taiwan Monkeypox Virus (MPOX/MPXV) Clade IIb by Establishing Dataset and Clustering by using Sequence and Image Similarity Comparisons 24

Exploring Taiwan Monkeypox Virus (MPOX/MPXV) Clade IIb by Es...

引用

7th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2024

作者： Wang, Tzung-Wei Li, Ming-Kuang Lou, Der-Chyuan Lin, Chun-Yuan Dept. of Electronics Engineering Chang Gung University Taoyuan Taiwan Dept. of Computer Science and Information Engineering Asia University Taiwan Taichung Taiwan Dept. of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan

ISBN: (纸本)9798400717437

Based on the MPXV Clade IIb cases in Taiwan, 175 segments were identified from OPG001 to OPG210. The first focus of this paper is on sequence analysis of the MPXV cases in Taiwan, detailing the procedures, including semi-automated segmentation and dataset creation from a large number of MPXV Clade IIb nucleotide sequences found in the NCBI and GISAID databases. The second feature highlights the pioneering establishment of an MPXV Clade IIb RNA 2nd structure image dataset in Taiwan, as such a dataset does not yet exist globally. The third characteristic involves the use of unsupervised learning methods for the rapid clustering of MPXV Clade IIb RNA 2nd structure images. This approach allows not only for the quick detection and clustering of unknown MPXV Clade IIb cases but also for the rapid identification of new MPXV mutations and structural changes. Compared to traditional sequence analysis, this new hybrid systematic method significantly enhances overall efficiency, improving processing speed by at least 24 hours when handling the alignment of 10 MPXV sequences from Taiwan. Future plans include using this prototype analytical model and method to accelerate the analysis of critical lethal factors in MPXV Clade IIb, such as D14L, ultimately helping biologists reduce the time required for analysis and antiviral drug development. © 2024 Copyright held by the owner/author(s).

关键词： Nucleotides

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Non-intrusive Emotional Development Monitoring of Pre-Primary Children Using Image Processing and Machine Learning Model

Non-intrusive Emotional Development Monitoring of Pre-Primar...

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2024 International Conference on Recent Innovation in Smart and Sustainable Technology, ICRISST 2024

作者： Frank, Anilloy Raj, Mrinal Presidency University Dept. of Electronics and Communication Engineering Bangalore India Presidency University Dept. of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350309140

The development of emotions begins in a child from infancy and continues into adulthood. The pre-primary experiences are unique to each child and the learning environments in early life often serve as the source for behavioral outcomes as they grow. The study is mainly based on detecting the emotions of children to have an insight of their behavior with respect to the external environment. The approach aligns with the Goal-3 Good health and well-being of the United Nations Sustainable Development Goals. It provides with the behavior pattern in better understanding of the child emotions both by parents and teachers of the student. Emotion recognition model using image processing enable understand how the emotions of children change and affects their lives with the human interaction. The model uses Transfer Learning model. Many students sit in front of their computers and can easily analyze their interaction with the system. The technology is evolving and using this model, can analyze human interaction and their behavior patterns in a given particular situation. © 2024 IEEE.

关键词： Child Emotional development face recognition Image processing Machine Learning in education

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Analysis of Historical Weather of Karnataka, India for Insights in Climate Trends

Analysis of Historical Weather of Karnataka, India for Insig...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Sanjana, B.K. Frank, Anilloy Presidency University Dept. of Computer Science Engineering Bangalore India Presidency University Dept. of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798331533311

Climate change in simpler words means the change in the long-term average weather parameters. Climate change is an important issue because its causing imbalance in the environment and affects the lives of all flora and fauna. The climate change is due to many factors, but the major factor is the human activities which are leading to global warming over the past decades. Numerous studies consider analysis of continental or global data, providing the overall insights in climatic changes continentally or globally respectively. The approach in this paper considers regional analysis to provide better insights to the local people needs so they get adapted to the changes that are taking place. This provides a warning to the people to take preventive and precautionary measures. The dataset of Karnataka region from 1951 to 2020 is used to study the regional climate change pattern. The main objective of the paper is to analyze the climate change and extending in the future. The results show that there is a gradual increase of average mean surface air temperature and gradual decrease of the precipitation over each decade between the years 1951 to 2020. The trend line extending to 2040 by analyzing the historical data gives insights about the climate change in the near future. Understanding the historical data and the future predictions are very important to the policy makers, people and researchers so that effective planning can be done for adapting and decreasing the climate change patterns. © 2024 IEEE.

关键词： Agriculture Environment impact Regional Climate trends

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Lidar-based Human Detection for a Mobile Robot in Social Environment using Deep Learning 25

Lidar-based Human Detection for a Mobile Robot in Social Env...

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25th International electronics Symposium, IES 2023

作者： Ramadhan, Alif Wicaksana Dewantara, Bima Sena Bayu Setiawardhana Akzada, Fursan Departmen of Electrical Engineering Surabaya Indonesia Dept. of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350314731

Nowadays, robotics implementation spans industries such as manufacturing, healthcare, and logistics, integrating AI-driven capabilities to enhance efficiency and collaboration in diverse operational environments. The ability to respond and interact while considering human presence is crucial for a mobile robot to operate effectively and be accepted in a human social environment. It is necessary to make a robot that can detect the presence of humans around it to make it capable of navigating while considering the social environment. Meanwhile, considering that the lidar sensor is almost always available in many robot navigation cases, it becomes an accessible tool for detecting the presence of humans in its surroundings. We brought the one-dimensional lidar data into two-dimensional image data, then involved some object selection based on weak characteristics, such as the acceptable range radius of the object and the acceptable distance between two legs. The result of the process is a group of human candidates. We use a deep learning model with a pretrained mobilenetv3 model as the feature extractor to ensure whether the candidates are humans and a fully connected layer of neural networks as the classifier. We defined several scenarios in an indoor environment to test the performance of the proposed algorithm. The experiment results show the system's performance with a precision score of 0.902, a recall score of 0.782, and an F1 score of 0.837. © 2023 IEEE.

关键词： Mobile robots

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Optimizing Customer Segmentation: A Comparative Analysis of Clustering Algorithms Using Evaluation Metrics 8

Optimizing Customer Segmentation: A Comparative Analysis of ...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Sakina, Noor Arun, Ajith P. Pavithra, R. Vishalakshi Prabhu, H. Gupta, Praveen Kumar Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India Rv College of Engineering Dept. of Chemical Engineering Bengaluru India Rv College of Engineering Dept. of Electronics and Communication Engineering Bengaluru India Rv College of Engineering Dept. of Biotechnology Bengaluru India

ISBN: (纸本)9798331505462

Customer segmentation is the route to data-driven decisions within organizations serving a wide variety of customer needs. This research explores customer segmentation with the utilization of K-means, hierarchical clustering, fuzzy C-means, along with DBSCAN to behavioural and demographic data. These algorithms are measured against each other using their silhouette score, Davies-Bouldin index, and cluster cohesion value. These results highlight that clustering can reveal meaningful patterns in customer data for improving personalized marketing strategies, customer retention, and profitability. © 2024 IEEE.

关键词： Sales

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Multifaceted Discrete Emotion Recognition from EEG Physiological Signals via Machine Learning Techniques 5

Multifaceted Discrete Emotion Recognition from EEG Physiolog...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Gupta, Amriti Nayyar, Kunal Chopra, Harshit Gahlan, Neha Sethia, Divyashikha Dept. of Software Engineering Delhi Technological University India Dept. of Electronics & Communication Engineering Delhi Technological University India Dept. of Computer Engineering Delhi Technological University India

ISBN: (纸本)9798350361155

Emotions, intricate mental states shaped by neurophysiological alterations influenced by cognitive processes, sensory inputs, behaviours, and diverse experiences, remain a compelling domain for exploration. The Electroencephalogram (EEG) is a potent tool that directly quantifies these emotional variations by capturing brain signals. Recent strides in emotion recognition have harnessed traditional machine learning classifiers to automate the identification of human emotions with remarkable success. This paper delves into the relatively underexplored Discrete Emotion Model, unveiling its capacity to achieve outstanding accuracy despite historical reservations regarding its effectiveness, using the ECSMP (Emotion, Cognition, Sleep, and Multi-model Physiological signals) dataset. It has two distinct environments, Video watching, and CANTAB-based cognitive assessment phases, enabling seamless data collection and analysis. This research effectively quantifies emotion for binary classification (classifies the type of emotion felt) and multiclass classification (classifies the intensity of emotion felt), elevating emotion recognition capabilities through the synergy of EEG technology. The exceptional performance of XGBoost, with a 96.5% accuracy rate in binary classification and 95% in multiclass emotion recognition, highlights its prowess compared to the other models tested. © 2024 IEEE.

关键词： Emotion Recognition

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Indonesian Hate Speech and Abusive Tweets Classification with Deep Learning Pre-trained Language Models 6

Indonesian Hate Speech and Abusive Tweets Classification wit...

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6th International Conference on computer and informatics engineering, IC2IE 2023

作者： Yulianto Darmawan, Bagus Tri Irnawan, Bassamtiano Renaufalgi Suzuki, Yoshimi Politeknik Negeri Jakarta Dept. of Computer and Informatics Engineering Depok Indonesia University of Yamanashi Dept. of Computer Science and Engineering Kofu Japan

ISBN: (纸本)9798350345162

Hate speech and abusive language on social media can spread massively and escalate into conflict between two or more individuals or parties. Previous studies about multi-label classification to identify whether the tweet contains hate speech, abusive language, or not have been conducted with machine learning techniques. They applied machine learning with a combination of feature extraction and achieved a good result in recognizing the tweet with hate speech content. In this research, we apply two approaches: first, we use vanilla pre-trained models of IndoBERT, IndoBERTweet, and Indonesian RoBERTa;second, we combine the three previous models with CNN. Our experiment shows that our proposed method has better results compared to previous research with the best performance achieved by IndoBERTweet+CNN with 93.9% Accuracy. © 2023 IEEE.

关键词： Abusive CNN Deep Learning Hate Speech IndoBERT IndoBERTweet Indonesian RoBERTa Indonesian Twitter

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Support Vector Regression Based Traffic Prediction Machine Learning Model∗ 8

Support Vector Regression Based Traffic Prediction Machine L...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Srivastava, Ansh Singh, Mrigaannkaa Nandi, Somesh Rv College of Engineering Dept. of Computer Science and Engineering Bengaluru India Rv College of Engineering Dept. of Electronics and Communication Bengaluru India Rv College of Engineering Dept. of AI/ML Bengaluru India

ISBN: (纸本)9798331505462

To combat the rising issue of urban traffic congestion, optimization of traffic light timings at intersections is crucial. Traditional methods show stagnancy in managing traffic flow dynamically. This study introduces Support Vector Regression (SVR) as an unconventional solution for traffic signal optimization. The model predicts the traffic conditions and alters the green light to turn on and off accordingly. Utilization of a comprehensive dataset consisting of vehicle distributions and critical flow ratios have been implemented to training a machine learning model. The statistics and performance is checked by usage of Mean Squared Error, R Squared, Mean Absolute Error demonstrating that SVR can effectively enhance traffic signal control. This approach pledges significant improvements in reduction in congestion and streamlined traffic flow. Our findings majorly highlight the capability of the SVR Machine Learning model in improving urban traffic management systems and call for further enhancement and collaboration with experts to address dynamic traffic issues. © 2024 IEEE.

关键词： Mean square error

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