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A Comparative Study of DenseNet121, VGG16, and Custom CNNs for Brain Tumor Classification using MRI Images

A Comparative Study of DenseNet121, VGG16, and Custom CNNs f...

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Emerging Trends in Electrical, Control, and Telecommunication engineering (ETECTE), international conference on

作者： Muhammad Masab Mujeeb Ur Rehman Zohaib Rafi Waqas Tariq Toor Dept. of Electrical Engineering UET Lahore Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

The categorization of brain tumors from magnetic resonance imaging (MRI) scans is a condemnatory step in the identification and management of brain diseases. With develpment in machine learning and specially deep learning, convolutional neural networks (CNNs) have shown impactful execution in the automatic identification of medical images. This paper compares three CNN-based models for classifying brain tumors: DenseNet121, VGG16, and a custom designed CNN. Both DenseNet121 and VGG16 utilize the power of transfer learning, while custom CNN is built from scratch for this specific task. The dataset used comprises MRI images categorized into glioma, meningioma, pituitary tumors, and no tumor. The models are evaluated on the basis of accuracy, loss, and generalization. The results reveal that DenseNet121 achieved an outstanding accuracy of 99%, VGG16 reached 98%, while the custom CNN yielded 96%. However, the custom CNN showed significant signs of overfitting during training, which will be thoroughly examined in this study. The paper further explores the benefits of transfer learning and discusses the tradeoffs in using pretrained models versus custom-built networks. This comparative analysis provides insights to the best practices to utilize deep learning models in medical image classification.

关键词： Deep learning Training Accuracy Magnetic resonance imaging Transfer learning Brain tumors Brain modeling Convolutional neural networks Medical diagnostic imaging Overfitting

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A Comparative Analysis of Time Series and machine learning Models for Wind Speed Prediction

A Comparative Analysis of Time Series and Machine Learning M...

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Mysore Sub Section international conference (MysuruCon), IEEE

作者： Rakesh Kumar M. Prakash B. Shakila Department of Electrical and Electronics Engineering NIT Nagaland Chumukedima India

The effective incorporation of wind power into the electrical system depends on accurate wind power forecast, which has become a key source of renewable energy. The forecasting of wind power is a challenging task due to the variability of wind speed, wind direction, and other meteorological variables that affect wind power generation. The temporal relationships and trends contained in the data are implemented using time series models, such as autoregressive integrated moving average (ARIMA)and to identify the seasonal component, a seasonal autoregressive integrated moving average with X lag (SARIMAX) model has been implemented. Additionally, in order to take advantage of the complex linkages and non-linear patterns in the dataset, we use machine learning models like Multilinear regression, Decision tree regression and ensemble learning models such as Random Forest regression, and xGBoost. However, conventional time-series methods may have limitations in accurately predicting the complex and nonlinear behavior of wind power generation. These methods assume that the relationship between wind power and meteorological variables is linear and stationary, which may not always hold in practice. To examine the performance of the models, we employ a variety of assessment metrics, such as Mean absolute error (MAE), Mean square error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE).The performance of each algorithm has calculated by tacking average of the predicted wind speed data. The machine learning models average error (MSE) 1.457 to 1.868.

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data-Driven House Price Prediction

Data-Driven House Price Prediction

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Applied Artificial Intelligence and Computing (ICAAIC), international conference on

作者： N. Shalini A Sai Kiran B Sathwik G Trishul Reddy Kilaru Aswini M Geetha Yadav Computer science and engineering Institute Of Aeronautical Engineering Computer science and engineering Institute Of Aeronautical Engineering Hyderabad India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

This research study analyzes the complex and dynamic field of real estate, aiming to reveal the numerous factors influencing home values. The primary objective of this research study is to develop a prediction system using Random Forest (RF). Leveraging a comprehensive dataset encompassing historical real estate data, this study intends to forecast house values by considering a wide array of variables. Key attributes such as the number of bedrooms and bathrooms, property size, location, and proximity to amenities like transportation, hospitals, and schools form the cornerstone of the proposed model. Drawing insights from past price fluctuations, the proposed algorithm enhances forecast accuracy by analyzing previous value trends. Additionally, for comparative analysis, the proposed approach incorporates multiple regression and linear regression models alongside the Random Forests approach, enabling to assess the performance of our primary model against industry-standard techniques. The resultant system offers users detailed insights into the local real estate landscape alongside precise property price projections. Users can compare specific properties with nearby alternatives, empowering them to make informed decisions. Tailored for real estate agents, investors, and homeowners, our system serves as an invaluable resource for navigating the complexities of real estate transactions. The proposed solution aims to facilitate wise and successful real estate decisions by furnishing precise forecasts and revealing market intricacies.

关键词： Analytical models Accuracy machine learning algorithms Linear regression Predictive models Prediction algorithms Boosting

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A Deep learning Framework for Anaphora Resolution from Social Media Text 3rd

A Deep Learning Framework for Anaphora Resolution from Socia...

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3rd international conference on machine Intelligence and Signal Processing, MISP 2021

作者： Saha, Baidya Nath Senapati, Apurbalal Garain, Utpal Concordia University of Edmonton Alberta Canada Central Institute of Technology Assam Kokrajhar India Indian Statistical Institute Kolkata India

ISBN: (纸本)9789811908392

Anaphora resolution is a classical problem in natural language processing. For more than forty years’ people are an attempt to resolve the issue with different approaches. Because of the linguistic complexity and resource constraints, it is still an active research problem, especially for the resource-scarce language like Indic languages. This paper attempted to resolve anaphora using the LSTM-based deep learning method. The primary focus of this approach is to do in an unannotated text. The system is evaluated by the matrices like mentions, MUC, BCUB, CEAFE, CEAFM and LEA. This research makes an effort to conduct anaphora resolution from twitter data which is a challenging task due to its inherent unstructured nature. Experimental results demonstrate high degree of agreement with human annotations. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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IoT-Enabled machine learning Framework for Ground Water Level Prediction in Peatland Ecosystems 3

IoT-Enabled Machine Learning Framework for Ground Water Leve...

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Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Arunkumar K Gokul S Jagadeeshwar J Manoj Kumar M Department of ECE Saveetha Engineering College Chennai Tamilnadu

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Peatland ecosystems are important for biodiversity and carbon storage, but they are vulnerable to fluctuations in the water table leading to improper environmental health and agricultural productivity. Accurate and timely Ground Water Level (GWL) predictions are necessary to overcome these challenges. The research presents an IoT-based machine learning-enabled framework for predicting the GWL, using real-time IoT sensor data on soil moisture, temperature, and atmospheric pressure. The system uses sophisticated algorithms in its analysis of data to predict GWL with great accuracy using Random Forest and LSTM. Thus, the system ensures timely alerts for stakeholders to manage peatland water resources effectively by being cost-effective and scalable. The real-time monitoring and predictive insights offered may also allow it to eventually integrate with the irrigation system for proactive intervention. All these approaches aim to enhance peatland management, reduce environmental degradation, and support sustainable agricultural practices with incentives for preserving ecological stability and economic resilience.

关键词： Temperature sensors Temperature distribution Accuracy Ecosystems Soil moisture Real-time systems Environmental monitoring Stakeholders Water resources Long short term memory

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Application research of full chain topology inference technology in the field of Nuclear Power Drawings

Application research of full chain topology inference techno...

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Energy, Power and Electrical Technology (ICEPET), international conference on

作者： Lihong Tang Yahui Yang Shanhong He Chenhui Dong Suzhou Nuclear Power Research Institute Co Ltd Suzhou China National Engineering Research Center for Nuclear Power Plant Safety & Reliability Suzhou China

ISBN: (数字)9798350352658

ISBN: (纸本)9798350352665

Under the double carbon target, with the gradual increase of China's nuclear power installed capacity and the gradual improvement of the degree of intelligence and digitalization in the industrial field, it is urgent to improve the operation efficiency of nuclear power. At present, the drawing application in the nuclear power field completely relies on manual reading of paper drawings to identify the content in the drawing, and lacks the means of intelligent application. In view of this situation, this paper proposes a set of drawing application solution based on graph theory and sorting algorithm. Based on graph theory, the topological connection relationship established by each node in the drawing is generated into a directed graph, and the generated directed graph is mapped to the external data. While the mapping relationship from the equipment entity to the directed graph is established, the connection between the equipment entity and the data is established. The calculation of the directed graph is performed using core algorithms such as depth first search, and the full link signal graph based on signal flow is derived. For the first time at home and abroad, the automatic generation of the hierarchical structure of the full link asset within or across the drawing is realized, and the digital twin of the drawing asset is constructed. At the same time, the method has strong generalization ability, which provides new ideas for the development and application of drawings in all walks of life. It creates the first intelligent application of drawings in the industrial industry, especially the nuclear power industry, and provides a foundation for the maintenance work based on machine learning, making the intelligent operation and maintenance based on drawings possible.

关键词： Error analysis Directed graphs machine learning Manuals Power industry Maintenance Digital twins

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machine learning Insights into Mobile Phone Usage and Its Effects on Student Health and Academic Achievement

Machine Learning Insights into Mobile Phone Usage and Its Ef...

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Advancement in Technology (ICONAT), international conference for

作者： Pritha Biswas Divya R Krishnan Md Shaik Amzad Basha M Martha Sucharitha Department of Professional Studies Christ (Deemed to be University) Bengaluru India GITAM School of Business (Deemed to be University) Bengaluru India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

The research intends to find how students’ health and academic performance are affected by their smartphone use. Considering how widely smartphones are used among students, it is important to know how they could affect health and learning results. This study aims to create prediction models that can spot trends and links between smartphone usage, health ratings, and academic achievement, thereby offering insightful information for teachers and legislators to encourage better and more efficient use among their charges. data on students’ mobile phone use, health evaluations, and academic achievement were gathered for the study. Preprocessing of the dataset helped to translate categorical variables into numerical forms and manage missing values. Trained and assessed were many machine learning models: Random Forest, SVM, Decision Tree, Gradient Boosting, Logistic Regression, AdaBoost, and K-Nearest Neighbors (KNN). The models’ performance was evaluated in line with their accuracy in influencing performance effects and health ratings. Predictive accuracy was improved by use of feature engineering and model optimization methods. With $63.33 \%$ of accuracy for estimating health ratings, the SVM model was most successful in capturing the link between smartphone usage and health results. With an accuracy of $50 \%$, logistic regression performed very well in forecasting performance effect, therefore stressing important linear connections between consumption habits and academic success. Random Forest and Decision Tree models were less successful for performance impact even if they showed strong performance in health forecasts. These results highlight the need of customized treatments to reduce the detrimental consequences of too high mobile phone use on students’ academic performance and health.

关键词： Support vector machines Logistic regression Adaptation models Accuracy Predictive models Nearest neighbor methods Mobile handsets data models Decision trees Random forests

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Comparison between 2D Classification and 3D Classification based on Deep learning

Comparison between 2D Classification and 3D Classification b...

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Embedded Systems and Artificial Intelligence (ESAI), international conference on

作者： Zouhir El Khalfi Soulaiman El Hazzat Majid Ben Yakhlef LSI Department of Computer Science Polydisciplinary Faculty of Taza Sidi Mohamed Ben Abdellah University Fez Morocco

ISBN: (数字)9798331522056

ISBN: (纸本)9798331522063

The rise in processing power of computers and the availability of a considerable amount of data have allowed the overlap of several machine learning approaches. This has led to breakthroughs on many deep learning tasks as speech recognition, natural language processing (NLP), object detection and image analysis. Thus, many fields as autonomous driving, industrial quality processes and mobile robotic have released multiples researches in deep learning algorithmic topics. In this paper, we have made a comparison, in terms of accuracy and process timing, between classifications based on 3D points cloud and 2D images for the same input dataset in raw format, and generated basic outlines of both deep learning methods using algorithms based on convolution networks. We discovered that 3D points cloud based classification model was slow on training stage than 2D images model timing due to the 3D complexity data representation. Besides that, classification neural networks based on 3D points cloud data performed better on accuracy than the networks based on 2D images datasets. It is to highlight that 3D based systems are so critical in terms of data processing compared to 2D images due to: data format as points cloud, meshes and volumes, the hardware resources requirement which is higher due to the third added dimension. Finally, we hypothesize that systems which would combine both of these data geometry and networks architectures would return accuracy better and low processing time wise.

关键词： Point cloud compression Deep learning Solid modeling Three-dimensional displays Accuracy Convolution Natural language processing data models Timing Classification algorithms

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On Securing Communications Between Connected Objects Using a data-Centric Security Approach

On Securing Communications Between Connected Objects Using a...

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Electrical, Computer, Communications and Mechatronics engineering (ICECCME), international conference on

作者： Fehmi Jaafar Darine Ameyed Yasmine Bouzid Abib Sy Department of Mathematics and Computer Science University of Quebec at Chicoutimi Quebec Canada École de Technologie supérieure de Montreal Quebec Canada École Polytechnique University of Carthage Tunis Tunisie Department of Mathematics and Computer Science University of Quebec at Chicoutimi Québec Canada

Connected objects are one of the most important vectors for the collection of personal data. With the increase in data volumes, we are observing an increase in network vulnerabilities and data ***-centric security (DCS) and its related protocols such as the NATO STANAG 4774 have become a suited approach to address diverse data protection and secure information exchange. Despite the novelty of the approach, it comes with a challenge regarding its implementation to ensure the integrity of data in real scenario. In this paper, we are evaluating the NATO STANAG 4774 protocol when securing smart home data. Then, we use Random Forest to detect cyber attacks based on malware injection. We conduct an empirical study to evaluate the performance of our approach and we show how a machine learning technique can be used to ensure the integrity of data when using a data-centric security protocol. In fact, our proposed approach has a recall of 0.781 —in other words, it correctly identifies more than 78% of all malicious data injection.

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COVID-19 (COV-19) Spreading Diagnoses by Feature Representation Method Through Visual learning (FVisL-CoV19) 3rd

COVID-19 (COV-19) Spreading Diagnoses by Feature Representat...

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3rd international conference on Information Technology, InCITe 2023

作者： Kakulapati, V. Jayanthiladevi, A. Srinivas University Karnataka Mangaluru India Institute of Computer Science and Information Science Srinivas University Karnataka Mangaluru India

ISBN: (纸本)9789819959969

The use of computer vision and AI methods in medical image assessment has shown impressive success in the analysis and monitoring of respiratory ailment deterioration. To decrease the impact on people and the global economy, the COV-19 epidemic basis on the coronavirus has highlighted the need for artificial intelligence [AI] and disease management. The COV-19 identification approach proposed in this study is completely focused on the depiction of modal functions in pictures. Using standard pre-training features and tiny pattern development during chest CT processing, this approach first extracts modal function characteristics. A regular pooling layer prevents overfitting and reduces training parameters. The representation is trained and fine-tuned;to get the trained model, a machine that learns the scoring standard is utilized. The visible learning modeling approach of Coronavirus 19 (FVisL-CoV19) is used in this study to give a novel multi-layered function feature illustrated representation that is used to examine visible demands, including feature optimum function identification. However, using visual learning of structures improves FVisL accuracy. By employing CoV19’s Chest X-ray images and scans from the reference coranavirus record, the recommended function-based total representations are assessed. For training and testing, we employed 265 (with data-fact augmentation) and 795 COV-19 images, respectively. The FVisL-CoV19 approach demonstrated improved performance overall, using all feature functions in the dataset with an accuracy of 95.22%. The studies show that the kind of technology in this suggested work can analyze the modal statistics of the COV-19 picture more effectively than the standard techniques, and it increases the anticipated impact of appropriately detecting incidents. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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