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A Robust EfficientNet Architecture for Brain Tumor Classification and Identification Using MRI Image 11

A Robust EfficientNet Architecture for Brain Tumor Classific...

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11th International Conference on Intelligent Systems and Embedded Design, ISED 2023

作者： Pathak, Manisha Chaudhary, Deevesh Sharma, Kratika Sharma, Akhilesh K Gupta, Ashish Sharma, Brij Kishore Mits Dept. of Cse Gwalior India Manipal University Jaipur Dept. of Data Science & Engineering Jaipur India

ISBN: (纸本)9798350318159

The most common method for classifying brain tumors is by using biopsy, but this procedure is only carried out after surgery in which a tiny piece of brain tissue is takeout as well as analyzed under an electronic microscope to see a tumor. Since technology has advanced, particularly in ML and AI, we are now able to identify and categorize tumors without undergoing surgery by feeding the machine's classification engine the current data from various MRI images. By doing our study, we present a approach for the identification of tumors utilizing EfficientNet, an architecture that has already been trained utilizing the transfer learning methodology. This study emphasizes on the EfficientNet-B2, EfficientNet-B3, as well as EfficientNet-B4 algorithms from the group of EfficientNet approach. The suggested approach makes use of thresholds to enhance the dataset for more effective accuracy as well as data augmentation to improve the variety of brain images in the data in addition to the previously-trained approach to enhance the effectiveness of training an improved the model. Because it utilizes the compound coefficient to scale all three dimensions of an image with a fixed value of ratio, dept., width, and resolution - EfficientNet relatives of models outperform earlier CNN architecture, according to preliminary results. The outcomes also showed that scaling the previous approaches, enabled the model to capture complex features, improving the accuracy of the model. © 2023 IEEE.

关键词： Tumors

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DeepCensored: Deep-Learning Based Probabilistic Forecasting Framework for Censored data 23

DeepCensored: Deep-Learning Based Probabilistic Forecasting ...

引用

23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Tian, Jiahao Porter, Michael D. School of Data Science University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia Dept. of Systems and Information Engineering CharlottesvilleVA United States

ISBN: (纸本)9798350374889

Accurate time series forecasting is critical in various fields, including resource allocation and crime prevention. While traditional approaches often focus on continuous data, count data forecasting, especially with partially observed (censored) data, remains challenging. This paper introduces DeepCensored, a novel deep learning-based framework that combines the Expectation-Maximization (EM) algorithm with deep neural networks to deliver robust probabilistic forecasts. DeepCensored naturally handles interval-censored event data, where exact event times are unknown but fall within a specific interval. Through extensive simulations and real-world crime data analysis, our method significantly outperforms traditional forecasting approaches, reducing the Mean Absolute Error (MAE) by 50% and better detection of emerging crime trends. These results highlight DeepCensored's potential to provide actionable insights for law enforcement and public safety by predicting crime intensity and enabling resource-efficient policing strategies. © 2024 IEEE.

关键词： Expectation maximization algorithm

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Enhancing Dental Diagnosis through Automated Detection of Teeth Diseases in Panoramic Radiographs

Enhancing Dental Diagnosis through Automated Detection of Te...

引用

2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Rawashdeh, Majdi Bdair, Tariq Salhi, DhaiEddine Al-Yamamah University Software Engineering Dept. Riyadh Saudi Arabia Psut Business Information Technology Dept. Amman Jordan Psut Data Science Dept. Amman Jordan

ISBN: (纸本)9798350351255

Edge learning has shown promising solutions to address diseases by exploiting edge computing capabilities. On the other hand, the analysis of dental radiographs is a crucial step in clinical routine practice since the dentist must evaluate tooth-related issues throughout the clinical diagnosis. Further, errors in manual human analysis may lead to false predictions. Fortunately, the automated approach for identifying and categorizing dental issues can help in the early diagnosis of diseases and prevent tooth loss. This paper aims to create a model that may offer a second viewpoint and simplify dental diagnosis for dentists. The proposed method utilizes state-of-the-art deep learning architecture for object detection. It provides panoramic teeth radiograph images to the proposed network to detect four teeth diseases: conservative (fillings and crowns), implant, RCT (root canal treatment), and bridges. After data preprocessing and augmentation, our framework is used to detect the above teeth diseases with high detection quality and accuracy with mAP and F1-score at 0.902 and 0.86, respectively. © 2024 IEEE.

关键词： Diseases

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Phishing URLs Using Machine Learning Hybrid Stacking Classifier Approach with XGBoost, Random Forest and Extra Trees

Phishing URLs Using Machine Learning Hybrid Stacking Classif...

引用

2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Chitteti, Chengamma Divya Sree, Tanguturi Sai Madhavi, K. Reddy Pooja, Pikkili Jayanth, Sannapaneni Reddy, Middi Harshavardhan Dept. of Data Science Tirupathi India Sree Vidyanikethan Engineering College Dept. of Information Technology Tirupathi India Mohan Babu University Dept. of Ai & Ml Tirupati India

ISBN: (纸本)9798350382693

Within the dynamic field of cybersecurity threats, phishing remains a prevalent and sophisticated tactic employed by malicious actors to deceive consumers. This paper presents a novel approach that leverages the strengths of Random Forest, ExtraTrees, and XGBoost in a machine learning framework to enhance the detection of phishing URLs through a hybrid stacking classifier. The suggested method combines the best features of all three ensemble learning techniques to provide a phishing detection model that is reliable and precise. An ensemble of classifiers is formed by combining XGBoost, which is well-known for its boosting abilities, with the highly randomized ExtraTrees method and the adaptable Random Forest algorithm. By using the various decision limits of each base learner, the hybrid stacking model combines their particular capabilities in the best possible way to provide higher prediction performance. Preprocessing is done on the training dataset to extract pertinent characteristics such as content, URL structure, and contextual data. The efficacy of the suggested method in detecting phishing URLs is assessed by extensive testing on an extensive dataset. The results indicate how well the hybrid stacking classifier performs in comparison to separate classifiers and highlight the possibility for improved phishing detection in practical situations. The study has importance as it presents a novel and efficient machine learning-based approach to counteract the rising danger of phishing assaults, hence propelling the area of cybersecurity forward. © 2024 IEEE.

关键词： Phishing

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Forecasting Air Pollution of Urban Chennai Localities using Artificial Intelligence 3

Forecasting Air Pollution of Urban Chennai Localities using ...

引用

3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Faiz, Rayaan Agarwal, Tisha Rajeswari, D. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Dept. of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350375190

India's air quality deteriorated significantly in 2023, ranking third worst globally, highlighting the urgency for effective monitoring and mitigation measures. To comprehend past trends and predict future patterns, historical air pollution data from seven urban areas in Chennai was analyzed using the XGBoost Algorithm. This study aims to gain insights into Chennai's air quality, identify abnormalities, and issue timely warnings to the public and governing authorities. XGBoost was employed to forecast AQI, NO2, PM2.5, NOx, PM10, CO, SO2, NH3, and Ozone levels, outperforming traditional LSTM, Prophet, and ARIMA models. Evaluation using RMSE and MAE scores revealed XGBoost's efficacy, with scores of 20.65 and 14.35, respectively. The research highlights regions like Alandur, Arumbakkam, and Manali with notable concentrations of contaminants, emphasizing the need for immediate action. These findings highlight the significance of utilizing advanced algorithms for accurate air quality forecasting and proactive pollution management. © 2024 IEEE.

关键词： Air quality

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Temporal Fusion Transformer -LSTM-based Improved State of Charge Estimation of Li-ion battery for Real Drive Cycle 4

Temporal Fusion Transformer -LSTM-based Improved State of Ch...

引用

4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Pandian, Anbumalar Shruthi, S. Dhanush, V. Sundarajan, Sriranganayaki Abishek, P. Subbian, Sutha Natarajan, Pappa Dept. of Instrumentation Engineering Anna University MIT Campus Chennai India Dept. of Artificial Intelligence and Data Science Meenakshi Sundararajan Engineering College Chennai India

ISBN: (纸本)9798331530402

Precise measurement of State of Charge (SoC) in Li-ion battery is crucial for effective utilization of energy storage system. However, it is challenging with physics-based model due to computation complexity while data-driven model often lacks generalization for unseen data. To improve the robustness of data-driven model, hybrid algorithms are employed. This paper focuses on combining Temporal Fusion Transformer (TFT)Long Short-Term Memory (LSTM) to improve the generalization of state-of charge estimation of unseen drive cycles. By leveraging the attention mechanism feature, temporal feature extraction of TFT along with memory capability of LSTM TFT-LSTM model is developed with optimal hyperparameters using Optuna framework. This model is used to estimate the SoC during the discharging cycle of a Turnigy Graphene Li-Ion Battery (5000mAh, 65C) for the LA92 drive cycle at an ambient temperature of 40°***, uncertainty analysis is performed against US06 drive cycle. Finally, the performance of TFT-LSTM is compared with LSTM model. The results demonstrate that TFT-LSTM achieves better performance compared to LSTM with Root Mean Square Error (RMSE) of 1.1962 for unseen US06 drive cycle. These findings demonstrate the robustness of hybrid TFT-LSTM against unseen drive cycle and highlight its potential under realistic conditions with variability. © 2025 IEEE.

关键词： Mean square error

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Earthquake Prediction Through Machine Learning Approach 4

Earthquake Prediction Through Machine Learning Approach

引用

4th International Conference on Technological Advancements in computational sciences, ICTACS 2024

作者： Shrote, Pratik Dasarwar, Priya Dongre, Snehlata Khobragade, Prashant Intern Center of Excellence Data Science Gh Raisoni College of Engineering Nagpur India Nagpur Campus Pune India Gh Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India H Raisoni College of Engineering Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350387490

The population of earthquake-prone areas is growing, as is the number of people killed and injured by earthquakes. The problem of the increasing number of populated regions that are vulnerable to earthquakes is being addressed by a shortage of experts in earthquake engineering. This implies the requirement for automated intelligence-monitored mitigation and warning systems. Predicting earthquakes is still a major problem for disaster management around the globe. The objective of this work is to build a machine learning (ML) approach that uses geographic coordinates (latitude, longitude) and elevation (height) as inputs to forecast earthquake magnitude. The estimated magnitude is used to classify the seismic risk level for a given site into high, low, or no risk categories using Richter's scale. The study offers a thorough analysis of the methodology, covering feature engineering, data collecting, preprocessing, and model construction. A variety of machine learning methods are investigated, and relevant measures are used to assess their effectiveness. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep Learning based Chatbot Architecture for Medical Diagnosis and Treatment Recommendation 3

Deep Learning based Chatbot Architecture for Medical Diagnos...

引用

3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Rajani, Girish Ruparel, Khushi Thadomal Shahani Engineering College Dept. of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9798350348347

With the exponential growth and rapid development in the fields of deep learning and neural networks, chatbots have gained a lot of popularity and have become a proven, and efficient tool to interact and provide service to users. Healthcare is one of the most promising fields where chatbots can be used more efficiently. This has become important, especially in the current medical landscape, where there is a shortage of doctors, and patients often have to wait long periods before getting any medical guidance. By using the power of transformer models and machine learning algorithms chatbots can help patients with personalized diagnoses and treatment recommendations, efficiently at ease and convenience. This helps the patients to access medical services anywhere, at any point in time. This paper proposes a well-planned systematic architecture for a medical chatbot that utilizes the potential of transformers, classification algorithms and machine-learning models. The architecture includes three main components: a Naïve Bayes Classifier, a Binary Tree classifier along with a Support Vector Classifier, and a sequence-to-sequence model. These algorithms are used to classify symptoms and determine the severity of a medical condition to provide patients with accurate medical diagnoses and treatment recommendations. Overall, the proposed architecture is built and designed to bridge the gap between doctors and patients by providing immediate access to medical advice, making it a promising tool for improving the quality and accessibility of healthcare services. © 2023 IEEE.

关键词： Diagnosis

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Youth Unemployment Mitigation: Leveraging Machine Learning and Exploratory data Analysis for Evidence-Driven Strategies

Youth Unemployment Mitigation: Leveraging Machine Learning a...

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2023 IEEE Conference on engineering Informatics, EI 2023

作者： Aarthi, S. Dharmita, A. Pragathi, G. Elakkiya, S. Srm Institute of Science and Technology Dept. of Computer Science Engineering Chennai India Srm Institute of Science and Technology Dept. Computer Science Engineering Specialization with Big Data Analytics Chennai India

ISBN: (纸本)9798350338522

Youth unemployment represents an urgent global socioeconomic challenge. This study combines machine learning and exploratory data analysis to understand and solve this problem comprehensively. Traditional approaches often rely on qualitative assessments and historical data requiring a data-driven transition to more effective solutions. Our approach integrates machine learning algorithms and Exploratory data Analysis techniques to analyze diverse data sets, identify influencing factors, and recommend targeted interventions. The analysis process includes data collection, preprocessing, feature selection, machine learning modeling, and Exploratory data Analysis techniques to understand the dynamics of the Better understanding unemployment among young people. Preliminary results reveal precise predictive patterns and previously unrecognized relationships, providing evidence-based solutions to inform policy and support the economic well-being of younger generations. Further research and validation are needed to increase applicability. © 2023 IEEE.

关键词： data visualization

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Predicting Customer Purchase Intent Using Logistic Regression: A data-Driven Approach 2

Predicting Customer Purchase Intent Using Logistic Regressio...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Rajarajachozhan, C. Karthika, G. Rajakrishnammal, A. Mano, M. Kumar, P. Nithish Jayahari, E. Saveetha Engineering College Dept. of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Engineering College Dept. of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha Engineering College Dept. of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

Predicting customer purchase intent is crucial for modern businesses in order to improve engagement, refine marketing strategies, and increase revenue. Conventional rule-based systems encounter difficulties with dynamic and extensive customer data, limiting their flexibility and precision. This project employs a machine learning methodology utilizing logistic regression to forecast customer intent based on demographic, transactional, and behavioral data. The proposed model analyses input features including age, browsing history, and previous purchases, employing sophisticated data preprocessing methods such as One-Hot Encoding and feature scaling to guarantee data integrity. Through hyperparameter tuning and the assessment of critical metrics including accuracy, precision, recall, and Fl-score, the system provides dependable and actionable insights. Real-time predictive capabilities enable businesses to customize customer interactions, facilitating targeted marketing and enhancing customer experience. This project underscores the capacity of predictive analytics to tackle real-world issues, facilitating data-driven decision-making and sustainable business development. © 2025 IEEE.

关键词： Logistic regression

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