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Flood Location, Management and Solution(FLMS): A Flood Prediction and Management System for Kurla 3

Flood Location, Management and Solution(FLMS): A Flood Predi...

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

作者： Naidu, Siddhanth Pandey, Ambuj Patel, Shiab Wagaskar, Kalpita Tripathy, Amiya Kumar Sen, Sumit Don Bosco Institute of Technology Dept. of Computer Engineering Mumbai India Indian Institute of Technology Bombay Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

Flooding is a common problem in Kurla, a suburban area of Mumbai, India, with significant impacts on infrastructure, public health, and their livelihoods. This paper proposes FLMS, a Flood Location, Management, and Solution system designed to predict flood events in Kurla to assist in managing them. The FLMS system uses historical data on rainfall gathered from different sources, water levels, and other factors to develop a prediction model based on machine learning techniques. FLMS being a web based application will allow its users to view alerts, contact the respective personnel and create awareness about the different stages of a disaster and ways to limit the losses caused by floods. © 2023 IEEE.

关键词： Machine learning

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Detecting Deceptive URLs: Exploring Logistic Regression and Decision Tree Models

Detecting Deceptive URLs: Exploring Logistic Regression and ...

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2024 IEEE Students Conference on Engineering and Systems, SCES 2024

作者： Kumar, Bhavishya Maria Anu, V. Vellore Institute of Technology Dept. of Computer Science&Engg. Chennai India

ISBN: (纸本)9798350374711

This research investigates the detection of phishing websites utilizing machine learning techniques and feature selection methodologies. Phishing remains a prevalent cyber threat, posing significant risks to users' sensitive information and online security. This study proposes a comprehensive approach that combines Chi-squared and XGBoost feature selection methods with logistic regression and Decision Tree classification models. By leveraging these techniques, it aims to achieve accurate and reliable identification of phishing websites while maintaining interpretability and generalizability. The findings underscore the importance of strategic feature selection and model building in enhancing the effectiveness of phishing detection systems. The results demonstrate promising outcomes, with both logistic regression and Decision Tree models exhibiting high accuracy in distinguishing between legitimate and phishing websites. Additionally, the key challenges such as feature scaling have been addressed to mitigate potential biases and data leakage. This research contributes to the advancement of cybersecurity measures, offering insights and methodologies to combat the persistent threat of phishing in the digital landscape. © 2024 IEEE.

关键词： Phishing

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A Novel Approach for Single-Shot Target Recognition by Matching Graph of Feature Pixels 2

A Novel Approach for Single-Shot Target Recognition by Match...

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Mitra, Baldivya Deshmukh, Maroti Kumar, Abhimanyu Dept. of Computer Science & Engineering National Institute of Technology Uttarakhand 246174 India

Image feature extraction and matching constitute fundamental steps in computer vision and image analysis, enabling various applications ranging from object recognition to scene reconstruction. Object recognition is one of the major applications of artificial intelligence. Since last two decades, different techniques for object recognition were given by researchers from across the world. This paper presents a novel approach for automatic target recognition in autonomous weapons by matching connected graph of feature pixels. The Moving and Stationary Target Acquisition and Recognition (MSTAR) is taken for experimental purpose. The Proposed model has three stages for a target recognition;Image Denoising, Feature extraction & graph generation, and feature matching. Firstly, a sample image of targets taken from the MSTAR is taken as an input. Further, input sample is de-noised and features are extracted using Speeded-Up Robust Features (SURF) algorithm. Thereafter, a graph is created using feature pixels and this complete process is repeated with test image. Further, both graphs are compared using a proposed feature graph matching algorithm which results an recognition accuracy score. Proposed model is tested over the MSTAR dataset and it resulted in varying accuracy at different feature matching thresholds. © 2024 Elsevier B.V.. All rights reserved.

关键词： Synthetic aperture radar

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Addressing the Long-Tailed Data Distribution in Bone Marrow Cell Identification through Class Balance Deep Classification Model 2

Addressing the Long-Tailed Data Distribution in Bone Marrow ...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Ghosh, Rakesh Shankar Shill, Pintu Chandra Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350317558

The accurate classification of bone marrow cells has long been a critical component of diagnosing blood diseases. However, traditional methods rely on subjective human interpretation and lack standardized quantitative criteria. To address this challenge, this research proposes a Class Balance Deep Classification Model (CBDCM) designed to classify bone marrow cells using EfficientNet to handle the long-tailed data distribution problem. In order to validate the propose model by applying it to proper dataset and analyzing the experimental outcomes of the propose method. Notably, CBDCM attains impressive precision, sensitivity, and specificity values of 86.14%, 87.53%, and 99.45%, respectively. Simulation results show that CBDCM outperforms, demonstrating superior performance in a detailed comparison with both deep neural networks and traditional machine learning techniques. The findings of this study, with significant implications for addressing class imbalance in datasets, hold promise for standardizing the classification of bone marrow cells, potentially revolutionizing the diagnosis of blood diseases. © 2024 IEEE.

关键词： bone marrow cell CBDCM data imbalance deep learning EfficientNet gaussian smoothing

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Simulator for Swarm Feature Selection Algorithms for Classification Problem 2

Simulator for Swarm Feature Selection Algorithms for Classif...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Kulkarni, Mayuri Diwakar Shahade, Makarand Deore, Shailesh Khandekar, Nilesh Bhavsar, Ishwari Garud, Jayesh Ahirrao, Kunal SVKM's Institute of Technology Dept. of Computer Engineering Dhule India SSVPS's B.S. Deore CoE Dept. of Computer Engineering Dhule India

ISBN: (纸本)9798350360523

Swarm Intelligence (SI) algorithms address difficult optimization and decision-making issues using cues from collective behavior. These behaviors are inspired by social creatures like ants, bees, and birds. Using these creatures' behaviors algorithms have demonstrated amazing promise in several fields, such as distributed computing, robotics, and optimization. Assessing SI algorithms' performance under various conditions and scenarios is still a significant difficulty, though. We introduce a new Swarm Intelligence Algorithm Performance Analysis Simulator (SIAPAS) to tackle this issue. This proposed simulator introduces a novel approach to automating the process of selection for Swarm Intelligence (SI) algorithms on datasets. The system empowers users to upload datasets into a specialized simulator, which then orchestrates a comparative analysis using a suite of SI algorithms. The simulator evaluates and selects the most suitable algorithm based on the dataset's characteristics and problem domain. This provides the optimal selected features by the respective algorithms based on measured parameters. © 2024 IEEE.

关键词： Swarm intelligence

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Architectural Design for AdhaarAssist - A Forward-Looking Lock-Free STM Solution for Adhaar

Architectural Design for AdhaarAssist - A Forward-Looking Lo...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Jafar, Sana Pranveer Singh Institute of Technology Dept. of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350385199

Any parallel program is judged based on its efficiency in doing multiple tasks at the same time without getting stuck due to an underlying problem. The main purpose of a parallel program is to effectively and quickly solve a large and complicated problem at hand. This is why;parallel programs form the basis of many powerful supercomputers where parallel computing is supported through special hardware which is complemented with parallel software achieving hundreds of petaflop/s of performance. The biggest challenge for a parallel programmer is to build a program that has a minimum tradeoff for increased parallelism and good concurrency control among the parallel tasks. One of the biggest applications of parallelism is the huge, ever-increasing database of Adhaar biometric. Its crucial to have a robust, fast and efficient way of accessing and manipulating this data so that along with faster results, consistency of the database is also maintained. This paper proposes a schema AdhaarAssist which is a lock-free STM based algorithm that will work on a shared in-memory data structure. Some of its features are increased concurrency by lazy conflict detection, partial aborts that optimize the algorithm for maximum throughput when complex and lengthy transactions are employed, memory barriers and timestamping. The algorithm proposed in this paper will open the way for leveraging it for distributed-shared in-memory databases like the one used by Adhaar. © 2024 IEEE.

关键词： Concurrency control

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Comprehensive Women's Health Risk Assessment Platform 2

Comprehensive Women's Health Risk Assessment Platform

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Navya, S. Saha, Sharmistha Nagarajan, Deepa Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349221

The goal of this research is to develop a machine learning-based method for predicting several diseases in women, with an emphasis on polycystic ovarian syndrome (PCOs), diabetes, heart disease, and breast cancer. Timely and accurate prediction of these disorders is essential for early intervention and prevention due to their impact on women's health and prevalence. Predictive models are trained using a wide variety of medical data, including genetic markers, lifestyle factors, demographic data, and clinical test findings. To find patterns and connections between these predictors and the incidence of diseases, machine learning methods are used, such as decision trees, random forests, and support vector machines. The models are able to forecast with accuracy the probability that a woman would develop one or more of these diseases by efficiently obtaining and evaluating data from these datasets. Metrics including accuracy, precision, recall, and F1-score are used to assess the models' performance;the outcomes demonstrate the models' promising levels of predictive power. The creation of a system like this can facilitate targeted screening and preventive measures, assist medical practitioners in identifying high-risk individuals, and eventually lead to better patient outcomes. © 2024 IEEE.

关键词： Diseases

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Alzheimer's Disease Prediction Using Modern Machine Learning Techniques 2

Alzheimer's Disease Prediction Using Modern Machine Learning...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Pranjal, Pragya Mallick, Saahil Das, Ananya Negi, Ayush Panda, Mohit Ranjan Kalinga Institute of Industrial Technology University Dept. of Computer Science Bhubaneswar India

ISBN: (纸本)9798350370188

The most well-known use of artificial intelligence is ML - Machine Learning, as it is currently revolutionizing the field of research. In this study, machine learning is applied to determine whether or not an individual suffers from Alzheimer's. Alzheimer's disease affects many people and even claims lives on a global scale. By taking into factors like discomfort in the chest, elevated cholesterol levels, a person's age, etc., machine learning techniques can be utilized to determine if a person has Alzheimer's. Four supervised ML algorithms are used in this paper - K-Nearest Neighbour estimates with a prediction accuracy of 90%, Random Forest estimates with a prediction accuracy of 89%, Artificial Neural Networks estimates with a prediction accuracy of 87%, and Logistic Regression estimates with a prediction accuracy of 87%. © 2024 IEEE.

关键词： Forecasting

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Effect of Varied Datasets on Training of a Segmentation Model Used in Visual Navigation

Effect of Varied Datasets on Training of a Segmentation Mode...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Wada, Marin Adachi, Miho Miyamoto, Ryusuke Graduate School of Science and Technology Dept. of Computer Science Japan Meiji University School of Science and Technology Dept. of Computer Science Kawasaki Japan

ISBN: (纸本)9798350324457

A visual navigation method based on results of semantic segmentation showed interesting results in previous researches. The most significant problem of the method is that the moving performance is affected by the segmentation accuracy, which strongly depends on the training data even though SOTA methods are adopted. To create high-quality dataset for this application without huge human efforts, the authors tries to generate datasets for semantic segmentation from a 3D scanned data composed of colored point clouds. In this study, we investigate what kinds of variations are effective to construct a classifier: variation of augmentation considering shadows, shooting angles, and shooting locations. Experimental results using actual images for evaluation and generated dataset for training captured at the course of Tsukuba Challenge, which is the famous competition for autonomous moving robots in Japan, showed that adding shadows in training datasets improved the mIoU but random changes to the shooting angle and location did not always work well. By the result, it is shown that augmentation considering the characteristic of the target environment becomes important for practical use. © 2023 IEEE.

关键词： 3D scanned data dataset generation semantic segmentation visual navigation

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Machine Learning Approaches for Survival Prediction in Covid-19 Patients: A Comparative Analysis

Machine Learning Approaches for Survival Prediction in Covid...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Islam, Md. Muhiminul Shuvo, M.I.R. Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350350289

The COVID-19 pandemic has underscored the significance of accurately predicting patient survival in order to promptly administer efficient medical care. The augmentation of biological and healthcare service data volume has been found to enhance the precision of disease prognosis, survival prediction, and other clinical assessments. Numerous biological characteristics serve as the underlying factors contributing to the etiology of numerous diseases. Therefore, it is imperative to have precise medical data that possesses appropriate characteristics in order to facilitate an analysis that exhibits exceptional clinical accuracy. In order to effectively analyze data, it is imperative to employ a machine learning model that is both exact and accurate in predicting sickness or survival outcomes. An expeditious and accurate assessment of the disease's magnitude is crucial during a particular phase of a pandemic, such as the Covid-19 outbreak. The primary aim of this research is to employ machine learning methodologies in order to forecast the survival outcomes of individuals diagnosed with COVID-19. This will be accomplished by using a publicly available dataset comprising various medical attributes pertaining to 383,499 COVID-19 patients, which was collected and made accessible by the Directorate General of Epidemiology, Secretariat of Health in Mexico. Various machine learning techniques, including Regression methods, Artificial Neural Networks, Random Forest Classifier, Support Vector Machine, AdaBoost, and XGBoost, are employed on the dataset that has undergone diverse preprocessing procedures. The experimental findings demonstrated that the system yielded numerous advantages in comparison to previous efforts in the same field. © 2024 IEEE.

关键词： COVID-19

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