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2023 IEEE International Conference on Big Data, BigData 2023

作者： Mutakabbir, Abdul Ajila, Samuel A. Carleton University Dept. of Systems and Computer Engineering OttawaON Canada

ISBN: (纸本)9798350324457

Long-term air pollution forecasting is essential for making public policies and issuing warnings. This will reduce the impact of pollution on the environment and human health. This research focuses on achieving long-term forecasting of air pollution attributes $(PM 2.5, PM_{10}, SO_{2}$, $NO_{2}, CO$, and $O_{3})$ by providing minimal historical data as input to the model. The best-performing models in this research produced between a 1% increase in RMSE for certain pollutants to a 50% decrease in RMSE for other air pollution attributes (except $CO)$ compared to the initial research on compositional learning, while being able to forecast the trend and seasonality accurately for more than one year into the future. The training time for each pollution attribute model dropped to 4 seconds compared to 1800 seconds in the case of the compositional learning method. This paper also delves into the challenges with long-term forecasting for the Beijing air quality dataset and discusses approaches to overcome these challenges. © 2023 IEEE.

关键词： Air Quality Index ARIMA Bagging Big Data Analytics Boosting Deep Learning Explainable AI Forecasting Machine Learning NeuralProphet Random Committee Random Forest Timeseries Forecasting

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Improved Object Detection Through a Learning-Based Joint-Initiative Supervised Autonomy (JISA) Framework

Improved Object Detection Through a Learning-Based Joint-Ini...

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2025 International Conference on Control, Automation, and Instrumentation, IC2AI 2025

作者： Tarabay, Andrea Daher, Naseem American University of Beirut Dept. of Electrical and Computer Engineering Beirut Lebanon American University of Beirut Dept. of Electrical and Computer Engineering Dept. of Mechanical Engineering Beirut Lebanon

ISBN: (纸本)9798331529604

Object detection is faced with accuracy and robustness limitations due to unstructured environments with objects of different sizes and occlusions. Despite advancements in object detection models, these models have not reached a point to possess a comparable level of perception to humans, thus limiting their ability to effectively address challenges in diverse environments. Hence, adopting a human-in-the-loop approach in autonomous systems can handle complex perception scenarios. To tackle these issues, the Joint-Initiative Supervised Autonomy (JISA) framework is used to allow a human supervisor to intervene once the agent is uncertain of its predictions based on its self-confidence (SC) metric, or when the human deems the prediction incorrect based on his/her situation awareness (SA). However, the existing JISA framework formally defines the levels of autonomy between the human and the agent, but does not provide a means for learning from human interventions. In this paper, we present an enhanced version of JISA in object detection applications by implementing a learning scheme that is inspired by the Interactive Corrections and Reinforcement for an Epistemic and Aleatoric uncertainty-aware Teaching (ICREATe) algorithm. To address these challenges, we evaluate the results of the proposed framework and demonstrate that the model effectively learns and improves its performance through human feedback. © 2025 IEEE.

关键词： Self-supervised learning

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Predicting Mental illness (Depression) with the help of NLP Transformers 2

Predicting Mental illness (Depression) with the help of NLP ...

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2nd International Conference on Data Science and Information System, ICDSIS 2024

作者： Kokane, Vedant Abhyankar, Ajit Shrirao, Nikhil Khadkikar, Prajakta Pune Institute Of Computer Technology Dept. Of Computer Engineering Pune India

ISBN: (纸本)9798350381665

In today's rapidly developing world, mental health is very important because it directly impacts the human thought process, productivity, relationships, etc. Being one of the most important aspects of a human's life, it is often neglected. Poor mental health can lead to anxiety, hopelessness, struggling to manage emotions, a lack of concentration, and loneliness. A mentally ill person can also have suicidal thoughts. In today's world, almost every person uses social media to share their lives and thoughts. With social media serving as a ubiquitous platform for self-expression and interaction, leveraging artificial intelligence (AI) and natural language processing (NLP) techniques becomes feasible for detecting mental health statuses based on users' social media posts. In this research paper, we proposed a methodology using NLP Transformers to study human behaviour as well as symptoms of depression. We analysed the status of mental health by examining the text of posts posted by different people on Twitter and Reddit. In our proposed methodology, we applied 4 different NLP Transformers (BERT, XLNet, RoBERTa, and DistilBERT) Transformers on two different datasets. Our proposed model concluded that DistilBert was the most accurate Transformer among the four with accuracy with 91% accuracy on twitter dataset and 84% accuracy on reddit dataset. Our proposed methodology will be effective in detecting mental illness in a person. © 2024 IEEE.

关键词： Diseases

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Breast Cancer Classification via the Use of ML and DL: A Comprehensive Review 3

Breast Cancer Classification via the Use of ML and DL: A Com...

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3rd International Conference on Automation, Computing and Renewable systems, ICACRS 2024

作者： Sarang, Ruvanshi Dabhi, Vipul Shah, Arpit Dept. of Computer Science Engineering Parul University Vadodara India Dept. of Computer Engineering Parul University Vadodara India

ISBN: (纸本)9798331532420

New and improved methods of diagnosis are needed because breast cancer is still the leading cancer-related killer worldwide. Updates to the methods used to categorize breast cancer have emerged because of recent advances in DL and ML. This review paper’s goal in conducting this research is to bring together existing breast cancer diagnostic and classification methods that make use of deep learning and machine learning techniques. We examine the many approaches, datasets, and performance indicators used in the industry and point out the pros and cons of each as part of the review paper. This review aims to thoroughly examine all the methods that are now in use, spot any trends, and propose new areas for research. This review helps researchers and practitioners understand the current state of breast cancer categorization by combining the findings of past research. Discuss recent advances in ML/DL techniques for improving classification, such as ensemble methods, transfer learning, or hybrid models. Additionally, propose the use of fine-tuning of hyperparameters or novel architectures to enhance classification. © 2024 IEEE.

关键词： Adversarial machine learning

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AI-based Visual Attention Scenario Identification Model in Military Environment 4

AI-based Visual Attention Scenario Identification Model in M...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Sapariya, Ankur Ravikumar, R.N. Bhatt, Urvi Singh, Suraj Prakash Wanglen, Soram Singh, Sushil Kumar Marwadi University Dept. of Computer Engineering Rajkot India School of Management Sciences Dept. of Computer Engineering Varanasi India

ISBN: (纸本)9798350343670

In response to the evolving landscape of modern military operations, where drone technology now constitutes an estimated 70% of Indian military missions, as evidenced by recent statistics, our proposed AI-based Visual Attention Scenario Identification Model takes center stage. This groundbreaking model is meticulously designed to augment the safety and efficiency of military endeavors in challenging terrains. Our model harnesses advanced deep-learning CNN techniques by processing real-time images from various sources, including drones, strategically focusing on critical details within these visuals. This approach enables the timely detection of potential dangers, even in complex environments, with the primary objective of significantly enhancing situational awareness for military personnel. Positioned as a valuable asset in the military's toolkit, our model, with 85% accuracy, proves particularly effective in identifying potential threats in forested and mountainous regions, ultimately minimizing risks faced by soldiers on the ground. © 2024 IEEE.

关键词： CNN Deep Learning Image Processing Military Operations Visual Attention AI model

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Unravelling the Enigma of Polycystic Ovary Syndrome (PCOS): Using MLAlgorithms 2

Unravelling the Enigma of Polycystic Ovary Syndrome (PCOS): ...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Juneja, Sonam Bhati, Bhoopesh Singh Atwal, Shikha Anand, Monika Maiti, Souvik Chandigarh University Dept. of Computer Science and Engineering Gharuan India Iiit Dept. of Computer Science and Engineering Sonepat India

ISBN: (纸本)9798350371055

The difficulties and ramifications of PCOS, which affects a sizable portion of women who are of reproductive age, are discussed in this review. The article covers the various clinical manifestations of PCOS, how it affects both reproductive and non-reproductive health, and how it's linked to psychological distress and metabolic disorders. It highlights how critical lifestyle modifications, early detection, and accurate diagnosis are. Additionally, the study presents machine learning techniques for PCOS detection, demonstrating the effectiveness of techniques like Random Forests, CNN, and SVM. An innovative CDSS that uses the Red Deer Algorithm shows encouraging accuracy. The necessity of continued research, diversified datasets, and cooperative efforts to enhance PCOS detection at the nexus of technology and healthcare is highlighted in the abstract's conclusion. © 2024 IEEE.

关键词： Deep Learning KNN Machine Learning PCOS Random Forest SVM

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Architectural Model of Deep Convolution Neural Network Approach for Brain Tumor Prediction

Architectural Model of Deep Convolution Neural Network Appro...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Panigrahi, Raghunath Sahu, Rekha Nayak, Suvendu Chandan Kabat, Manas Ranjan IMIT Dept. of Computer Science and Engineering Cuttack India Silicon University Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350369175

Magnetic Resonance Imaging (MRI) is most commonly used technique to detect a tumor by physicians in medical science. The MRI images are three-dimensional images with the number of slices that are analyzed to identify the defective cells in the naked eye that leads to a time-consuming process. Machine Learning (ML) techniques are investigated as a solution to improve tumor identification in a better way. Early and accurate detection is crucial for effective treatment planning and improving patient outcomes. Traditionally, brain tumor diagnosis relies on manual analysis of MRI (Magnetic Resonance Imaging) scans by radiologists, which can be time-consuming and prone to human error. The advent of deep learning, particularly Convolutional Neural Networks (CNNs), has opened new avenues for automating and improving the accuracy of brain tumor prediction. In this paper, we study the Convolutional Neural Network(CNN) model which is popular in image analysis. Similar way, we develop a CNN model to process and classify the MRI images to detect the type of brain tumor. We examine the model's performance by using 5712 MRI images from a standard dataset available in the public domain. The aim of the work is to classify the tumor in four classes like: Glioma, Meningioma, Pituitary and No Tumour using the proposed CNN model. Our model follows data preprocessing, image encoding, filtering and classification. The proposed model scores 78% accuracy as its performance in the dataset which needs more investigations and comparison with other techniques in further study. © 2024 IEEE.

关键词： Convolutional neural networks

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Artificial Intelligence to Combat Audio Fraud: A Flask-Deployed Hybrid Deep Learning System 12

Artificial Intelligence to Combat Audio Fraud: A Flask-Deplo...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Solanki, Shashank Ravikumar, R. Devataval, Tushar Gajera, Jeet Kumar Singh, Sushil Gupta, Himanshu Marwadi University Dept. of Computer Engineering Rajkot India Marwadi University Dept. of Computer Engineering-AI Rajkot India

ISBN: (纸本)9798350340204

As the use of synthetic communication is growing widely spread, the ability to accurately identify counterfeit spoken words is now more important than ever. In this study, we have developed an advanced system using deep learning techniques that can differentiate between real and modified audio files. Our research employs Kaggle's DeepFake Voice Recognition Dataset and explores different deep learning architectures such as "Artificial Neural Networks (ANN)", "Convolutional Neural Networks (CNN)", and "Recurrent Neural Networks (RNN)". One crucial novelty of our work lies in merging these models with XGBoost, a machine learning algorithm, to create a hybrid system which greatly enhances the accuracy of detecting false audio tracks. What is surprising, however, is that RNN and XGBoost showed accuracy rate of 99.32% outperforming other settings. Additionally, we created an easy-to-use web application using Flask enabling users to upload and verify authenticity of voice records easily. This system tackles both the critical problem of voice forgery as well as advances in the field of sound authentication technology. © 2024 IEEE.

关键词： Convolutional neural networks

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Using Ant Colony Optimised Algorithms for Shortest Path Exploration by Robots 2

Using Ant Colony Optimised Algorithms for Shortest Path Expl...

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

作者： Singh, Aashdeep Dhawan, Sakshi Verma, Jyoti Singh, Gurpreet Kaur, Amanpreet Dept. of Computer Science & Engineering Punjab Rajpura India Dept. of Computer Application Punjab Rajpura India Chitkara University Institute of Engineering & Technology Dept. of Computer Science & Engineering Punjab Rajpura India

ISBN: (纸本)9798350360523

In the realm of robotics and autonomous systems, efficient path planning is a critical aspect for optimizing resource utilization and achieving mission objectives. This study explores the application of Ant Colony Optimized (ACO) algorithms to enhance the efficiency of robots in navigating and exploring unknown environments. Inspired by the foraging behavior of ants, ACO algorithms are employed to find the shortest paths by leveraging pheromone communication and decentralized decision-making. Ant Colony Optimization is then introduced as a bio-inspired solution to address these challenges. The experimental results demonstrate the capability of the ACO algorithm to efficiently explore complex environments, adapt to changes in the surroundings, and converge to optimal paths. Insights gained from these analyses contribute to the refinement of the ACO algorithm for diverse robotic applications. The findings of this research have practical implications for the development of autonomous robots operating in real-world scenarios, such as search and rescue missions, environmental monitoring, and surveillance. This study provides a valuable foundation for the integration of bio-inspired algorithms in robotics, advancing the state-of-the-art in autonomous exploration and path planning. © 2024 IEEE.

关键词： Motion planning

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Integration between Bioinformatics Algorithms and Neutrosophic Theory

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Neutrosophic Sets and systems 2024年 66卷 34-54页

作者： Farag, Romany M. Shams, Mahmoud Y. Aldawody, Dalia A. Khalid, Huda E. El-Bakry, Hazem M. Salama, Ahmed A. Dept. of Math and Computer Science Faculty of Science Port Said Univ. Egypt Dept. of Machine Learning Faculty of Artificial Intelligence Kafrelsheikh University Egypt Telafer University Telafer Iraq Dept. of Information Systems Faculty of Computer and Information Sciences Mansoura University Egypt Dept. of Math and Computer Science Faculty of Science Port Said University Egypt

This paper presents a neutrosophic inference model for bioinformatics. The model is used to develop a system for accurate comparisons of human nucleic acids, where the new nucleic acid is compared to a database of old nucleic acids. The comparisons are analyzed in terms of accuracy, certainty, uncertainty, neutrality, and bias. The proposed system achieves good results and provides a reliable standard for future comparisons. It highlights the potential of neutrosophic inference models in bioinformatics applications. Data mining and bioinformatics play a crucial role in computational biology, with applications in scientific research and industrial development. Biological analysts rely on specialized tools and algorithms to collect, store, categorize, and analyze large volumes of unstructured data. Data mining techniques are used to extract valuable information from this data, aiding in the development of new therapies and understanding genetic relationships between organisms. Recent advancements in bioinformatics include gene expression tools, Bio sequencing, and Bio databases, which facilitate the extraction and analysis of vital biological information. These technologies contribute to the analysis of big data, identification of key bioinformatics insights, and generation of new biological knowledge. Data collection, analysis, and interpretation in this field involves the use of modern technologies such as cloud computing, machine learning, and artificial intelligence, enabling more efficient and accurate results. Ultimately, data mining and bioinformatics enhance our understanding of genetic relationships, aid in developing new therapies, and improve healthcare outcomes. © (2024), (Neutrosophic Sets and systems). All rights reserved.

关键词： Bioinformatics

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