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2024 International Conference on Smart Devices, ICSD 2024

作者： Kumar, Vivek Kumar, Kamal Singh, Maheep National Institute of Technology Dept. of Computer Science -and Engineering Uttarakhand Pauri India IGDTUW Department of Information Technology New Delhi India Doon University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350394412

Deep learning models have been applied in various fields and continue producing exceptional results. However, these models are vulnerable to adversarial attacks, which are modified data samples maliciously crafted to cause misclassification by a target model. Fields like Network intrusion detection systems (NIDS) are security-critical;therefore, adversarial attacks pose a high risk in such applications. The attacker not only aims for evasion but also tries to launch an attack once the adversarial example has evaded detection by a NIDS. This paper presents a novel technique for detecting adversarial examples targeted towards a NIDS by learning the dependencies between features known as domain constraints. Our proposed method detects adversarial examples that violate these constraints learned during training. Compared with prevalent detection techniques, our method does not require generating adversarial examples for training, which reduces its computational requirement. We assume a grey-box threat model where the attacker can access the training data. We perform experiments on the CICIDS2017 dataset against five state-of-the-art adversarial attacks: FGSM, BIM, PGD, Deep fool and BIM. The results show that our method can detect up to 100% adversarial attacks, outperforming the contemporary schemes. © 2024 IEEE.

关键词： Adversarial machine learning

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An ensemble learning-based machine learning with voting mechanism for chronic disease prediction

An ensemble learning-based machine learning with voting mech...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Soualihou, Soualihou Ngnamsie Kim, Jeong-Dong Zonyfar, Candra Lee, Hyun Lee, Taek Lee, Jung-Been Sun Moon University Dept. of Computer Science and Electronic Convergence Asan Korea Republic of Sun Moon University Dept. of Computer Science and Engineering Asan Korea Republic of Sun Moon University Division of Computer Science and Engineering Asan Korea Republic of

ISBN: (纸本)9798350394528

Chronic diseases present a significant challenge in healthcare, often requiring ongoing medical attention and posing limitations on patients' daily activities. Diagnosis of such diseases is hindered by the absence of specific symptoms making them hard to detect and or prevent. Addressing this issue, researchers have turned to computational approaches, analyzing patients' medical records to predict the presence or absence of chronic diseases with promising results. However, there is potential for further improvement. This paper introduces an ensemble classifier, ELVot-CroDiP, designed to enhance chronic disease prediction. ELVot-CroDiP harnesses the collective strengths of various machine learning algorithms through a majority voting mechanism, thereby boosting predictive accuracy. The model's performance was evaluated using precision, recall, f1-score, and accuracy across five medical datasets, including heart disease, chronic kidney disease, diabetes, and heart stroke. Comparative analysis shows that ELVot-CroDiP offers superior performance in predicting chronic diseases compared to existing models, as evidenced by its higher scores in the mentioned evaluation metrics for each dataset tested. © 2024 IEEE.

关键词： Forecasting

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Enhanced Rainfall Prediction: Leveraging Ensembling Models for Maximum Forecasting Performance 1

Enhanced Rainfall Prediction: Leveraging Ensembling Models f...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dasari, Krishna Kumar Nuthanakanti, Bhaskar Raju, K.Srujan Madhusudhan, Mudimela Nazare, Sanjana S Raji Reddy, Avala Cmr Technical Campus Dept. Computer Science and Engineering Telangana Hyderabad India Cmr Dept. of Mechanical Engineering Telangana Hyderabad India

ISBN: (纸本)9798350367720

Many important fields rely on accurate rainfall predictions, including agriculture, water resource management, and emergency preparation. The many nonlinear interactions included in weather data are notoriously difficult for traditional approaches to grasp. Agriculture, water resource management, and disaster preparation are just a few of the many sectors that might benefit greatly from accurate rainfall predictions. Because meteorological data is both complicated and dynamic, traditional methods often get it wrong. Recent years have seen encouraging results in the use of machine learning (ML) approaches, especially ensemble models, to enhance the precision and consistency of precipitation forecasts. This research provides a new method for predicting precipitation using an ensemble of ML models. The ensemble model EERP algorithm combines the predictions of numerous base learners to create a more reliable and accurate forecast. Decision trees, random forests, gradient boosting machines, neural networks, and other ML approaches serve as the ensemble's base learners. Assess the effectiveness of the proposed approach and then compare it with some existing classifiers in which the proposed classifier gave the best accuracy when compared with the other i.e., 96 %. © 2024 IEEE.

关键词： Prediction models

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Using Machine Learning Algorithms in Edge Intelligence for Ultra-Reliable and Low-Latency Communications in the Industrial Metaverse 29

Using Machine Learning Algorithms in Edge Intelligence for U...

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29th International computer Conference, computer Society of Iran, CSICC 2025

作者： Mohammadvand, Reza Mozayani, Naser Dept. Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9798331523114

Given the rapid advancements in technologies such as the metaverse and digital twins, the need for effective integration of communication, computation, and storage in complex systems and edge computing environments is becoming increasingly apparent. This paper proposes an architecture to support an industrial metaverse based on digital twins that optimizes resources by leveraging mobile edge computing and ultra-reliable low-latency communication. This architecture reduces latency by employing task offloading and storage methods on nearby edge servers, meeting the precise requirements of future metaverses in terms of reliability and latency minimization. The proposed model, utilizing reinforcement learning algorithms such as Deep Q-Network (DQN), Double Deep Q-Network (DDQN), and Dueling Double Deep Q-Network (Dueling DDQN), is capable of achieving adaptability in dynamic conditions and making intelligent decisions. The simulation results demonstrate that the proposed method reduces by more than 10% on average compared to the best method available in the literature and improves resource utilization efficiency. © 2025 IEEE.

关键词： Reinforcement learning

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BanglaGPT: A Generative Pretrained Transformer-Based Model for Bangla Language 2

BanglaGPT: A Generative Pretrained Transformer-Based Model f...

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2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Salim, Md. Shahidul Murad, Hasan Das, Dola Ahmed, Faisal Khulna University of Engineering Technology Dept. of Computer Science and Engineering Khulna9203 Bangladesh Chattogram University of Engineering Technology Dept. of Computer Science and Engineering Chattogram4349 Bangladesh

ISBN: (纸本)9798350358667

Natural Language Processing (NLP) has entered a new era with the advent of pre-trained language models, paving the way for constructing robust language models. Pretrained transformer-based models such as GPT-2 have become prevalent due to their cutting-edge efficiency. However, these approaches rely heavily on resource-intensive languages, forcing other languages to adopt multilingual frameworks (mGPT). The mGPT model could perform better for low-resource languages such as Bangla because the model has been trained on a diverse dataset spanning multiple languages. Recent studies show that the language-specific GPT model outperforms the multilingual mGPT model. In this research, we have proposed a pretrained monolingual GPT model called BanglaGPT using the objective of causal language modeling (CLM). Due to the lack of available large datasets for NLP tasks in Bangla, we have created a Bangla language model dataset called BanglaCLM using a 26.24 GB Bangla corpus scraped from several public websites. We have used a subword-based tokenization algorithm named Byte-Pair Encoding (BPE) for Bangla and finally trained the Bangla-GPT2 model from scratch using the BanglaCLM dataset. Our pretrained BanglaGPT provides state-of-the-art performance for Bangla text generation with a perplexity score of 2.86 and a loss score of 0.45 on the test set. © 2023 IEEE.

关键词： Large dataset

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Explainable AI-based Detection and Interpretation of Abnormalities in Chest X-rays 3

Explainable AI-based Detection and Interpretation of Abnorma...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Muthukumar, Sai Smrithi Gandham, Bhavika Vijayekkumaran, Trisha Jyotsna, C. Aiswariya Milan, K. Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331523893

Chest X-ray images are widely used in diagnosing medical conditions, however, due to radiologist fatigue and shortage of resources the possibility of spotting peculiarities increases. This work proposes an explainable AI system to identify, quantify, and explain the pathology found in chest x-ray images. It builds credibility, enhances medical judgment, and reflects efficient utilization of radiology assets. This study used ResNet for the chest X-ray image classification and compared it with other related models like EfficientNetB0, DenseNet, VGGNet, and MobileNet. ResNet had the best AUC value of 0.826 thereby garnering it the best classification accuracy hence validating its selection as the main model. Moreover, ResNet adapted explainability to provide visual and textual interpretations and to classify diseases where such actionable insights could improve the reliability of diagnosis and patient outcomes. © 2025 IEEE.

关键词： CheXpert Explainable AI Grad-CAM ResNet X-ray masks X-rays

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Towards Developing a Transformer-Based Bangla Typing Error Correction Model: A Deep Learning-Based Approach 2

Towards Developing a Transformer-Based Bangla Typing Error C...

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2nd International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2023

作者： Salim, Shahidul Islam, Taqiul Zannat, Rowzatul Mia, Naeem Fuad, Mohtasim Murad, Hasan Khulna University of Engineering Technology Dept. of Computer Science and Engineering Khulna9203 Bangladesh Chattogram University of Engineering Technology Dept. of Computer Science and Engineering Chattogram4349 Bangladesh

ISBN: (纸本)9798350358667

Despite being the sixth most widely spoken language globally, the Bangla language faces a lack of effective and efficient automated methods to correct keyboard errors in written text. Such errors can result in communication misunderstandings and misinterpretations, particularly in the digital domain, where written text plays a crucial role in communication and is heavily relied upon. However, no keyboard error model for the Bangla language has been found due to the lack of available datasets. In this research work, we have proposed a probabilistic approach to generate a synthetic Bangla keyboard typing error correction (BKTEC) dataset. By utilizing the novel BKTEC dataset, we have fine-tuned a transformer-based model for Keyboard error correction. After training it to convergence, the proposed model has been tested on a test dataset, and we have found a word level accuracy of 88.56%. © 2023 IEEE.

关键词： Error correction

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A Comparative Analysis of Brain Atlases and DNN-Based Autism Spectrum Disorder Detection Using IMPAC Dataset

A Comparative Analysis of Brain Atlases and DNN-Based Autism...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Kabir, Mir Arnab Arafin, Protiva Barman, Shawon Hussein, Molla Rashied Tasnim, Rumana Zonayed, Md University of Asia Pacific Dept. of Computer Science & Engineering Dhaka Bangladesh World University of Bangladesh Dept. of Mechatronics Engineering Dhaka Bangladesh

ISBN: (纸本)9798350369106

An autism spectrum disorder (ASD) is a physiological condition that impairs an individual's ability to acquire skills, engage in social interactions, empathize, and socialize. Early intervention is crucial to alleviate intellectual, behavioral, and functional challenges related to ASD. The significance of early ASD assessment cannot be overstated, as it is rapidly addressed through cognitive progress, yielding equally favorable outcomes. This research work developed a machine learning (ML) model for early assessments of autism spectrum disorder (ASD) utilizing functional magnetic resonance imaging (fMRI) data and deep neural networks (DNNs). A comprehensive analysis is carried out utilizing seven distinct brain atlases from the IMPAC dataset. The results indicate that among the seven atlases, Craddock 200 (CC200) attains the highest ROC AUC of 85.091% and an impressive accuracy of 82.759%, which outperforms numerous current state-of-the-art models. These findings emphasize the potential of fMRI-based machine learning models, especially deep neural networks, in improving the precision and efficacy of early autism spectrum disorder diagnosis. Such enhancements offer potential for enhancing the lives of individuals with ASD, as prompt treatments can profoundly influence their developmental trajectory and general well-being. © 2024 IEEE.

关键词： Diseases

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AI-Driven Power Gating for Enhanced Energy Efficiency in Superscalar Processors 31

AI-Driven Power Gating for Enhanced Energy Efficiency in Sup...

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Kalra, Naman Tudu, Jaynarayan T. Dept. of Electrical Engineering Indian Institute of Technology Tirupati India Dept. of Computer Science Engineering Indian Institute of Technology Tirupati India

ISBN: (纸本)9798331509118

Amid the rising demand for efficient processors, the challenge has always been to reduce power consumption without compromising performance. FinFET technology has significantly reduced leakage power issues, but dynamic power consumption at lower nodes has reemerged as a concern. Methodologies such as power/clock gating, DVFS, and voltage biasing have been proposed in the past. However, next-generation complex SoC designs at © 2024 IEEE

关键词： System-on-chip

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Optimal Trajectory Planning of an Omni-directional Mobile Manipulator for Obstacle Avoidance

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Transactions of the Korean Institute of Electrical Engineers 2023年第8期72卷 952-958页

作者： Kim, Minsu Sung, Young Whee School of Electrronic Engineering Kumoh National Institute of Technolgy Korea Republic of Dept. of Electrical Engineering and Computer Science DGIST Korea Republic of

The ultimate purpose of a mobile robot is to do some tasks in addition to moving to a desired point, so it is very natural for a mobile robot to be equipped with a manipulator. Among many types of mobile robots, omni-directional mobile robot has been very popular because it has advantages in trajectory planning and control. In this paper, we deal with an omni-directional mobile manipulator that consists of a four Mecanum wheeled mobile robot platform and a manipulator mounted on it. In general, it is very difficult to solve inverse kinematics for a mobile manipulator because it has infinite solutions. Omni-directional mobile robot platform can move in any direction and can rotate on a pivot in a plane, so we propose it to be modeled as a manipulator with two prismatic joints and one revolute joint. Then the overall omni-directional mobile manipulator can be considered as a manipulator with an (n+3) DOF manipulator if the mounted manipulator has n DOF. We propose a weighted pseudo-inverse based method to solve the inverse kinematics problem at velocity level for the omni-directional mobile manipulator. In our method, in addition to a primary task of following end-effector trajectory, a secondary task of obstacle avoidance is accomplished by maximizing a performance index that represents a distance between a link of the manipulator and an obstacle. Simulation results showed the feasibility of the proposed method. © 2023 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Manipulators

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