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arXiv 2024年

作者： Hossain, Eftekhar Sharif, Omar Hoque, Mohammed Moshiul Preum, Sarah M. Department of Computer Science and Engineering Department of Computer Science Dartmouth College United States Department of Electronics and Telecommunication Engineering Chittagong University of Engineering & Technology Bangladesh

Multimodal hateful content detection is a challenging task that requires complex reasoning across visual and textual modalities. Therefore, creating a meaningful multimodal representation that effectively captures the interplay between visual and textual features through intermediate fusion is critical. Conventional fusion techniques are unable to attend to the modality-specific features effectively. Moreover, most studies exclusively concentrated on English and overlooked other low-resource languages. This paper proposes a context-aware attention framework for multimodal hateful content detection and assesses it for both English and non-English languages. The proposed approach incorporates an attention layer to meaningfully align the visual and textual features. This alignment enables selective focus on modality-specific features before fusing them. We evaluate the proposed approach on two benchmark hateful meme datasets, viz. MUTE (Bengali code-mixed) and MultiOFF (English). Evaluation results demonstrate our proposed approach’s effectiveness with F1-scores of 69.7% and 70.3% for the MUTE and MultiOFF datasets. The scores show approximately 2.5% and 3.2% performance improvement over the state-of-the-art systems on these datasets. Our implementation is available at https://***/eftekhar-hossain/Bengali-Hateful-Memes. © 2024, CC BY-NC-SA.

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A Comprehensive Ensemble-Based Approach for Heart Disease Prediction with Feature Selection and Model Analysis 5

A Comprehensive Ensemble-Based Approach for Heart Disease Pr...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Tahosin, Mst. Sazia Hossain Pranto, Ismail Uddin Chowdhury, Mohamamd Muid Barua Emon, Sonet Alam, Md Ashraful Khan, Chonchal Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science and Engineering Dhaka Bangladesh Chittagong University of Engineering and Technology Department of Electronics and Telecommunication Engineering Chattogram Bangladesh Noakhali Science and Technology University Department of Computer Science and Telecommunication Engineering Noakhali Bangladesh Colorado State University Department of Computer Science CO United States

ISBN: (纸本)9798331530983

Heart disease is a leading global cause of mortality, emphasizing the urgent need for early and accurate diagnostic methods. However, traditional diagnostic tools often lack the scalability and precision to meet diverse healthcare demands. We propose a robust ensemble-based machine learning framework for heart disease prediction that combines advanced data preprocessing, feature selection, and model optimization techniques to address this. This study utilized a consolidated dataset of 1,888 records from five heart health databases. First, systematic preprocessing addressed missing values and standardized features. Next, a hybrid feature selection strategy, integrating Univariate Selection, Correlation Coefficient Analysis, and Mutual Information, reduced the feature set from 14 to 11 key indicators. Subsequently, we evaluated 12 machine learning models on all and reduced feature sets. Finally, our ensemble model, combining Random Forest, Decision Trees, XGBoost, CatBoost, and Extra Trees, achieved an outstanding accuracy of 98.12±1.20. Thus, our results demonstrate that integrating multiple classifiers with optimized feature selection significantly enhances predictive accuracy and model reliability. This scalable framework can transform early heart disease detection, improving outcomes across diverse healthcare settings. © 2025 IEEE.

关键词： Cardiology

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Objective Sleep Quality Measurement based on Fuzzy Logic and Wearable Device 8

Objective Sleep Quality Measurement based on Fuzzy Logic and...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Wijaya, Jason A. Andersen, Pauw D. Surantha, Nico Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia Department of Electrical Electronic and Communication Engineering Tokyo City University Japan

Sleep is the natural state of relaxation for human being. Sleep quality is an essential yet frequently neglected aspect of sleep in general. Sleep quality is essential because it allows the body to restore itself and prepare for the next day. The standard method for evaluating sleep quality was subjective evaluation. Actigraphy devices, which can measure the sleep cycle, are now widely available. This study developed a method using Fuzzy Logic and an actigraphy device to measure and classify sleep quality. The fuzzy logic method was developed in several stages, which are determining the sleep quality measurement parameters, constructing the fuzzy set for each input variable, and developing the fuzzy rules. To evaluate the proposed fuzzy model, five individuals were invited to participate in the experiment and required to complete the PSQI subjective sleep questionnaire. The evaluation result shows that our proposed Fuzzy model achieves lower error compared to the existing method. © 2023 The Authors. Published by Elsevier B.V.

关键词： actigraphy artificial intelligences fuzzy logic sleep sleep quality

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Exploring the Potential of Transfer Learning for Accurate Segmentation of Pathological Structures in X-Ray Images 2

Exploring the Potential of Transfer Learning for Accurate Se...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kishore, Lalit Shaikh, M.Z. Goel, Pawan Kumar Badhoutiya, Arti SRMIST Delhi Ncr Department of Computer Application UPGhaziabad Modinagar India Shree Bhagubhai Mafatlal Polytechnic College Department of Electronics & Telecommunication Maharastra Mumbai India Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Gla University Department of Electrical Engineering UP Mathura India

ISBN: (纸本)9798350371055

This paper explores the capability of transfer learning for the correct segmentation of pathological systems in X-ray pictures. Switch mastering is a way in gadget mastering wherein understanding received from a source challenge is transferred to an extraordinary goal task to enhance performance. This paper specializes in how transfer getting to know may be applied to segmenting pathological systems in X-ray photos. The paper discusses the proposed technique's overall performance on datasets from public medical resources gathered from healthy and diseased lungs and stomach organs. The experiments used in this paper display that with the resource of pre-educated models in transfer getting to know, the performance of the segmentation of the pathological regions in X-ray photos may be stepped forward. Eventually, the paper outlines the ability of transfer mastering for clinical picture segmentation and discusses how it can be further progressed in the future. © 2024 IEEE.

关键词： Healthy Pathological Potential And Medical Transfer

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Automated Vehicle License Plate Recognition System: An Adaptive Approach Using Digital Image Processing 1

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3rd International Conference on Sustainable Advanced Computing, ICSAC 2021

作者： Rahman, Saidur Trisha, Tahmina Aktar Imran Hossain Imu, Md. Department of Electronics and Telecommunication Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering Daffodil Institute of IT Dhaka Bangladesh

ISBN: (数字)9789811690129

ISBN: (纸本)9789811690112

In the last twenty years, the number of vehicles has increased rapidly in our country. With this increase, it is gradually more difficult to track each vehicle for effective law enforcement and traffic management purposes. The growing rate of cars brings upon itself the need to rapidly check the lawfulness of the drivers. When traveling, there are several legal obligations a driver has to fulfill according to the law. In this paper, we proposed an automatic license plate recognition (ALPR) system is a digital image processing procedure that identifies the vehicle number plate without human involvement. It is a computer system that automatically detects and recognizes any digital image on the number plate. This system involves image preprocessing and edge detection using a canny edge detection algorithm, regional localization, and pytesseract used to identify the English characters in the number plate efficiently as well as with less computational complexity. This system simulates an accuracy rate of 97% (approximate) with a comparison of the average processing time of 0.75 s. Our automated system might be highly effective in real-time traffic control, security enhancement in parking lots in shopping malls, hospitals, airports, as well as in electronic toll collection. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical character recognition

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CIMulator: Evaluating Neuromorphic Accelerators with HZO FefinFET-Based Synaptic Device 7

CIMulator: Evaluating Neuromorphic Accelerators with HZO Fef...

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7th International Conference on Green Technology and Sustainable Development, GTSD 2024

作者： Le, Hoang-Hiep Lu, Hao-Yu Baig, Md. Aftab De, Sourav Lin, Ing-Chao Lu, Darsen D. Minghsin University of Science and Technology Department of Computer Science and Information Engineering Hsinchu Taiwan National ChengKung University Intelligent Computing Industrial Doctorate Program Tainan Taiwan Institute of Microelectronics National ChengKung University Tainan Taiwan National ChengKung University Department of Computer Science and Information Engineering Tainan Taiwan

ISBN: (纸本)9798350386073

In this study, we use the CIMulator platform to evaluate the performance of neuromorphic accelerators with novel Hf0.5Zr0.5O2 (HZO) ferroelectric fin field-effect transistor (FefinFET) as synaptic device. The MNIST handwritten digit dataset is used for training and inference processes in multilayer perceptron (MLP) neural networks. With the highly optimized synaptic device, the results demonstrate high training and inference accuracy, achieving up to 97% for the MLP, approaching the accuracy of software implementations. © 2024 IEEE.

关键词： Multilayer neural networks

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Design of surrogate models in civil engineering by neural networks 9

Design of surrogate models in civil engineering by neural ne...

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9th South-East Europe Design Automation, computer engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2024

作者： Drahy, Vojtech Marik, Radek Kalviainen, Heikki Czech Technical University in Prague Department of Computer Science Prague Czech Republic Czech Technical University in Prague Department of Telecommunication Engineering Prague Czech Republic Lappeenranta-Lahti University of Technology Lut Computer Vision and Pattern Recognition Laboratory Lappeenranta Finland

ISBN: (纸本)9798350342482

We present a task from the critical infrastructure field in materials engineering. We created a surrogate model for the bridge construction object to determine the material parameters' values. The work aims to use neural networks to conduct an initial investigation of the task and to find out the aspects of machine learning application. To reduce the computational complexity of the models, we designed specific neural networks whose architecture corresponds to the structure and characteristics of the processed data. Furthermore, we outcome also interpretability and justification of the model's decision-making. The main contribution of the work is the replacement of the unknown or too complex physical, mathematical description of material objects with a neural network model. © 2024 IEEE.

关键词： Neural network models

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Hotel Information System Management Audit Using COBIT 2019 - APO12 2

Hotel Information System Management Audit Using COBIT 2019 -...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Utamajaya, Joy Nashar Supangat, Suhono Harso Gaol, Ford Lumban Ranti, Benny BINUS University BINUS Graduate Program School of Information System Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatic Bandung Indonesia Bina Nusantara University Computer Science Department BINUS Graduate Program Computer Science Jakarta Indonesia University of Indonesia Computer Science Department Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350320923

In general, public or private organizations or companies have used information-based technology as a support to improve business performance to be more effective and efficient in order to achieve a company's business goals. Given the large contribution of information technology in the application of hotel applications as a supporting information system, it is a vital system that must avoid risks that can hinder and cause harm to the hotel management business processes. Therefore, it is necessary to carry out risk management using the COBIT 5 framework to manage possible risks that may occur based on the APO12 (Manage Risk) domain. From the evaluation results of the data obtained through observation and interviews as well as the calculation of the results of the questionnaire based on 6 APO12 subdomains, the results of the risk management level capability assessment in hotel applications are still at level 3 or have reached the level of established process with the target to be achieved at level 4 resulting in a gap of 1 level. Based on the results obtained, it is necessary to propose recommendations that can be used by hotels in improving the application of information technology risk management so that in the future it can achieve the expected target level so that the APO12 level of capability can increase and become more optimal. © 2023 IEEE.

关键词： Risk management

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An Improved Particle Swarm Approach for Energy-Aware Location-Aided Routing in Mobile Ad-Hoc Network 7

An Improved Particle Swarm Approach for Energy-Aware Locatio...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kumar, Sandeep Sharma, Khemraj Ashok Kumar, P.M. Jain, Arpit Bhagat, Sukesh Kumar Singh, Pranita Tula's institute U.K Dehradun India Nanhua University Doctoral Program in Management Sciences Department of Business Administration Dalin Chiayi62248 Taiwan Koneru Lakshmaiah Education Foundation Department of Computer Science Engineering AP Vaddeswaram522502 India Uttaranchal University Department of Computer Science & Engineering Uttarakhand Dehradun India Roorkee Institute of Technology Department of Computer Science and Engineering Uttarakhand Roorkee India

ISBN: (纸本)9798350350067

A network with no infrastructure, multiple hops, and dispersed nodes called a MANET. Nodes can communicate directly or through intermediaries without the need for permanent infrastructure. In dynamic networks like MANETs, routing procedures enhance the overall appearance of the network by ensuring Quality of Service (QoS). A routing protocol may use a composite routing measure to determine the best path for transferring data. Optimal location-based routing for MANETs based on energy-awareness is presented in this paper. Optimal routes are selected using this fast algorithm. Particle swarm optimization (PSO) is used instead of location-aided routing to select the normal data transmission route. Last but not least, the network is tested by simulating its packet delivery ratio, its energy level, its delay, and its overhead. © 2024 IEEE.

关键词： Adversarial machine learning

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An Investigation of Hyperparameters Optimization and Feature Reduction Techniques: Predicting Airline Passenger Satisfaction Using Machine Learning Models 26

An Investigation of Hyperparameters Optimization and Feature...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Islam, Farjana Farukuzzaman Faruk, Md. Mahedy Hasan, S.M. Akter, Farzana Srizon, Azmain Yakin Rakib Hossain, Md. Al Mamun, Md. Nazmul Islam, Md Faruk Hossain, Md. Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Electrical & Electronic Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350359015

Aviation companies faced significant losses due to transportation restrictions amid the COVID-19 pandemic. Their inability to provide customer-friendly services resulted in a decrease in customer satisfaction. Machine learning models have become indispensable tools for predicting customer satisfaction and improving the quality of airline services. This study focused on utilizing three distinct feature reduction algorithms to generate three sets of feature collections, which were employed in six different machine learning models along with a hyperparameter tuning method, such as GridSearch, to achieve optimal results. GridSearch systematically evaluated numerous hyperparameter combinations. After applying three distinct algorithms for feature reduction and hyperparameter tuning, a comparative analysis of six different machine learning models was conducted to identify the one that provides the most accurate predictions of passenger satisfaction. The study's outcome indicated that the Random Forest stood out as the most favorable choice for the highest performance score. It achieved an outstanding accuracy score of 97%, as well as impressive precision, recall, and F1-score values of 97%, 97%, and 97%, respectively. This model's exceptional performance surpassed most recent similar studies regarding accuracy and efficiency. © 2023 IEEE.

关键词： Airlines Customer Feature Reduction GridSearch Hyperparameter Machine Learning Satisfaction

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