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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Akter, Tahmina Mahmud, Tanjim Barua, Tikle Naher, Sultana Rokeya Hossain, Mohammad Shahadat Andersson, Karl Port City International University Dept. of Computer Science and Engineering Chittagong Bangladesh Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati4500 Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh University of Chittagong Dept. of Computer Science and Engineering Chittagong4331 Bangladesh Luleå University of Technology Cybersecurity Laboratory Luleå97187 Sweden

ISBN: (纸本)9798350370249

Recognition of Bengali sign language characters is crucial for facilitating communication for the deaf and hard-of-hearing population in Bengali-speaking regions, which encompass approximately 430 million people worldwide. Despite the significant number of individuals requiring this support, research on Bengali sign language character recognition remains underdeveloped. This article presents a novel approach to categorize Bengali sign language characters using the Ishara-Lipi dataset, based on convolutional neural networks (CNNs) and pretrained models. We evaluated our approach using metrics such as accuracy, precision, recall, F1-score, and confusion matrices. Our findings indicate that the CNN model achieved the highest performance with an accuracy of 98%, followed by VGG19 with 94% and ResNet variants achieving around 88%. The proposed model demonstrates robust and efficient classification capabilities, significantly bridging the gap in existing literature. This study holds substantial promise for enhancing assistive technology, thereby improving social inclusion and quality of life for Bengalispeaking deaf and hard-of-hearing individuals. © 2024 IEEE.

关键词： Bengali sign language CNNs Deaf and hard-of-hearing community Image classification

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Analysis of Billing Inducement Methods in Smart-phone Games

Computer Software

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computer Software 2024年第1期41卷 75-81页

作者： Asou, Naoki Nishiura, Kinari Sasakura, Mariko Monden, Akito Dept. of Information Technology Faculty of Engineering Okayama University Japan Graduate School of Natural Science and Technology Okayama University Japan

With the growth of the smartphone game market, many cases have been reported in which children have purchased charged items in games without restraint and have been charged large sums of money. In addition, there are users called"heavy payers" or"wasteful payers," who pay much more than the normal amount of money, and there are also reports of cases in which these users are disturbed in their daily lives. In this study, we actually play several representative smartphone games and analyze how developers induce users to pay for their games. As a result of extracting and tabulating the billing elements for each game, we found that many games employ a rich gacha system that makes consumers dependent on the game, similar to gambling, and that they also employ a number of game management strategies to make the amount charged look profitable and to stimulate user motivation to pay. © 2024 Japan Society for Software science and technology. All rights reserved.

关键词： Smartphones

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Quantitative Analysis and Forecasting in Finance: Leveraging Wavelet Packet Transform for Enhanced Market Insights

Quantitative Analysis and Forecasting in Finance: Leveraging...

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Libina, M. Kavitha, C. Kabilan, A. Ifet College Of Engineering Dept. Of Information Technology Villupuram India Manakula Vinayagar Institute Of Technology Dept. Of Computer Science Puducherry India

ISBN: (纸本)9798350352689

Finance forecasting is always known for its ever-changing and complex nature in geopolitical markets, requiring advanced analytical tools to provide valuable deep insights for financial advisors, investors, and vital decision-makers. In this study, we delve into the realm of both quantitative analysis and forecasting in finance-related markets, utilizing sophisticated techniques to enhance our understanding of the markets. Our study focuses on the innovative utilization of the Optimized Wavelet Packet Transform (WPT), an advanced tool for processing signals, to uncover complex patterns within financial time series data through quantitative analysis using Plotly's interactive line charts. It bridges the gap between quantitative analysis and forecasting. It also reaches an accuracy rate of 99.5%, which is considered the best among the other prediction models. These insights help stakeholders and advisors navigate the ever-evolving landscape of financial markets and make decisions wisely. © 2024 IEEE.

关键词： Forecasting

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Detecting adversarial examples using constraint-based learning

Detecting adversarial examples using constraint-based learni...

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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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Detection of Chronic Stress Based on Electroencephalography Responses Induced by the Stroop Color Word Test 16

Detection of Chronic Stress Based on Electroencephalography ...

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16th Biomedical engineering International Conference, BMEiCON 2024

作者： Nugroho, Totok Yoshimura, Natsue Tejada, Laura Alejandra Martinez Tokyo Institute of Technology Dept. Information and Communications Engineering Tokyo Japan Tokyo Institute of Technology Dept. Computer Science Tokyo Japan

ISBN: (纸本)9798331505431

Stress significantly affects the learning, academic performance, and health of university students. Traditional detection methods often rely on subjective self-reports, that can be stigmatized. In this study, we developed an electroencephalography (EEG)-based system to offer a more objective approach to chronic stress assessment. Fourteen participants (aged 24-35) were evaluated using the Perceived Stress Scale (PSS) and the Trier Inventory for Chronic Stress (TICS), with EEG data collected during resting and cognitive tasks (i.e., the Stroop color word test). The study aimed to identify the most effective brain regions and EEG features for chronic stress detection. Data analysis involved feature extraction, normalization, classification, and regression to build and assess detection models. The results indicated no significant differences among resting state, cognitive task, and perturbation signals in the classification of chronic stress levels. Resting-state signals were more effective, with logistic regression, support vector machine classifier (i.e., SVC), and random forest classifiers achieving accuracies of 93.35%, 93.26%, and 94.69%, respectively. Furthermore, the EEG-based regression models estimated PSS and TICS scores with high accuracy, with adjusted R2 values exceeding 0.90, particularly for PSS. Contributive EEG features for chronic stress detection included gamma in the frontal, central, and parietal regions, as well as beta in the frontal and parietal regions. Limitations related to the sample size and circadian rhythm control were acknowledged, suggesting the need for future research with larger samples and varied recording schedules. © 2024 IEEE.

关键词： Electroencephalography

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Electricity Generation From Food Waste Using IOT 3

Electricity Generation From Food Waste Using IOT

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Arunkumar, M. Agalya, S. Bhuvanaeshwari, M. IFET College of Engineering Dept. of Information Technology Villupuram India Surya Group of Institutions Dept. of Computer Science and Engineering Villupuram India

ISBN: (纸本)9798350372120

One inventive solution is to turn food waste into power using the Internet of Things (IoT) to meet the growing need for sustainable energy sources. It turns thermal energy from trash into electricity by utilizing thermoelectric devices and the Seebeck effect. Real-time monitoring and control are made possible with IoT integration. By improving the extraction of energy from liquid or semi-solid waste, this technique reduces waste and promotes a circular energy economy. The system makes use of hardware components such as transformers and voltage regulators. The outcomes of experiments demonstrate its efficacy and possible role in renewable energy. This creative idea, which provides a sustainable route ahead for the production of power from food waste, highlights the importance of IoT in improving waste-to-energy systems. © 2024 IEEE.

关键词： Internet of things

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Efficiency Redefined: The Document Scanner App for Precise Entity Identification with Customized NER 3

Efficiency Redefined: The Document Scanner App for Precise E...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Javid, Sajid Saim, Mohammad Islam, Saiful Indraprastha Institute of Information Technology Delhi Dept. of Computer Science & Engineering Delhi India University of Cincinnati Dept. of Computer Science & Engineering CincinnatiOH United States Zakir Husain College of Engineering & Technology Aligarh Muslim University Dept. of Computer Engineering Aligarh India

ISBN: (纸本)9798331518592

In the era of rapid advancements in technology, the efficient digitization of paper-based documents remains a crucial challenge across various domains. The traditional approach to document scanning often struggles with accurate extraction of critical information from unstructured or diverse document formats. This paper presents the development of a web-based document scanner application that leverages Named Entity Recognition (NER) to extract entities from scanned business cards efficiently. The study aims to address the challenge of digitizing and organizing physical documents, with a focus on precise entity extraction. The methodology involves creating a custom dataset of scanned business cards, preprocessing the data, and training an NER model using machine learning techniques. The application is built using the Flask framework and integrates OpenCV for text extraction and SpaCy for NER. The key findings include the model's high F1 score in identifying entities such as names, organizations, designations, emails, and contact details. The application streamlines document digitization by automatically extracting and tagging relevant metadata, minimizing the need for manual data entry. This research contributes to enhancing document management and demonstrates broader applicability for other document types apart from business cards. The implications highlight improvements in workflow automation, data accuracy, and the potential for integrating with larger document management systems. © 2024 IEEE.

关键词： Optical character recognition

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An Overview on IoT in Electric Vehicles: Efficiency and Sustainability 2

An Overview on IoT in Electric Vehicles: Efficiency and Sust...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Gupta, Shashank Sounkhla, Indu Dubey, Ranjeet Kumar Yadav, Mithilesh Kumar Singh Yadav, Sandeep NIT Dept. of Computer Science and Engineering Jalandhar India KIPM Dept. of Computer Science and Engineering Gorakhpur India Buddha Institute of Technology Dept. of Computer Science and Engineering Gorakhpur India

ISBN: (纸本)9798350368109

Internal combustion engine (ICE) vehicles are very polluting and release high nitrogen oxides, carcinogens, and soot into the environment. ICE vehicles are the best choice in this era, but replacing ICE vehicles with electric vehicles (EVs) and automated vehicle control is a potential transition process. The most important turning point in the history of the automobile occurred with the entry of EVs into the vehicle market. The rise of the Internet of Things (IoT) paradigm in electric vehicles (EVs) has changed the way we look at large-scale transportation by making it intelligent and more sustainable. Recently, with the advent of connectivity through IoT platforms, IoT in EVs has improved performance and reduced environmental impact: from any on-off switch and adoption of IoT through our smartphones placed on the charging base. We can implement smart charging solutions for better energy consumption management, reduction of grid load, and battery life enhancement. IoT allows electric vehicles to communicate directly with charging stations and even the power grid, providing a better way to create balanced transportation networks. But implementing IoT in electric vehicles also raises some issues, including data security and user privacy. With all of these issues, there is huge potential in IoT usage with electric vehicles to help produce cleaner transportation and create a more sustainable future. © 2024 IEEE.

关键词： Electric Vehicles Internal Combustion engine Internet of Things

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Enhancing Network Intrusion Detection in a Limited Labeled Data Scenario: An Active Learning Approach

Enhancing Network Intrusion Detection in a Limited Labeled D...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Hossain, Md Sharafat Hossain, Md Al Amin Sikdar, Md Shahin Islam, Md Saiful Bangladesh University of Engineering and Technology Institute of Information and Communication Technology Dhaka Bangladesh Selcuk University Dept. of Information Technology Engineering Konya Turkey Prime University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331529765

The Internet was originally developed to facilitate easier communication through digital devices. It has made our lives easier by providing means for communication and delivering digital resources anywhere and at any time in the world. However, the internet also exposes users to the risk of criminals exploiting various vulnerabilities in the internet system, launching sophisticated attacks to gain valuable information and hamper users' lives. Henceforth, an effective intrusion detection system (IDS) is imperative for safeguarding the data and resources of users in the cyber world. This research aims to enhance semi-supervised IDS models by leveraging limited labeled data and unlabeled data and exploring innovative approaches to improve adaptability and accuracy in detecting known and emerging threats, particularly in scenarios with limited labeled data available. Our study uses the UNSW-NB15 dataset, and the results show that our suggested active learning-based semi-supervised model, which was created with 2000 labeled data, is better than the supervised model that uses the same number of labeled data. The proposed model achieved an accuracy score of 97.67% while existing studies achieved an accuracy score of 94.61%. © 2024 IEEE.

关键词： Network intrusion

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Exploring Taiwan Monkeypox Virus (MPOX/MPXV) Clade IIb by Establishing Dataset and Clustering by using Sequence and Image Similarity Comparisons 24

Exploring Taiwan Monkeypox Virus (MPOX/MPXV) Clade IIb by Es...

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7th International Conference on Computational Intelligence and Intelligent Systems, CIIS 2024

作者： Wang, Tzung-Wei Li, Ming-Kuang Lou, Der-Chyuan Lin, Chun-Yuan Dept. of Electronics Engineering Chang Gung University Taoyuan Taiwan Dept. of Computer Science and Information Engineering Asia University Taiwan Taichung Taiwan Dept. of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan

ISBN: (纸本)9798400717437

Based on the MPXV Clade IIb cases in Taiwan, 175 segments were identified from OPG001 to OPG210. The first focus of this paper is on sequence analysis of the MPXV cases in Taiwan, detailing the procedures, including semi-automated segmentation and dataset creation from a large number of MPXV Clade IIb nucleotide sequences found in the NCBI and GISAID databases. The second feature highlights the pioneering establishment of an MPXV Clade IIb RNA 2nd structure image dataset in Taiwan, as such a dataset does not yet exist globally. The third characteristic involves the use of unsupervised learning methods for the rapid clustering of MPXV Clade IIb RNA 2nd structure images. This approach allows not only for the quick detection and clustering of unknown MPXV Clade IIb cases but also for the rapid identification of new MPXV mutations and structural changes. Compared to traditional sequence analysis, this new hybrid systematic method significantly enhances overall efficiency, improving processing speed by at least 24 hours when handling the alignment of 10 MPXV sequences from Taiwan. Future plans include using this prototype analytical model and method to accelerate the analysis of critical lethal factors in MPXV Clade IIb, such as D14L, ultimately helping biologists reduce the time required for analysis and antiviral drug development. © 2024 Copyright held by the owner/author(s).

关键词： Nucleotides

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