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22nd International Learning and Technology Conference, L and T 2025

作者： Mazhar Ul Haq, Qazi Yao, Leethar Lin, Bo-Yu Yuan Ze University Dept. of IBPI and Computer Science and Engineering Zhongli Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798331532215

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method. © 2025 IEEE.

关键词： Adversarial machine learning

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A review on investigating the role of block-chain in cyber security

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Materials Today: Proceedings 2022年 56卷 3312-3316页

作者： Aggarwal, Bharat Kumar Gupta, Ankur Goyal, Deepak Gupta, Pankaj Bansal, Bijender Barak, Dheer Dhwaj Dept. of Computer Science and Engineering Vaish College of Engineering Haryana Rohtak India

In the issue of cyber security, block-chain, which is best known for crypto currencies, is providing an interesting answer. Block-chain has excellent safeguards against information manipulation. It supports securing access to Internet of Things (IOT) devices. Furthermore, it enables hacked IOT network instruments to be closed and unplugged. Block-chain serves as a distributed ledger system. It is used in conjunction with IOT to enable machine-to-machine interactions. It makes use of a database that contains a collection of transactions. These transactions are double-checked and recorded in a central ledger. On each node, these ledgers have been deployed. The idea of block chain is discussed in conjunction with the Internet of Things in this research study. Several studies in the fields of block-chain and IOT are considered in this paper. The necessity for block-chain and its development have been discussed in terms of its applicability difficulties. In addition, the significance of block chain in IOT-based research for online transaction security is examined. © 2022

关键词： Internet of things

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NeuroNet: An Attention-Driven Lightweight Deep Learning Model for Improved Brain Cancer Diagnosis

NeuroNet: An Attention-Driven Lightweight Deep Learning Mode...

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

作者： Ahmed, Istyak Nabil, Hadiur Rahman Abir, Golam Rabbani Hossain, Tazdik Das, Aritra Mridha, M.F. American International University-Bangladesh Dept. of Computer Science Dhaka Bangladesh Bangladesh University of Business and Technology Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350369106

This paper introduces NeuroNet architecture, a lightweight deep-learning framework designed for brain tumor identification, integrating a spatial attention-driven convolutional neural network (CNN) architecture. NeuroNet aims to enhance the classification accuracy of brain tumors, specifically targeting three types: gliomas, meningiomas, and general brain tumors, using Magnetic resonance imaging (MRI) data. The framework is estimated on the Bangladesh Brain Cancer MRI dataset, which consists of 6,056 MRI images divided into three balanced classes. Unlike traditional machine learning techniques that rely on handcrafted features, NeuroNet's architecture leverages CNN layers with a spatial attention mechanism, enabling the model to focus on critical tumor regions. This approach reduces manual intervention and enhances feature representation. The proposed model performed a high classification accuracy of 99.04%, with a weighted F1-score of 99.02%, outperforming conventional models. These results indicate NeuroNet's potential to assist medical professionals by providing an accurate and reliable diagnostic tool, thus improving brain tumor treatment outcomes. This research underscores the promise of attention mechanisms within deep learning frameworks for advancing cancer diagnosis and medical imaging. © 2024 IEEE.

关键词： Diseases

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A Novel Hybrid Xception-DenseNet121 Model for Acute Lymphoblastic Leukemia Classification 6

A Novel Hybrid Xception-DenseNet121 Model for Acute Lymphobl...

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6th International Conference on Sustainable Technologies for Industry 5.0, STI 2024

作者： Munna, Mohammad Mehedi Hasan Habib, Iqbal Aman, Nakib East West University Dept of Computer Science and Engineering Bangladesh Jahangirnagar University Dept of Statistics and Data Science Bangladesh Pabna University of Science and Technology Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798331531973

This study investigates a new approach to improving diagnosis for ALL using Convolutional Neural Networks. In this work, a hybrid approach was proposed, merging the Xception and DenseNet121 architecture to classify images of blood cells. The approach should try to address critical problems that include limited Labeled data, class imbalance, and variability in image quality that might impede precise detection. The hybrid CNN model itself automatically extracts the critical features, thereby considerably improving the diagnostic accuracy. The model was tested on a dataset of blood smear images that resulted in 99.69% training accuracy and more than 97% validation accuracy. These results emphasize the strength and efficiency of this model in distinguishing ALL-positive samples from ALL-negative ones. The research presented here extends the knowledge of automated diagnostic tools by proposing a more effective and consistent method for early and accurate diagnosis of ALL and provides the foundation for further developments in diagnostics related to leukemia. © 2024 IEEE.

关键词： Acute Lymphoblastic Leukemia (ALL) Blood Cancer Diagnosis Deep Learning in Healthcare DenseNet121 Medical Image Classification Xception

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An Edge AI and Adaptive Embedded System Design for Agricultural Robotics Applications 25

An Edge AI and Adaptive Embedded System Design for Agricultu...

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30th Asia and South Pacific Design Automation Conference, ASP-DAC 2025

作者： Huang, Chun-Hsian Chen, Zhi-Rui Hsu, Huai-Shu Dept. Electrical Engineering National Changhua University of Education Changhua Taiwan Dept. Computer Science and Information Engineering National Taitung University Taitung Taiwan

ISBN: (纸本)9798400706356

This work presents the AgrBot, an agricultural robot designed to intelligently estimate and predict crop pest and disease severity (PDS). The AgrBot incorporates two binarized neural network (BNN) hardware modules for recognizing target crops and estimating their PDS. In a resource-constrained FPGA-based design, these BNN hardware modules can be configured on-demand, showcasing system adaptivity. Furthermore, a multimodal model that integrates crop images, sensor data, and time features is presented for predicting PDS. Employing edge artificial intelligence (AI) through the BNN hardware modules and the multimodal model enables the AgrBot to determine if biological agents are applied to protect crops from pests and diseases, creating a comprehensive agricultural cyber-physical system (CPS). Experimental results demonstrate accuracies of 76.3% for recognizing target crops, 65.3% for estimating PDS, and 67% for predicting PDS. In comparison to existing microprocessor-based design methods, the AgrBot's BNN hardware modules improve frames per second (FPS) by a factor of 790, while the multimodal model reduces processing time by up to 50.9%. © 2025 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Digital storage

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Accurate and Optimized Labelling of Fashion Products Through Attention Based Spiking Neural Networks 10

Accurate and Optimized Labelling of Fashion Products Through...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Vithyatharshana, N. Thrinayani, Yedhoti Kulkarni, Yogeshwar Nitin Singh, Rimjhim Padam Kanchan, Sneha Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering and Computer science Malaysia

ISBN: (纸本)9798350384369

Automation of garment classification has become the backbone of e-commerce, influencing product search, personalization, and customer experience. This paper uses the Fashion MNIST dataset, a rich collection of clothing images to investigate the potential of Spiking Neural Networks (SNNs) for accurate and efficient classification. SNNs are an excellent choice for widespread usage and real-world integration due to their low power consumption and biomimicry. With the intention of revolutionizing product search, personalization, and customer experience, SNNs are made to address style differences, perspectives, and illumination. In order to fully utilize Fashion MNIST, this paper suggests strategic improvements to the SNN architecture. By addressing key challenges and introducing novel optimizations, our research contributes to a more robust and intelligent future for automated fashion product labelling. An SNN model with 93.6% accuracy has been developed as the base model for all the applications for the same. This data-driven approach leads the way to flawless product searches, highly customized recommendations, and a more intelligent fashion technology in future. © 2024 IEEE.

关键词： Deep learning

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House Price Prediction Using Linear and Lasso Regression 3

House Price Prediction Using Linear and Lasso Regression

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Sharma, Mayank Chauhan, Rahul Devliyal, Swati Chythanya, Kanegonda Ravi Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350381931

This research paper aims to predict house prices with more efficiency and accuracy. The research paper involves three machine learning algorithms, "linear regression, lasso regression, and ridge regression,"that are applied to the data set of Bangalore and have various information about different places in Bangalore, like BHK, number of bathrooms, area, etc. The house price prediction is quite a difficult task, as predicting the price of the house involves many factors like area, number of bathrooms, climate condition, age of the property, and many other factors. To make predictions according to all the factors, the author has applied three algorithms, one by one. From these machine learning algorithms, we have developed a model that helps predict the prices of houses with more accuracy and a lower error percentage. This model can be very useful for both buyers and sellers, as it can justify the price of the house for both of them. © 2024 IEEE.

关键词： Houses

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Integration of Q-Learning with Maximal Ratio Combining to Enhance IoT Communications in Dynamic Fading Environments 12

Integration of Q-Learning with Maximal Ratio Combining to En...

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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Sharma, Rashmi Upadhyay, Abhay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Nagpur Campus Pune India

ISBN: (纸本)9798350387315

This paper serves to show how Q-learning can be used in complement with Maximal Ratio Combining to better IoT communication systems under dynamic fading, high-density networks, and severe interference sources. The ML-enhanced MRC demonstrated promising improvements in signal strength and quality, allowing for performance that was either above or at least equal to the regular MRC. The ML-enhanced presented the added benefit of dynamic adaptation, modifying its performance to meet real-life needs by adapting to issues such as increased density and interference. These results support that integrating Q-learning into MRC-based frameworks significantly increases the reliability and efficiency of IoT communication protocols. Future investigations will aim to improve ML algorithms more appropriate for devices with few resources and evaluate their potential with state-of-the-art technology to foster future applications across intelligent IoT networks. These approaches have the potential to support more efficient operations and increased connectivity in IoT systems. © 2024 IEEE.

关键词： Fading MRC Q-Learning Wireless

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Semi-Automated Behavior-Driven Testing for the Web Front-Ends 19

Semi-Automated Behavior-Driven Testing for the Web Front-End...

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19th IEEE International Conference on e-Business engineering, ICEBE 2023

作者： Ma, Shang-Pin Chen, Yu-An Guo, Yi-Jie Su, Yu-Sheng National Taiwan Ocean University Dept. of Computer Science & Engineering Keelung Taiwan National Chung Cheng University Dept. of Computer Science & Information Engineering Chiayi Taiwan

ISBN: (纸本)9798350325553

The required effort and time increase as web applications grow in size and complexity. Regression testing becomes time-consuming and repetitive, and there is a higher chance of human errors and omissions. Therefore, automating tests by recording test scripts and writing test case code (i.e., code for conducting specific test cases) can reduce effort, time, and human errors. However, technical personnel may not always understand and describe user requirements correctly, leading to issues of misaligned requirements. Therefore, integrating Behavior-Driven Development (BDD) into front-end automated testing can help facilitate communication and discussion with users. By collaboratively establishing user stories and scripts for testing, it should be possible to reduce issues of unclear or misaligned requirements. Currently, some project teams combine automated testing with BDD methods, which has been recognized for its effectiveness. However, the manual creation of test cases and the definition of BDD steps increase the workload. Moreover, there still needs to be a systematic workflow and supporting tools for effectively incorporating script recording tools into BDD testing. To address these issues, we propose a systematic process called 'Recorder-based BDD Testing for Front-end' (RBTF), which combines the script recording capabilities of automated testing tools with BDD practices. We also developed a tool called 'JUnit to Cucumber Converter' (JUCC) that facilitates the conversion between BDD specifications and test case codes. © 2023 IEEE.

关键词： Selenium

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Credit Card Fraud Detection Using Machine Learning- A Comprehensive Review 3

Credit Card Fraud Detection Using Machine Learning- A Compre...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Jaswant, T. Venkata Manoj, G. Sai Vamisdhar, V. Aravind, A. Reddy, S. Vasudeva Patni, Jagdish Chandra Bahadure, Nilesh Kumar, Rohit Alliance School of Advanced Computing Alliance University Dept. of Computer Science Engineering Bengaluru India School of Technology Gsfc University Department of Computer Science and Engineering Gujarat India

ISBN: (纸本)9798331518592

Credit card fraud is an omnipresent threat in the digital era that cannot be effectively managed without complex detection and prevention strategies. Machine learning has become instrumental for the industry, principally, due to providing tools to analyze transactional information and identify patterns of fraud. The article dwells on the methods, problems, and prospects of their future use. The algorithms are analyzed according to their potential applicability, scalability, and interpretability for fraud detection. This study includes convolutional neural networks and recurrent neural networks are used for utilization of anomaly exposure detection methods to detect unseen fraud with models. © 2024 IEEE.

关键词： Convolutional neural networks

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