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An Automatic Brick Grading System Using Convolutional Neural Network: Bangladesh Perspective 1

3rd International Conference on Advanced Computing and Intelligent Technologies, ICACIT 2023

作者： Dhali, Sourav Islam, Md. Hasibul Barmon, Sourav Ghosh, Arjan Department of Computer Science and Engineering Northern University of Business and Technology Khulna Khulna9100 Bangladesh Computer Science and Engineering Discipline Khulna University Khulna9208 Bangladesh Department of Computer Science and Engineering BRAC University Dhaka1212 Bangladesh

ISBN: (数字)9789819719617

ISBN: (纸本)9789819719600

In this work our primary objective is to devise an automated system for grading bricks based on their physical attributes. The conventional brick grading methods heavily rely on manual inspection, a process known for its time-consuming nature and subjectivity. To address this, our research proposes a machine learning-centric approach, leveraging computer vision techniques and deep learning algorithms to accurately and efficiently classify bricks. Our system is designed to extract pertinent features from brick images, focusing on properties such as color, texture, and size. The model is trained using an extensive dataset comprising various brick types. A key innovation of our work is the integration of an auto-detection system based on deep learning networks. Operating with an impressive accuracy of 89.58%, the system excels in image grading. The potential impact of our automated brick grading system extends to the construction industry, offering a time and cost-effective alternative to manual brick sorting. This abstract presents a new development in the industry and puts our work at the forefront of the development of automated brick grading systems. As far as we are aware, no one has ever completed this unique work before. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Grading

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Mal Class: A Deep Learning Approach for Automatic Classification of Malware Images 4

Mal Class: A Deep Learning Approach for Automatic Classifica...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Divya, S. Sree Sastha Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chembarambakkam600123 India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523923

These days, malware evolves and multiplies exponentially through structural changes and camouflage using methods like encryption, obfuscation, polymorphism, and metamorphism. As deep learning has advanced, techniques like convolutional neural networks (CNN) have become powerful instruments for identifying complex patterns in this malicious software. The present study leverages CNN's capacity to detect patterns in malware datasets generated from RGB or images in greyscale and to determine the global structure of code that has been converted into an image. Convolutional Neural Networks (CNN) are a method of deep learning that has recently demonstrated better performance than conventional learning algorithms, particularly in applications like image categorization. Motivated by this result, a CNN-based malware sample categorisation architecture is proposed. After converting binaries of malware to monochrome images, we train a CNN to classify the images. Our method exceeds the latest advances, according to analyses done on the difficult malware classification dataset Malimg, © 2025 IEEE.

关键词： Convolutional neural networks

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Full-model-free Adaptive Graph Deep Deterministic Policy Gradient Model for Multi-terminal Soft Open Point Voltage Control in Distribution Systems

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Journal of Modern Power Systems and Clean Energy 2024年第6期12卷 1893-1904页

作者： Huayi Wu Zhao Xu Minghao Wang Youwei Jia Department of Electrical and Electronic Engineering The Hong Kong Polytechnic UniversityHong KongChina Shenzhen Research Institute The Hong Kong Polytechnic UniversityShenzhenChina Department of Electrical and Computer Engineering University of MacaoMacaoChina Department of Electrical and Electronic Engineering Southern University of Science and TechnologyShenzhenChina

High penetration of renewable energy sources(RESs)induces sharply-fluctuating feeder power,leading to volt-age deviation in active distribution *** prevent voltage violations,multi-terminal soft open points(M-sOPs)have been integrated into the distribution systems to enhance voltage con-trol ***,the M-SOP voltage control recalculated in real time cannot adapt to the rapid fluctuations of photovol-taic(PV)power,fundamentally limiting the voltage controllabili-ty of *** address this issue,a full-model-free adaptive graph deep deterministic policy gradient(FAG-DDPG)model is proposed for M-SOP voltage ***,the attention-based adaptive graph convolutional network(AGCN)is lever-aged to extract the complex correlation features of nodal infor-mation to improve the policy learning ***,the AGCN-based surrogate model is trained to replace the power flow cal-culation to achieve model-free ***,the deep deterministic policy gradient(DDPG)algorithm allows FAG-DDPG model to learn an optimal control strategy of M-SOP by continuous interactions with the AGCN-based surrogate *** tests have been performed on modified IEEE 33-node,123-node,and a real 76-node distribution systems,which demonstrate the effectiveness and generalization ability of the proposed FAG-DDPGmodel.

关键词： Soft open point graph attention graph convolutional network reinforcement learning voltage control distribution system deep deterministic policy gradient

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A 0.75 to 1.5 GHz Reconfigurable Diplexer With Independently Controllable Center Frequency and Insertion Phase

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IEEE Transactions on Circuits and Systems II: Express Briefs 2024年第4期71卷 1924-1928页

作者： Yi, Chengfei Chen, Xiong Chi, Pei-Ling Yang, Tao The School of Electronic Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China The Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu300 Taiwan

A novel reconfigurable diplexer with independently controllable center frequency and insertion phase is proposed in this brief. It simply consists of six coupled resonators and two non-resonating-nodes (NRNs). These six resonators are arranged in two triplet topologies and construct two typical third-order filters, forming the lower and upper channels in the diplexer, respectively. The NRNs are loaded at the receiver (RX) and transmitter (TX) ports. With the utilization of NRNs, the insertion phases of the upper and lower channels in the diplexer can be independently controlled. Optimization technique is used to obtain the coupling matrix of the diplexer, in which the NRN is considered to achieve a prespecified insertion phase. To validate the proposed idea, a microstrip prototype is fabricated and measured. Experiment results show that the center frequencies of lower and upper channels can be tuned from 0.75 GHz to 0.98 GHz, and from 1.23 GHz to 1.5 GHz, respectively. The insertion phases of both channels can be independently tuned from 0 to 120 degrees at each frequency tuning state. © 2023 IEEE.

关键词： Resonators

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Utilizing Customized 3D U-Net Framework for the Classification and Segmentation of Multi-regional Brain Tumors in Volumetric MRI Images 3

Utilizing Customized 3D U-Net Framework for the Classificati...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Titu, Md. Minhaj Taher Mary, Mekhala Mariam Ahamed, Md. Faysal Oishee, Tasfia Mashud Hasan, Md Mahadi International Standard University Department of Computer Science & Engineering Dhaka1212 Bangladesh Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Rajshahi6204 Bangladesh International Standard University Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350388282

Precise diagnosis and treatment planning depend heavily on the segmentation and classification of brain tumors in medical pictures. The diagnostic and therapeutic challenges posed by brain tumors necessitate innovative solutions within the domain of medical imaging. This research endeavors to address this pressing need through the introduction of a pioneering approach to multi-regional tumor classification and segmentation. Utilizing volumetric MRI data, our study introduces a bespoke 3D U-Net framework meticulously tailored to the intricacies of brain tumor analysis. Our framework offers holistic solutions to two foundational objectives: classification and segmentation. In the classification facet, we strive to discriminate between various brain tumor types. Concurrently, our segmentation component aspires to precisely delineate tumor boundaries and partition distinct tumor regions. We used the BraTS 2020 dataset, and dice and Jaccard coefficient functions were employed in the model optimization process. The custom-tailored architecture of 3D U-Net, purpose-built for the nuances of brain tumor imaging, ensures resilient performance while preserving vital spatial information essential for accurate tumor delineation. Leveraging deep learning methodologies and a comprehensive dataset of volumetric MRI scans, our model showcases outstanding accuracy in classifying diverse tumor types and in the precise segmentation of tumor regions with a dice score are 88.1%, 79.4%, and 76.0% for WT dice, TC dice and ET dice. And Jaccard Scores are 79.4% for WT Jaccard, 68.2% for TC Jaccard and 64.3% for ET Jaccard. The proposed framework holds the potential to alleviate subjectivity in diagnostic processes, enhance the precision of treatment planning, and ultimately ameliorate patient outcomes. © 2024 IEEE.

关键词： Tumors

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On the Design of Cancer Tumor Type Prediction using Machine Learning Techniques for Efficient Diagnosis 2

On the Design of Cancer Tumor Type Prediction using Machine ...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Thakkar, Hiren Kumar Kumar, Yogesh Kathirisetty, Nikhila School of Technology Pandit Deendayal Energy University Department of Computer Science and Engineering Gujarat 382007 India Vardhaman College of Engineering Department of Computer Science and Data Science Hyderabad Shamshabad India

ISBN: (纸本)9798350370188

In the recent past, cancer is designated as the life threatening disease causing significant deaths across the globe. Countries are putting efforts for early stage cancer detection, and mortality prediction. Machine Learning (ML) models are proven to efficiently find the correlation among the features for robust predictions over statistical approaches. In this paper, we used kaggle dataset that includes 569 cancer cells and around 30 features cancer cells' classification. The proposed study employs ML models (e.g., binary classifiers) for effective predictive analytics. The evaluation results indicate that Random Forest achieves better results with Area Under Curve (AUC) of 0.99 followed by Decision tree with AUC be 0.92. However, the Logistic Regression (LR) and Support Vector Machine (SVM) fails to capture the generalized behavior of the cancer data for predictive analysis. © 2024 IEEE.

关键词： Classification (of information)

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Enhancing structural health monitoring with AI-ML algorithms: a focus on crack detection and prediction

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Asian Journal of Civil engineering 2025年第5期26卷 1907-1918页

作者： Bader, Ahmad Shtayat, Amir Al-Mistarehi, Bara’ Communication and Computer Department Faculty of Engineering Jadara University Irbid Jordan Department of City Planning and Design Jordan University of Science and Technology Irbid 22110 Jordan Department of Civil Engineering Jordan University of Science and Technology Irbid Jordan

SHM is a very important process in terms of the safety and durability of infrastructure. Traditional SHM often faces problems detecting minor structural defects and handling large datasets. Therefore, certain more advanced approaches are called for. The paper discussed the applications of AI and ML algorithms, such as CatBoost and the African Vultures Optimization Algorithm, for such challenges. The research is based on a unique dataset of 8,541 rows and diverse features, developing a predictive framework that enhances crack detection and forecast capabilities. The approach mainly deals with heterogeneous data using the CatBoost algorithm, given its capability for high-accuracy predictions, while AVOA optimizes feature selection, reduces the computational cost, and guarantees no loss in model performance. This methodology has resulted in a significant enhancement of the prediction accuracy, which states the importance of AI-ML integration in SHM. The key results demonstrate the effectiveness of the model in detecting structural anomalies and crack propagation to enable proactive maintenance strategies. This study’s contributions have gone toward advancing SHM with scalable and efficient AI-ML frameworks, enabling real-time monitoring for better infrastructure management. Such development might have a transforming potential to cut down on maintenance costs and enhance operational safety, thus further encouraging sustainable infrastructure systems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： African vultures optimization algorithm CatBoost Crack Detection Feature selection Machine learning Structural health monitoring

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An Efficient Security Solution for IoT and Cloud Security Using Lattice-Based Cryptography

An Efficient Security Solution for IoT and Cloud Security Us...

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2023 International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2023

作者： Bagla, Piyush Sharma, Ravi Mishra, Amit Kumar Tripathi, Neha Dumka, Ankur Pandey, Neeraj Kumar National Institute of Technology Department of Computer Science & Engineering Punjab Jalandhar India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Department of Computer Science & Engineering Dehradun India Women Institute of Technology Department of Computer Science & Engineering Dehradun India Dehradun India

ISBN: (纸本)9798350314878

This study introduces lattice-based cryptography to enhance security in IoT and cloud systems. It focuses on two fundamental challenges in lattice-based encryption. The paper compares the advantages and disadvantages of lattice-based encryption with traditional RSA and elliptic curve cryptography. Furthermore, it presents a novel approach called 'Lattice-based secure data aggregation,' specifically designed for IoT and cloud systems, ensuring confidentiality and integrity during data aggregation. The paper covers various aspects, such as data encryption, aggregation, decryption, and implementation on resource-constrained devices. Performance and security evaluations compare the proposed algorithm with other data aggregation techniques, demonstrating its effectiveness against attacks, computational efficiency, and low memory requirements. The paper also addresses the approach's limitations and proposes potential areas for future research. Overall, the proposed lattice-based cryptography offers a promising solution for privacy and security-sensitive IoT and cloud systems, making it a viable option for widespread adoption. © 2023 IEEE.

关键词： Computational efficiency

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Advanced Semantic Communication Techniques for IoT Using Disentangled Information Bottleneck

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IEEE Internet of Things Journal 2025年第12期12卷 20122-20137页

作者： Xie, Wenwu Xiong, Ming Yang, Liang Wang, Ji Li, Xingwang Yang, Zhihe Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Central China Normal University College of Physical Science and Technology Department of Electronics and Information Engineering Wuhan430079 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454003 China

This article explores the impact of source data compression on the performance of task execution at the receiver side of a communication system, and investigates the interference and impact of channel environment variations on semantic coding features. In order to further optimize the performance of the semantic communication model, a semantic communication framework (DIB-DeepSC) based on disentangled information bottleneck is proposed, which improves the inference accuracy of the model by separating and decoupling irrelevant information in the source data, thus compressing valid information related to downstream task execution to a greater extent, reduced communication overhead. Meanwhile, the influence of the Lagrange multiplier β in the classical information bottleneck (IB) framework is eliminated, which avoids the need to manually optimize β several times in the semantic communication model. And the dynamic coding method (DIB-DE) is further designed based on adaptive weights, which can dynamically adjust the coding features according to the channel conditions and enhance the robustness of the model. Numerous experiments show that the proposed DIB-DeepSC framework combined with the DIB-DE dynamic encoding communication scheme possesses better semantic extraction and task inference performance relative to the benchmark methods. This scheme is expected to realize more efficient and reliable semantic transmission in practical communication systems and provides new ideas for developing practical semantic communication systems. © 2014 IEEE.

关键词： Channel coding

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Predicting software vulnerability based on software metrics: a deep learning approach

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Iran Journal of computer science 2024年第4期7卷 801-812页

作者： Agbenyegah, Francis Kwadzo Asante, Micheal Chen, Jinfu Akpaku, Ernest Department of Computer Science Kwame Nkrumah University of Science and Technology Kumasi Ghana School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Department of Information Technology Faculty of Computing and Information Systems Ghana Communication Technology University Accra Ghana

The security of IT systems is the topmost priority of software developers. Software vulnerabilities undermine the security of computer systems. Lately, there have been a lot of reported issues of software vulnerabilities recorded in individuals and corporate systems. Software metrics-based techniques have been used and found to be effective due to low false positives and false negatives. However, there are software metrics that have not been investigated and could have an impact on the performance of the predictive model. In this study, we developed an ensemble deep learning algorithm made of LSTM, CNN, and MLP models with the non-investigated metrics as a feature to learn the latent representation of code metrics to predict anomalies in source code effectively. We trained the proposed model on the SySeVR datasets and compared the model's performance against five deep learning algorithms and four state-of-the-art SVPs. The proposed model performs better than the other deep learning with a classification accuracy of 96.17%, and a precision of 97.72%. The model's classification accuracy is 4.98% higher than that of the five deep learning models and 1.71% lower in precision than the state-of-the-art SVP. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Deep learning Machine learning Software metrics Software security Software vulnerabilities

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