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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Farhan, Maruf Sulaiman, Rejwan Bin Nur, Abdullah Hafez Northumbria University Department of Computer Science and Engineering Northumbria United Kingdom Northumbria University Department of Computer Science & Technology Northumbria United Kingdom International Islamic University Chittagong Department of Electronic and Telecommunication Engineering Chittagong Bangladesh

ISBN: (纸本)9798350350289

Counterfeit goods provide a substantial risk to both consumers and corporations. This study presents a system that employs blockchain technology to address the issue of counterfeiting by validating the authenticity of products using QR codes. The framework enables manufacturers to register products and monitor their ownership across the whole supply chain. Subsequently, consumers have the ability to scan QR codes of products in order to authenticate their authenticity and obtain information regarding the product's past. The suggested system has numerous benefits, such as: Enhanced security: Blockchain technology guarantees the integrity of data storage and transaction records, making them resistant to tampering. Enhanced transparency: Consumers are able to obtain comprehensive information regarding the product's trajectory, promoting trust and assurance. Cost reduction: The solution obviates the necessity for intermediaries and manual verification procedures, resulting in financial savings. The study also recognises constraints such as the requirement for specialised knowledge and possible difficulties in expanding the solution. Furthermore, the efficacy of the system hinges on the extensive acceptance and understanding of blockchain technology. In summary, this article introduces a potential framework for utilising blockchain technology to tackle the increasing problem of counterfeit items. Subsequent investigations should prioritise enhancing the system's scalability, security, and user acceptance to optimise its overall impact © 2024 IEEE.

关键词： Smart contract

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Development of an Application for Detecting Lung Cancer Using Machine Learning

Development of an Application for Detecting Lung Cancer Usin...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Marathe, Abha Makadi, Sahil Meshram, Pratik Mahulkar, Amey Maurya, Ayush Mhaske, Krishna Vishwakarma Institute of Technology Department of Electronics and Telecommunication Engineering Pune India Vishwakarma Institute of Technology Department of Computer Science Pune India

ISBN: (纸本)9798331542344

Lung cancer continues to be a major contributor to cancer-related deaths globally, making it necessary to bring improvements in reliable and accurate detection methods. This study discusses lung cancer detection which uses an open source dataset about patient and 4 ML algorithms-Random Forest (RF), Support Vector Machines (SVM), Decision Tree (DT), and Logistic Regression (LR). To reduce the bias data shuffling was performed initially to randomize the order of tuples. Categorical variables changed to numerical variables with use of factorization for accurate algorithmic processing. Feature selection techniques like Mean Decrease in Impurity (MDI), Recursive Feature Elimination (RFE), and Random Forest Mean Decrease (RFMD) were used to reduce computation load without compromising performance. The study deeply evaluates model performance on the basis of sensitivity, specificity, accuracy, and detection rate. Among the algorithms, RF demonstrated superior accuracy when combined with the selected features. Visualizations using importance scores and confusion matrices provided deeper insights into the models, which aids in interpreting the findings. Finally, an app was developed with selected 8 features and RF to predict the lung cancer. This research demonstrates the vital role of ML in lung cancer diagnosis and its potential of early detection and treatment. The findings of research contributed in advancing the healthcare and addresses the global challenges of lung cancer. © 2024 IEEE.

关键词： Lung cancer

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Robust Graph Autoencoder-Based Detection of False Data Injection Attacks Against Data Poisoning in Smart Grids

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1287-1301页

作者： Takiddin, Abdulrahman Ismail, Muhammad Atat, Rachad Davis, Katherine R. Serpedin, Erchin Department of Electrical and Computer Engineering Texas A and M University College StationTX77843 United States Department of Computer Science Tennessee Tech University CookevilleTN38505 United States Department of Electrical and Computer Engineering Program Texas A and M University Qatar Doha23874 Qatar

Machine learning-based detection of false data injection attacks (FDIAs) in smart grids relies on labeled measurement data for training and testing. The majority of existing detectors are developed assuming that the adopted datasets for training have correct labeling information. However, such an assumption is not always valid as training data might include measurement samples that are incorrectly labeled as benign, namely, adversarial data poisoning samples, which have not been detected before. Neglecting such an aspect makes detectors susceptible to data poisoning. Our investigations revealed that detection rates (DRs) of existing detectors significantly deteriorate by up to 9-29% when subject to data poisoning in generalized and topology-specific settings. Thus, we propose a generalized graph neural network-based anomaly detector that is robust against FDIAs and data poisoning. It requires only benign datasets for training and employs an autoencoder with Chebyshev graph convolutional recurrent layers with attention mechanism to capture the spatial and temporal correlations within measurement data. The proposed convolutional recurrent graph autoencoder model is trained and tested on various topologies (from 14, 39, and 118-bus systems). Due to such factors, it yields stable generalized detection performance that is degraded by only 1.6-3.7% in DR against high levels of data poisoning and unseen FDIAs in unobserved topologies. Impact Statement-Artificial Intelligence (AI) systems are used in smart grids to detect cyberattacks. They can automatically detect malicious actions carried out bymalicious entities that falsifymeasurement data within power grids. Themajority of such systems are data-driven and rely on labeled data for model training and testing. However, datasets are not always correctly labeled since malicious entities might be carrying out cyberattacks without being detected, which leads to training on mislabeled datasets. Such actions might degrade the d

关键词： Cybersecurity

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Shallow Tuned DenseNet: A Lightweight Convolutional Neural Network Approach for Enhanced Skin Lesion Recognition 2

Shallow Tuned DenseNet: A Lightweight Convolutional Neural N...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Ahmmed, Tomal Tahsin Faruk, Md. Farukuzzarnan Srizon, Azmain Yakin Mahedy Hasan, S.M. Khansur, Md. Nasif Efat, Anwar Hossain Hossain, Md. Rakib Al Mamun, 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

ISBN: (纸本)9798331517984

Skin lesions are irregularities on the skin, often harmless, though some may progress to skin cancer. Detecting them early is crucial for successful treatment and prevention of cancer development. Traditional detection methods involve significant expenses and testing. This study introduces an auto-mated system for classifying skin lesions using a Shallow Tuned DenseNet (STDN), a compact Convolutional Neural Network (CNN) model. We employ Transfer Learning (TL) by fine-tuning a pretrained DenseNet-169 model with additional lightweight layers, allowing the STDN to adapt to the PH2 dataset's specific characteristics while remaining efficient. Initially, we utilize data augmentation techniques to address class imbalances within the dataset, improving model performance by reducing bias towards the majority class. The evaluation demonstrates a promising hundred percent accuracy with perfect F1-score and specificity. We attribute these results to effective transfer learning utilization, enhanced tuning, and partially to data augmentation, which mitigates overfitting and biases, facilitating robust feature extraction. Furthermore, we employ Grad-CAM (Gradient-weighted Class Activation Mapping) to visualize and interpret the model's predictions, enhancing trust and comprehension. The proposed STDN model makes a significant contribution by achieving high accuracy with a lightweight design, offering a potentially cost-effective and efficient solution for automated skin lesion detection, thereby aiding in early cancer prevention. © 2024 IEEE.

关键词： Convolutional neural networks

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Design and Analysis of a UWB Slotted Vivaldi Antenna for Microwave Imaging Applications

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IEIE Transactions on Smart Processing and Computing 2023年第4期12卷 350-357页

作者： Paul, Liton Chandra Majumder, Akash Rani, Tithi Hossain, Md. Najmul Rahim, Md. Abdur Shin, Jungpil Yun, Keun Soo Department of Electrical Electronic and Communication Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh Department of Electronics & Telecommunication Engineering Faculty of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Department of Computer Science and Engineering Faculty of Engineering and Technology Pabna University of Science and Technology Pabna6600 Bangladesh School of Computer Science and Engineering The University of Aizu / Fukushima Aizuwakamatsu965-8580 Japan School of Computer and Information Technology Ulsan College / Dong-gu Ulsan44610 Korea Republic of

An ultra-wideband (UWB) slotted compact Vivaldi antenna with a microstrip line feed was evaluated for microwave imaging (MI) applications. The recommended FR4 substrate-based Vivaldi antenna is 50×50×1.5 mm3 in size. The proposed compact Vivaldi antenna showed good radiation characteristics and spanned an ultra-wide bandwidth of 10 GHz, ranging from 3.6 to 13.6 GHz, using a microstrip line feed with a radial stub, tapered slots, and resonant cavities. After analyzing the simulation findings, the proposed compact slotted UWB Vivaldi antenna was compared with the basic Vivaldi antenna. The maximum gain and efficiency were 7.35 dB and 82%, respectively. The antenna was simulated in the time domain using the computer simulation technology (CST) microwave studio. The frequency domain solver was also used for cross-validation, and the results were equally good. In conclusion, the proposed compact Vivaldi antenna is a good option for microwave imaging applications. © 2023 The Institute of Electronics and Information Engineers.

关键词： Cavity resonators

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Trade Ease with Machine Learning and AWS

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IEEE Access 2023年 11卷 25893-25905页

作者： Teja, Kamurthi Ravi Liu, Chuan-Ming National Taipei University of Technology International Program of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station before deployment on a cloud machine. The execution of the strategy resulted in a total profit of 42.74526 USD, with a test accuracy score of 87.45% and a training accuracy score of 89.15% over 6047 epochs. The roadmap provides a step-by-step overview of the entire process from strategy development to execution. In addition, this study offers insights into related issues and solutions that can enhance the effectiveness of trading strategies. Overall, this study contributes significantly to the field of cloud-based trading strategies and opens avenues for future research. © 2013 IEEE.

关键词： Deep neural networks

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Design of a High-Efficiency Temporal Engine for Real-Time Spatial Satellite Image Classification Using Augmented Incremental Transfer Learning for Crop Analysis

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SN computer science 2024年第5期5卷 585页

作者： Dhande, Akshay Pramodrao Malik, Rahul Saini, Dipen Garg, Rachit Jha, Sudan Nazeer, Jabeen Ahmad, Sultan Department of Electronics and Telecommunication Engineering Lovely Professional University Punjab Phagwara India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Kathmandu University Dhulikhel Nepal Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India

Satellite image processing is a multidomain task which involves design of image capturing, denoising, segmentation, feature extraction, feature reduction, classification, and post-processing tasks. A wide variety of satellite image processing models are proposed by researchers, and each of them has different data and process requirements. For instance, the image capturing module might obtain images in layered form, while feature extraction module might require data in 2D or 3D forms. Moreover, performance of these models also varies due to changes in internal process parameters and dataset parameters, which limits their accuracy and scalability when applied to real-time scenarios. To reduce the probability of these limitations, a novel high-efficiency temporal engine for real-time satellite image classification using augmented incremental transfer learning is proposed and discussed in this text. The model initially captures real-time satellite data using Google’s Earth Engine and processes it using a transfer learning-based convolutional neural network (CNN) via backscatter coefficient analysis. These coefficients indicate average intensity value of Precision Image (PRI) when evaluated over a distributed target. Due to extraction of backscattering coefficients, the model is capable of representing crop images in VV (vertical transmit, vertical receive), and HV (horizontal transmit vertical receive) modes. Thereby assisting the CNN model to extract a wide variety of features from input satellite image, which classifies these datasets (original, VV, and VH) into different crop categories. The classified images are further processed via an incremental learning layer, which assists in visual identification of affected regions. Due to use of incremental learning and CNN for classification, the proposed TRSAITL model is capable of achieving an average accuracy of 97.8% for crop type and severity of damage detection, with an average PSNR (Peak Signal-to-Noise Ratio) of 29.

关键词： Accuracy Classification Crop Damage Deep learning Earth Incremental Precision Recall Satellite Transfer Type

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An Explainable Deep Learning Framework for Multi-Class Skin Lesion Classification While Resolving Class Imbalance 2

An Explainable Deep Learning Framework for Multi-Class Skin ...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Jahan, Israt Efat, Anwar Hossain Hasan, S. M. Mahedy Faruk, Md. Farukuzzaman Srizon, Azmain Yakin Hossain, Md. Rakib Al Mamun, 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

ISBN: (纸本)9798331517984

Skin lesions are indicative of various skin diseases, including serious conditions like melanoma, which can be life-threatening if not detected early. Automated systems for analyzing dermatoscopic images have shown promise in improving diagnostic accuracy. However, many existing studies rely on pre-trained convolutional neural network (CNN) architectures without customization, leading to potential biases and suboptimal performance. In this study, we propose a customized DenseNet121 architecture tailored for skin lesion classification. We address class imbalance issues through data augmentation and fine-tune the model to extract fine-grained features. Our method surpasses other cutting-edge models in terms of accuracy, precision, recall, specificity, sensitivity, and root mean square (ROC) values. Furthermore, we utilise Gradient-weighted Class Activation Mapping (Grad-CAM) to visually represent the decision-making process of the model. The next study will prioritise the validation of the model using a wide range of datasets and the integration of lesion-related information to enhance its performance. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Study of Ensemble Learning Models for Accurate Solar Irradiance Forecasting 8

A Comparative Study of Ensemble Learning Models for Accurate...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Chaudhari, Tejaram Joshi, Aditya Bharti, Devkumar Chavan, Pratibha Deshmukh, M.K. Bhalgat, Pritam Trinity College of Engineering & Research Department of Electronics & Telecommunication Engineering Pune India Birla Institute of Technology & Science Pilani Department of Electrical & Electronics Engineering Goa India SCTR's Pune Institute of Computer Department of Electronics & Telecommunication Engineering Pune India

ISBN: (纸本)9798331505462

Accurate estimation of solar radiation is very important in energy planning and the integration of renewable energy infrastructures into power grids for most places where the data may be sparse. This paper investigates advanced machine learning technologies specifically, ensemble methods such as XGBoost, LightGBM, CatBoost, NGBoost, AdaBoost, Gradient Boosting, Random Forest, and SGD Boost - to improve the prediction of solar radiation. It shows the performance of the above-mentioned models to forecast solar irradiance from meteorological factors using data from the Hawaii Space Exploration Analog and Simulation (HI-SEAS) weather station. It has been shown that ensemble learning boosts the forecast accuracy considerably, especially for ensemble learners optimized for categorical data and probabilistic predictions. This shows how machine learning can be potential and support the global shift to sustainable energy with reliable forecasts of solar radiation in data-scarce regions and offering valuable insights for optimizing renewable energy deployment. © 2024 IEEE.

关键词： Solar irradiance

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Design & Implementation of Crop Monitoring Using WSN 7

Design & Implementation of Crop Monitoring Using WSN

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Ansari, Nadia Mustaqim Iqbal, Kashan Tariq, Talha Iqbal, Rizwan Dawood University of Engineering & Technology Department of Electronic Engineering Karachi Pakistan National University of Computer & Emerging Sciences Fast Department of Computer Science Karachi Pakistan Dawood University of Engineering & Technology Department of Telecommunication Engineering Karachi Pakistan

ISBN: (纸本)9798331534271

Agricultural cultivation is an essential requirement for the survival and well-being of the human species. Lack of care or incorrect assessment of various growth and productivity-related metrics leads to a significant loss of production. The wireless crop monitoring system needs a portable, user-friendly, accurate, and automatic data generator for various critical metrics. This paper presents the temperature, humidity, soil moisture, and air quality monitoring of soil conditions and atmosphere for efficient crop growth. The parameters listed above will be monitored using Wireless Sensor Networks (WSNs). This research resolves the issue of excessive water supply by assessing soil moisture. Monitoring temperature allows for the estimation of a plant's water requirements. Monitoring air quality allows for the maintenance of healthy air quality. © 2024 IEEE.

关键词： Soil moisture

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