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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Liawatimena, Suryadiputra Gunawan, Devina Bina Nusantara University Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Deparment BINUS Graduate Program Master of Computer Science Jakarta11480 Indonesia Bina Nusantara University Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Modern retail businesses face a significant challenge with the inefficiency of manually changing price labels on shelves. This manual process not only consumes valuable time and resources but also increases the likelihood of errors, leading to potential inaccuracies in pricing and a less streamlined shopping experience for customers. The proposed solution is the implementation of an Electronic Shelf Label (ESL) system that automatically displays the prices of goods on retail business shelves. This system connects a website (front end and back end) to e-paper via a Wi-Fi network and microcontroller, allowing retail business owners to update prices more easily. Additionally, buyers can search for desired items through the website. The results are the time to send data from the website to the e-paper, namely, to know the performance of the e-paper used. The average time required from 10 attempts to send data from the website to the e-paper is 20.935 seconds. Since the data is connected to the server and will be updated automatically, using this system will be more efficient than manually changing the price label, although it takes time to transfer data from the website to the e-paper. © 2024 IEEE.

关键词： Sales

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Comparison of Machine Learning Algorithms for Detection of Stuttering in Speech

Comparison of Machine Learning Algorithms for Detection of S...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Gudlavalleti, Sarvagna Devi, P. Sunitha Lakka, Ramyasri Kuchanpally, Rithika Dudekula, Sai Sonali Department of Computer Science and Engineering G Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad India Department of Electronics and Telecommunication Engineering G Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad India

ISBN: (纸本)9783031513374

Stuttering is a speech disorder characterized by the repetition of sounds, syllables, or words and the prolongation of sounds. An individual who stutters exactly knows what he or she would like to say but has trouble producing a normal flow of speech. This project aims to use machine learning algorithms to detect stuttering behavior in Telugu language speech samples. Stuttering is a speech disorder that affects the flow of speech, making it difficult to communicate with others. The study will collect voice samples from individuals reading the same Telugu script and generate 8- and 16-kHz samples in .wav files. The data will be annotated and coded on a 0–8 scale and will be generated as a dataset. Machine learning algorithms will be used to build a model for detecting stuttering patterns in the audio files. Finally, the model will be integrated with a user interface. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： User interfaces

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Children Hematopoietic Stem Cell Transplant Survival Status Prediction using Machine Learning

Children Hematopoietic Stem Cell Transplant Survival Status ...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Islam Rifat, Ariful Hossain, Mehrab Nahid, Nafiz Akter, Sharmin Islam, Ashraful American International University Department of Computer Science and Engineering Dhaka Bangladesh Sylhet Engineering College Department of Computer Science and Engineering Sylhet Bangladesh Noakhali Science and Technology University Computer Science and Telecommunication Engineering Noakhali Bangladesh University of Regina Department of Computer Science Regina Canada

ISBN: (纸本)9798350303452

A surgical treatment known as a bone marrow transplant (BMT) or hematopoietic stem cell transplant (HSCT) successfully treats bone marrow diseases. However, the treatment has a number of risk factors that may reduce long-term survival. This research aimed to predict the survival of children receiving Hematopoietic Stem Cell Transplantation (HSCT) or bone marrow transplant (BMT) using different machine learning classifier models, including Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), AdaBoost (AdB), Gradient Boosting Classifier (GBC), and XGBoost (XGB), using a publicly available dataset. The study involved preprocessing the data and balancing it using SMOTEENN to address the class imbalance issue. Feature selection was also performed using chi-square and correlation methods, resulting in the use of 19 out of 39 features. The data was then split into a 70-30 train-test ratio and trained using the aforementioned machine learning classifier models. The results showed that the decision tree classifier had the highest accuracy rate of 96.77%. These findings suggest that machine learning models can be utilized to predict the survival of children undergoing HSCT, with the decision tree classifier being the most accurate. So, this research provides a foundation for future studies that aim to improve the accuracy of survival predictions for children undergoing HSCT. Additionally, the findings may be utilized to aid in treatment decision-making and counseling for patients and their families. © 2023 IEEE.

关键词： Decision trees

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Disrupting the Ballot Box: Blockchain as a Catalyst for Innovation in Electoral Processes

Disrupting the Ballot Box: Blockchain as a Catalyst for Inno...

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

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

ISBN: (纸本)9798350350289

The blockchain is a revolutionary computing framework that uses a unique data structure to create a record-keeping system that is accessible, communal, and decentralized. It has a wide range of applications in several disciplines. The worldwide implementation of electronic voting systems in public elections is increasing, propelled by the many benefits they provide. These systems, known for facilitating remote voting and accelerating the vote-tabulation process, are becoming increasingly popular due to their ability to improve confidentiality and reduce voting prejudices. By incorporating blockchain technology, the voting process is strengthened since it utilises an unchangeable storing mechanism for votes, hence reducing the possibility of manipulation and guaranteeing the trustworthiness of elections. This study offers a thorough analysis of the present state of blockchain-based electronic voting systems, investigating their deployment by different countries and organizations, as well as recommendations put out in academic research. In addition, the study examines the difficulties these systems face and proposes potential directions for future research to enhance the dependability of blockchain e-voting systems. © 2024 IEEE.

关键词： Blockchain

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Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

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Blockchain: A secure solution for identifying counterfeits and improving supply chain reliability

Blockchain: A secure solution for identifying counterfeits a...

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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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