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58th Hawaii International Conference on System Sciences, HICSS 2025

作者： Kobiela, Dariusz Hajdasz, Michal Erezman, Mateusz Nurzyńska, Karolina Zaporowski, Szymon Kurowski, Adam Weichbroth, Pawel Department of Software Engineering Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Multimedia Systems Faculty of Electronics Telecommunication and Informatics Gdańsk University of Technology Gabriela Narutowicza 11/12 Gdańsk80-233 Poland Department of Algorithmics and Software Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9780998133188

Identifying different vehicle types can help manage traffic more efficiently, reduce congestion, and improve public safety. This study aims to create a classification model that can recognize vehicle types based on the sound of passing vehicles. To achieve this, a database of raw audio files containing 1763 samples from two sources was assembled. The time-domain signals were converted to a time-frequency representation using the short-time Fourier transform to generate Mel Spectrograms. Mel-frequency Cepstral Coefficients (MFCCs) were also generated using the discrete cosine transform. In our experiments we compared these approaches. Since the data was imbalanced we applied online augmentation. Based on the literature review, we chose a Convolutional Neural Network (CNN) classifier because it is particularly well suited for analyzing large datasets due to its automatic feature extraction, parameter sharing and sparsity. The results showed that Mel Spectrograms were more effective for audio data preprocessing in this particular use case, achieving the highest accuracy of 0.875 and the highest f1-score of 0.877 compared to MFCCs. © 2025 IEEE computer Society. All rights reserved.

关键词： Image coding

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Deep Convolutional Neural Network Approach for COVID-19 Detection

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computer Systems Science & engineering 2022年第7期42卷 201-211页

作者： Yu Xue Bernard-Marie Onzo Romany F.Mansour Shoubao Su School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjingChina Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharja72511Egypt Jiangsu Key Laboratory of Data Science and Smart Software Jinling Institute of TechnologyNanjing211169China

Coronavirus disease 2019(Covid-19)is a life-threatening infectious disease caused by a newly discovered strain of the *** by the end of 2020,Covid-19 is still not fully understood,but like other similar viruses,the main mode of transmission or spread is believed to be through droplets from coughs and sneezes of infected *** accurate detection of Covid-19 cases poses some questions to scientists and *** two main kinds of tests available for Covid-19 are viral tests,which tells you whether you are currently infected and antibody test,which tells if you had been infected ***-tine Covid-19 test can take up to 2 days to complete;in reducing chances of false negative results,serial testing is *** image processing by means of using Chest X-ray images and Computed Tomography(CT)can help radiologists detect the *** imaging approach can detect certain characteristic changes in the lung associated with *** this paper,a deep learning model or tech-nique based on the Convolutional Neural Network is proposed to improve the accuracy and precisely detect Covid-19 from Chest Xray scans by identifying structural abnormalities in scans or X-ray *** entire model proposed is categorized into three stages:dataset,data pre-processing andﬁnal stage being training and classiﬁcation.

关键词： COVID-19 deep learning convolutional neural network X-ray

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A Logical Evaluation of Predicting Immune System Strength by using Enhanced Deep Learning Strategy

A Logical Evaluation of Predicting Immune System Strength by...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Malathi, K. Ramkumar, M. Ruby, E. D. Kanmani Santhi, G.B. Korde, Vivek Aljawawdeh, Hamzeh J. Saveetha University Department of Cse Chennai India Veltech Rangarajan Dr Sagunthala R & D Institute of Science & Technology Department of Ece Chennai India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of information technology Department of software engineering Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The immune system plays a crucial role in defending the body against diseases, making it vital to develop models that can accurately predict immune system strength. This study presents an Enhanced Deep Learning Strategy (EDLS) designed to predict immune system strength based on immune-related biomarkers such as White Blood Cell (WBC) count and Immunoglobulins. A dataset consisting of 23,654 records with 12 features was collected from clinical sources. The data underwent comprehensive preprocessing, including noise reduction using a Median Filter, feature selection with Random Forest, and data fusion using Kalman Filters. The EDLS model integrates AutoEncoder for dimensionality reduction and Cascaded Long Short-Term Memory (LSTM) for capturing temporal dependencies. The proposed model outperforms nine traditional and advanced machine learning models, including Logistic Regression, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), with an accuracy of 94.72%. Evaluation metrics such as Precision (92.01%), Recall (93.77%), and F1-Score (92.88%) highlight the model's effectiveness in predicting immune system strength. Feature importance analysis reveals that WBC count and Immunoglobulins are the most influential predictors. This study demonstrates the potential of EDLS for enhancing immune strength prediction, which can have significant implications for personalized healthcare and disease prevention. © 2024 IEEE.

关键词： Median filters

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A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification 18

A New Rhythm in A1: Convolutional Neural Networks for Music ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Javed, Huzaifa Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of AI in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of AI within music analysis. © 2023 IEEE.

关键词： Convolution

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The YOLOv8 Edge: Harnessing Custom Datasets for Superior Real-Time Detection 18

The YOLOv8 Edge: Harnessing Custom Datasets for Superior Rea...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ahmed, Tafreed Maaz, Ahmad Mahmood, Danyaal Abideen, Zain Ul Arshad, Usama Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications. © 2023 IEEE.

关键词： Object detection

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HistoDX: Revolutionizing Breast Cancer Diagnosis Through Advanced Imaging Techniques

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IEEE Access 2025年 13卷 94416-94436页

作者： Wishal Arshad Tehreem Masood H. M. Shahzad Hassan A. Ahmed Syed Hamza Ahmed Hafiz Muhammad Tayyab Khushi Faculty of Software Engineering and Information Technology University of Central Punjab Lahore Pakistan Faculty of Computer Science and Information Technology Superior University Lahore Pakistan Department of Software Engineering Superior University Lahore Pakistan Department of Information Systems Monte Ahuja College of Business Cleveland State University Cleveland OH USA

Breast cancer is the second leading cause of mortality among women worldwide, highlighting the need for efficient histopathology-based screening methods for early diagnosis. This study introduces HistoDX, a deep learning framework to classify Invasive Ductal Carcinoma (IDC) using 277,524 histopathology image patches ( $50\times 50$ pixels) from Paul Mooney’s IDC dataset on Kaggle, comprising No Cancer and IDC(+) classes. HistoDX employs a preprocessing pipeline with normalization, data augmentation, and class balancing via oversampling and weighted loss to address the class imbalance. A customized convolutional neural network, built on EfficientNetV2-B3 with additional layers, achieves 97% accuracy and a 0.91 ROC-AUC score on the test set. Validation on BreakHis (97% accuracy) and BACH (90% accuracy) datasets confirm generalizability, though detecting under sampled IDC(+) cases remains challenging. Low training and test losses underscore reliability. HistoDX empowers pathologists by enhancing diagnostic efficiency and minimizing subjectivity through effective class imbalance mitigation. Its robust performance across diverse datasets like BreakHis and BACH suggests readiness for clinical integration. Future research into advanced augmentation techniques, ensemble models, and whole-slide image analysis could further optimize accuracy, sensitivity, and scalability, paving the way for broader adoption in precision oncology.

关键词： Breast cancer Cancer Accuracy Tumors Feature extraction Computational modeling Imaging Histopathology Deep learning Breast

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AdaptiveCloset: Reinforcement Learning in Personalized Clothing Recommendations 18

AdaptiveCloset: Reinforcement Learning in Personalized Cloth...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Majeed, Omar Kashif Ul Abideen, Zain Arshad, Usama Ali, Raja Hashim Habib, Abdullah Mustafa, Rafay BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

The fashion industry, with its myriad choices, often overwhelms consumers. Addressing this, AdaptiveCloset introduces a groundbreaking approach to tailoring clothing suggestions by harnessing the power of reinforcement learning (RL). Unlike conventional AI methodologies in a fashion that merely suggests based on past preferences, our system dynamically ad-justs using realtime user feedback, ensuring that recommendations remain relevant and personalized. Historically, AI's fusion into the fashion realm has witnessed multiple methodologies but conspicuously lacked the RL perspective. Our research stands at this juncture, aiming to redefine online shopping experiences. Through AdaptiveCloset, we envisage a scenario where online shoppers not only receive personalized recommendations but also feel a sense of involvement, thanks to the system's feedback-oriented adaptability. This responsiveness not only augments user engagement but provides actionable intelligence for businesses, bridging the gap between consumer desires and market offerings. In this study, our focus was to ensure the robustness and adaptability of the RL environment, positioning it as a potent tool for enhancing e-commerce interactions, optimizing sales strategies, and fortifying customer loyalty. © 2023 IEEE.

关键词： Reinforcement learning

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Deciphering Faces: Enhancing Emotion Detection with Machine Learning Techniques 18

Deciphering Faces: Enhancing Emotion Detection with Machine ...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Ishaq, Muhammad Haseeb Mustafa, Rafay Arshad, Usama Abideen, Zain Ul Ali, Raja Hashim Habib, Abdullah Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Facial emotion detection holds significant relevance across various domains, from psychology and marketing to education and security. Despite its importance, prevalent techniques often grapple with issues like low precision, susceptibility to lighting changes, obstructions, and distinct facial characteristics. Addressing these challenges, our research embarked on devising a robust and precise facial emotion detector harnessing the potential of machine learning, focusing on convolutional neural networks (CNN). Comprehensive testing revealed that our model surpasses existing state-of-the-art techniques, showcasing superior performance on benchmark datasets. The salience of our research is underscored by its profound implications for myriad real-world applications hinging on accurate facial emotion recognition. We present an enhanced model, distinguished not just by its accuracy but also its robustness, making it apt for diverse scenarios from insightful marketing initiatives and nuanced medical diagnoses to enriched educational experiences. Through this endeavor, we have accentuated the transformative capacity of machine learning in refining and redefining facial emotion detection methodologies. © 2023 IEEE.

关键词： Emotion Recognition

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An Enhanced Genetic Algorithm Framework for Efficient Solutions to Capacitated Vehicle Routing Problems 18

An Enhanced Genetic Algorithm Framework for Efficient Soluti...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Arshad, Usama Ellahie, Hamza Hasan Abideen, Zain Ul Ali, Raja Hashim BCMaterials Basque Center for Materials Applications & Nanostructure Basque Country Leioa Spain Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany

ISBN: (纸本)9798350328172

Capacitated Vehicle Routing Problems (CVRPs), a widely acknowledged NP-hard issue, pertains to the optimal routing of a limited-capacity vehicle fleet to fulfill customer demand, aiming for the least possible travel distance or cost. Despite the presence of numerous heuristic and exact approaches, the combinatorial characteristic of CVRP renders it challenging, especially for large-scale instances. This research provides an in-depth exploration of utilizing Genetic Algorithms (GAs) to address Capacitated Vehicle Routing Problems (CVRPs), a recognized and intricate optimization issue in the realm of logistics and supply chain management. Our paper concentrates on the innovative usage of GAs, a category of stochastic search methodologies inspired by natural selection and genetics, to grapple with CVRP. We put forth a fresh framework grounded in GA that infuses unique crossover and mutation operations tailor-made for CVRP. Our comprehensive computational trials on benchmark datasets suggest that our GA-centric method is proficient in deriving high-standard solutions within acceptable computational durations, surpassing multiple contemporary techniques concerning solution quality and resilience. Our results also underscore the scalability of our proposed approach, marking it as a viable choice for tackling extensive, real-world CVRPs. This paper enriches the current knowledge bank by demonstrating the prowess of GAs in deciphering complicated combinatorial optimization issues, thus offering a novel viewpoint for future advancements in crafting more robust and efficient CVRP resolutions. © 2023 IEEE.

关键词： Genetic algorithms

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QoS Prediction Model of Cloud Services Based on Deep Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 564-566页

作者： WenJun Huang PeiYun Zhang YuTong Chen MengChu Zhou Yusuf Al-Turki Abdullah Abusorrah Nanjing University of Information Science&Technology School of Computer and SoftwareNanjingChina IEEE New Jersey Institute of Technology Helen and John C.Hartmann Department of Electrical and Computer EngineeringNewarkUSA Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of Engineeringand K.A.CARE Energy Research and Innovation CenterKing Abdulaziz UniversityJeddah 21589Saudi Arabia

Dear editor,This letter presents a deep learning-based prediction model for the quality-of-service(QoS)of cloud ***,to improve the QoS prediction accuracy of cloud services,a new QoS prediction model is proposed,which is based on multi-staged multi-metric feature fusion with individual *** multi-metric features include global,local,and individual *** results show that the proposed model can provide more accurate QoS prediction results of cloud services than several state-of-the-art methods.

关键词： services Services individual

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