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2023 Advances in science and engineering Technology International Conferences, ASET 2023

作者： Al Mansouri, Ltifa Mohammed Khalifa Alnajjar, Fady College of It United Arab Emirates University Department of Computer Science and Software Engineering Al Ain United Arab Emirates

ISBN: (纸本)9781665454742

Human microchip implants are small devices that are implanted into the body for various purposes including storing and transmitting information, monitoring health or location, and providing access to facilities or systems. Although human microchip implants offer numerous advantages, their adoption remains low in the public sector. This article presents a comprehensive survey on the emerging trend of human microchip implants and their potential uses in various fields. In addition to introducing the general concept of microchip implants, the work discusses their current and potential future applications. To better understand the direction of this trend, recent case studies of existing applications of human microchip implants are analyzed and presented. The article also explores the barriers to the adoption of human microchip implants and discusses their potential impact on humans, whether positive or negative. © 2023 IEEE.

关键词： Near field communication

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Using Deep Learning and Object-Oriented Metrics to Identify Critical Components in Object-Oriented Systems 23

Using Deep Learning and Object-Oriented Metrics to Identify ...

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5th World Symposium on software engineering, WSSE 2023

作者： Tete, Akpedje Touré, Fadel Badri, Mourad Software Engineering Research laboratory Department of Mathematics and Computer Science University of Quebec Trois-RivièresQC Canada

ISBN: (纸本)9798400708053

This paper aims at studying the ability of deep machine learning to predict software faults based on object-oriented metrics. This research investigated software faults from the perspective of fault-proneness, faults number and faults frequency, and used data collected from several versions of a Java open-source software system. This study relied on Chidamber and Kemerer suite of metrics as proxy to capture various software characteristics. In this study, the deep learning regression and classification results were compared to linear and logistic regressions. Auto-Encoders (AE) and Principal Components Analysis (PCA) have been used to reduce redundant information from the dataset. To evaluate the prediction ability of the models, this research used the inter-version validation strategy. The results showed that the models can achieve a significant average performance up to 89%. © 2023 Copyright held by the owner/author(s).

关键词： Principal component analysis

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Wildfire Spread Prediction Using Attention Mechanisms in U-NET

Wildfire Spread Prediction Using Attention Mechanisms in U-N...

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2023 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2023

作者： Shah, Kamen Pantoja, Maria California Polytechnic State University Dept. Computer Science and Software Engineering San Luis Obispo United States

ISBN: (纸本)9798350322972

Global warming has increased large-scale natural disasters at an alarmingly greater frequency. These natural disasters affect humans and the ecosystems that all species rely upon for food and shelter. A significant byproduct of global warming is the emergence of mega-fires, which are wildfires that burn more than 100,000 acres. Mega fires have exhibited the capacity to destroy entire ecosystems and urban areas. Researchers forecast a significant increase in wildfires during the next decade. This is a fundamentally growing problem that mandates modern solutions. Currently, simulation models are employed to predict wildfire propagation and aid firefighters in suppressing them. However, these methods are computationally expensive and often inaccurate. A recent advance in this area is the usage of neural networks (NN), which are systematically more accurate and computationally efficient. This paper focuses on predicting wildfire spread using NN with an attention mechanism to improve spatial recognition and overall performance in neural networks for wildfire spread prediction. We train different models on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. Results show that when augmenting segmentation models with attention mechanisms, the attention component improves feature suppression and recognition, improving overall performance. Furthermore, we use ensemble modeling to reduce bias and variation, leading to more consistent and accurate predictions. The architecture presented in this research achieved a ROC-AUC score of 86.2% and an accuracy of 82.1% when inferencing on wildfire propagation at 30-minute intervals. © 2023 IEEE.

关键词： Forecasting

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Competition for roadside camera monocular 3D object detection

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National science Review 2023年第6期10卷 34-37页

作者： Jinrang Jia Yifeng Shi Yuli Qu Rui Wang Xing Xu Hai Zhang Baidu Inc. Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University College of Software Jilin University School of Computer Science and Engineering University of Electronic Science and Technology of China Pazhou Laboratory (Huangpu) School of Mathematics Northwest University

INTRODUCTION Accurate environment perception is a critical topic in autonomous driving and intelligent *** environmental perception methods mostly rely on on-board ***,limited by the installation height,thereare problems such as blind spots and unstable long-range perception in vehicle perceptual ***,with the rapid improvement of intelligent infrastructure,it has become possible to use roadside cameras for traffic environment *** from the increased height when compared with on-boardsensors,roadside cameras can obtain a larger perceptual field of view and realize long-range observation.

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Artificial Intelligence For Realtime Face Recognition Attendance Using College Classrooms and Buses

Artificial Intelligence For Realtime Face Recognition Attend...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Ahila, A. Hosanna Princye, P. Poonguzhali, A. Kavivendhan Deepa, M. Arthy, A. Saravanakumar, R. Sri Sairam College of Engineering Department of Electronics and Communication Engineering Anekal Bangalore India Francis Xavier Engineering College Department of Computer Science and Business Systems Tamil Nadu Tirunelveli India Iconix Software Solution Software Analyst Tamil Nadu Tirunelveli India

ISBN: (纸本)9798350378092

Teachers take attendance by having pupils sign in or check-in classes and transportation. Student absences often result from individual mistakes. This article examines a technology that records data from classroom photographs of every student's face. This research uses an Adaptive Boost Classifier, Random Forest (RF), and Deep Convolutional Neural Networks (DCNNs). The model performs well on the DCNN model with 88 and 92% accuracy and on the ResNet50 pre-trained model with 97.21% accuracy. After detecting each student's face, they recorded their present status in an Excel document. It kept the best system implementation approach based on performance. © 2024 IEEE.

关键词： Convolutional neural networks

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Learning Age of Information-Oriented Energy-Efficient Scheduling with Non-Orthogonal Multiple Access 13

Learning Age of Information-Oriented Energy-Efficient Schedu...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Li, Jinhua Cao, Yi Chan, Tse-Tin Pan, Haoyuan Shenzhen University College of Computer Science and Software Engineering Shenzhen China The Education University of Hong Kong Department of Mathematics and Information Technology Hong Kong

ISBN: (纸本)9798350355079

We propose a deep Q-network (DQN)-based user scheduling scheme aimed at low network-wide age of information (AoI) in a non-orthogonal multiple access (NOMA) uplink status update system. In our system, end users can harvest energy from wireless signals received on the radio-frequency band to extend the lifespan of their batteries. Given the battery replacement cost in many practical scenarios, our objective is to design a scheduling scheme to maintain a low AoI while efficiently utilizing energy harvesting and reducing the number of battery replacements. Considering the instantaneous AoI, remaining energy, and channel condition of end users, we model the user scheduling problem as a Markov decision process (MDP), which is solved by DQN. In particular, the choice of transmission power to save energy while enabling a high NOMA decoding rate is incorporated in the design of DQN. Experiments indicate that our DQN-based scheduling scheme achieves a low average AoI of the system with significantly fewer battery replacements compared to various benchmarking schemes. © 2024 IEEE.

关键词： Markov processes

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Development of Area-Based Explosive Trace Detection Using Deep Transfer Learning 1

Development of Area-Based Explosive Trace Detection Using De...

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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Ohemu, Monday F. Azeta, Ambrose A. Adeyanju, Ibrahim A. Obasi, Chukwuemeka C. Airforce Institute of Technology Department of Electrical/Electronics Engineering Kaduna Nigeria Namibia University of Science and Technology Department of Software Engineering Namibia Federal University Department of Computer Engineering Oye-Ekiti Nigeria Edo State University Department of Computer Engineering Uzairue Nigeria

ISBN: (纸本)9798350348637

Terrorism poses a significant global threat, necessitating comprehensive security measures to protect people and critical infrastructure. With the increasing frequency of explosive-based attacks, there is a pressing need for advanced detection systems to mitigate potential devastation. This paper presents the development of an area-based explosive trace detection system using deep transfer learning. Leveraging deep learning technology trained on a substantial dataset collected from sensor networks, the proposed model, named Deep Transfer Learning for Explosive Trace Detection (DTLETD), demonstrates remarkable capabilities in classifying explosive gases based on concentrations of Carbon (C), Hydrogen (H), Oxygen (O), and Nitrogen (N). The methodology involves converting the dataset into 2D data using a serial data to image generator, followed by the adaptation of a pre-trained model for transfer learning. The DTLETD model outperformed conventional Convolutional Neural Network (CNN) models, achieving a training time reduction of about 92 seconds compared to 1287 seconds for the CNN model. Additionally, the transfer learning model exhibits faster convergence with nearly zero losses during training and validation, yielding an impressive accuracy of 99.7% and an average AUC value of approximately 0.89. This research significantly advances the field of explosive trace detection by integrating machine learning-based approaches with deep transfer learning techniques, thereby enhancing security protocols and mitigating potential threats. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Protein Solubility Prediction through Pre-trained Language Models and Graph Convolutional Neural Networks

Enhancing Protein Solubility Prediction through Pre-trained ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Jie Qian, Yurong Huang, Zhijian Xiao, Xiaojun Deng, Lei Xinjiang University School of Software Urumqi China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350337488

Achieving optimal protein solubility is pivotal for efficient high-throughput purification, especially in industrial settings. However, conventional experimental techniques for assessing protein solubility in such contexts are not only costly but also time-intensive. Currently, numerous methods are available for predicting protein solubility, yet their effectiveness remains limited. Most of these approaches are predominantly sequence-based, failing to harness the invaluable structural insights inherent in proteins. Addressing these limitations, we introduce PPSol, an innovative protein solubility prediction methodology. Operating on protein sequences, PPSol employs ESM2 to predict protein contact maps, forming the basis for constructing protein graphs. Subsequently, well-established techniques are employed to predict protein feature representations as node features, including the utilization of the Position-Specific Scoring Matrix (PSSM). The resulting graph is fed into a graph convolutional neural network (GCN), enabling the acquisition of spatial structural information from proteins. Concurrently, ESM2-generated features undergo dimensional reduction via fully connected layers, integrating into every layer of the GCN for precise protein solubility prediction. Our approach excels through the fusion of pre-trained protein language models and GCNs, surpassing existing methodologies. Notably, PPSol attains state-of-the-art performance, showcasing a remarkable 2.8% enhancement in AUROC performance com-pared to prior strategies. © 2023 IEEE.

关键词： ESM2 GCN Protein contact map Protein solubility

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Low-confidence Heterogeneous Pixel-Prototype Contrastive Learning for Semi-supervised Medical Image Segmentation

Low-confidence Heterogeneous Pixel-Prototype Contrastive Lea...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： He, Shihuan Lai, Zhihui Kong, Heng Shenzhen University College of Computer Science and Software Engineering Shenzhen China Baoan Central Hospital of Shenzhen Department of Breast and Thyroid Surgery Shenzhen China

ISBN: (纸本)9798350386226

Medical image segmentation is a challenging task especially when dealing with unlabeled data. It has been proven that the use of complementary information for co-training is effective for medical image segmentation. We find that high-confidence regions are easy to segment, but low-confidence regions are difficult to correctly segment, which results in poor segmentation performance. Therefore, accurate segmentation in low-confidence regions is the effective way to enhance the performance of medical image segmentation. To achieve this goal, we propose a novel co-training strategy named low-confidence heterogeneous pixel-prototype contrastive learning with uncertainty-guide cross supervision for semi-supervised medical image segmentation. Specifically, an uncertainty-guided cross supervision module is firstly designed to estimate the confidence score of predictions from multiple outputs and then perform cross supervision between high-confidence regions. Moreover, we design a low-confidence heterogeneous prototype contrastive learning, which builds the relationship between low-confidence pixel and heterogeneous prototype to mine discriminative information in low-confidence regions so as to improve the segmentation performance. Extensive experiments on medical image datasets demonstrate that our method outperforms state-of-the-art methods in segmentation results. © 2024 IEEE.

关键词： Contrastive Learning

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CoML: Machine Learning based approach for COVID-19 related Suicidal Ideation detection 6

CoML: Machine Learning based approach for COVID-19 related S...

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6th International Conference on Intelligent Systems and computer Vision, ISCV 2024

作者： Bouktif, Salah Ud Din Khanday, Akib Mohi Ouni, Ali CIT United Arab Emirates University Dept. of Computer Science and Software Engineering Al Ain United Arab Emirates École de Technologie Supérieure University of Quebec Dept. of Software Engineering and IT MontrealQC Canada

ISBN: (纸本)9798350350180

The pervasive use of online social media platforms has changed the communication habits and subsequently the content and the frequency of the human exchanges. People share their thoughts on social media more frequently and freely. In particular and due to the societal stigma, people find less obstacle to discuss mental health problems on social media compared to face-o-face discussion. Mining social media for the sake of analysing suicidal intention is an attractive and at the same time challenging task especially during COVID-19 era. The main objective is to leverage machine learning to classify the social media user suicidal behavior while considering the impact of COVID-19 circumstances. We propose a rigorous feature engineering technique based on TF/IDF and Bag of Words. Machine learning classifiers and ensemble models are compared to find that the Neural Network(NN) classifier outperforms the benchmarks with a precision of 94%, a Recall of 94%, an F1 Score of 94%, and an overall accuracy of 94%. In the future, more feature selection techniques and deep learning can be used. © 2024 IEEE.

关键词： Adversarial machine learning

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