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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, HongHao Zhang, Acong Li, Ping Southwest Petroleum University School of Computer Science and Software Engineering Chengdu China China Mobile Jiangxi Co. Ltd. Network Department Ganzhou China

ISBN: (纸本)9798350386226

Continuous molecular graph representations are highly useful for effective molecule property predictions. However, learning graph-specific structure information remains challenging. Current graph neural network models typically aggregate all node embeddings of a graph into a single vector, which, while being an efficient compressor, may not capture the high-level function-related substructure information, e.g., motifs. Moreover, the nodes that are loosely relevant to the graph representation may lead to the sub-optimal representation learning. Towards these limitations, in this paper we propose a new approach to enrich molecular graph representation, where the most relevant motif are selected to represent the graph at a high level and the key-node spanned subgraph is constructed to filter out irrelevant nodes. By comparing to recently proposed unimodal graph neural network models and multi-modal methods, we show that our method achieves state-of-the-art performance on molecular property prediction task. © 2024 IEEE.

关键词： Molecular docking

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Multimodal Biometric Recognition Neural Networks with Branch Attention Mechanisms 2

Multimodal Biometric Recognition Neural Networks with Branch...

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2nd Asia Conference on Advanced Robotics, Automation, and Control engineering, ARACE 2023

作者： Liujun Mingjin, Chen Guoyong Zengxi, Huang Xihua University College of Computer and Software Engineering Chengdu China Sichuan Police College Criminal Science and Technology Department Chengdu China

ISBN: (纸本)9798350328363

It is generally known that compared to unimodal systems, the multi-modal biometric systems can improve recognition accuracy by utilizing the complementary features of multiple biometric. Nevertheless, the modalities used may not always play equally important roles in multi-modal recognition. Especially, in practical application scenarios, it often occurs that some of the biometric samples are contaminated by noise and corruption. In this context, the multi-modal recognition systems should pay more attention to the high-quality modality than the ones with low-quality samples, so as to maintain high accuracy. In this paper, we proposes a multi-branch deep learning network for multi-modal recognition, each branch using one biometric sample as input. To highlight the more important biometric modalities, we propose to embed a branch attention module between branches before multi-modal feature fusion. The branch attention module includes fuse, select, and calibrate steps, which can dynamically calculate the weight of each branch based on its importance. The proposed multi-modal recognition model is tested on a chimeric bimodal dataset with the face and the ear modalities. Our proposed method still achieves 92.76% of rank-1 on the most contaminated dataset;the rest of the experimental results are shown in the Experiment and Performance Analysis section. Our experimental results clearly demonstrate that the proposed branch attention method can improve the multi-modal recognition performance. © 2023 IEEE.

关键词： Biometrics

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Recognition of Fire Situation Using Graph Convolutional Network Model 15th

Recognition of Fire Situation Using Graph Convolutional Netw...

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15th International Conference on computer science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Kim, Si Jin Park, Ji Su Kang, Jungho Shon, Jin Gon Advanced Convergence Technology Laboratory Korea National Open University Seoul Korea Republic of Department of Computer Science and Engineering Jeonju University Jeonju-Si Korea Republic of Department of Software Engineering Baewha Woman University Seoul Korea Republic of Department of Computer Science Korea National Open University Seoul Korea Republic of

ISBN: (纸本)9789819724468

Fires cause a lot of casualties and economic losses. In order to prevent fire accidents in advance, it is necessary to find out the cause of the fire. Existing fire alarm systems detected fires with temperature, smoke, and flame sensors, but could not distinguish the cause of the fire. In this paper, to solve this problem, the recognition of fire situation was tested using the GCN model. The fire dataset used in the experiment is a fire dataset composed of causes such as boxes, clothing, and electricity. The accuracy of 93.44% was confirmed as a result of the learning experiment with the GCN model, a graph-based deep learning model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Premixed flames

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AI Ethics in Healthcare-A Survey 23

AI Ethics in Healthcare-A Survey

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23rd IEEE International Conference on software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Singh, Bhavraaj Rahim, Muhammad Abdul Basit Ur Hussain, Shahid Rizwan, Muhammad Abid Zhao, Jie California State University Department of Computer Science Long BeachCA United States Penn State University Department of Computer Science Behrend PA United States Florida Polytechnic University Department of Computer Science LakelandFL United States Penn State University Department of Computer Science and Software Engineering Behrend PA United States

ISBN: (纸本)9798350359398

In the realm of healthcare, the exponential growth of Artificial Intelligence has precipitated a need to scrutinize its ethical implications. This research undertakes a comprehensive survey to unravel the intricate tapestry of AI ethics within the healthcare landscape. Objective is to delineate the multifaceted challenges that arise from the symbiotic relationship between AI and healthcare and proposing viable solutions for mitigation. A pivotal focus of this study is to bridge the divide between medical practitioners and AI developers, thus addressing a conspicuous research gap. This gap pertains to fostering seamless collaboration between these stakeholders, ensuring that AI systems align with the actual requirements of healthcare providers. The paper explores strategies to establish an effective dialogue, facilitating the design and implementation of ethically sound AI applications. The paper also delves into the moral conundrums engendered by AI's lack of emotional intelligence in sensitive healthcare contexts. The absence of human emotional comprehension has, in certain instances, led to grievous outcomes, necessitating a nuanced approach to machine autonomy. This study advocates for an equilibrium where intelligent machines operate under prudent human oversight, striking a harmonious balance between precision and compassion. Furthermore, the research evaluates prevailing systems and their attendant challenges, emphasizing the advantages of integrating ethically guided, intelligent systems. The paper contemplates governance structures, protocols and strategies to counteract biases inherent in AI algorithms. By dissecting the principles of fairness, accountability and transparency, this study paves the way for a cogent framework that governs AI deployment within healthcare. In essence, this paper charts an uncharted course through the unexplored terrain of AI ethics in healthcare. It not only recognizes the inherent challenges but also underscores the imperative

关键词： Data privacy

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Gamification of E-Learning Apps via Acceptance Requirements Analysis 19

Gamification of E-Learning Apps via Acceptance Requirements ...

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19th International Conference on Evaluation of Novel Approaches to software engineering, ENASE 2024

作者： Calabrese, Federico Piras, Luca Al-Obeidallah, Mohammed Ghazi Egbikuadje, Benedicta Oghenevoke Alkubaisy, Duaa Department of Information Engineering and Computer Science University of Trento Trento Italy Department of Computer Science Middlesex University London United Kingdom Department of Software Engineering Al Ain University Abu Dhabi United Arab Emirates College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

ISBN: (纸本)9789897586965

In the last few years, and particularly during and after the COVID-19 pandemic, E-Learning has become a very important and strategic asset for our society, relevant both for academic and industry settings, involving participants ranging from students to professionals. Different applications have been developed to support E-Learning to be an effective tool, particularly in relation to the software engineering and programming areas. However, in order to be effective, in particular within academic settings, such tools require students to be continuously engaged and motivated to learn both practical and theoretical aspects. The integration of gamification in educational environments has gained considerable prominence as a potential mean to augment students’ motivation and involvement, providing them with immediate feedback and reinforcement, bolstering their sense of accomplishment and motivation to persist in their studies. However, to design gamified applications that can effectively engage and motivate users, as the literature has demonstrated, it is required to consider psychological, sociological, and human behavioural aspects, often referred to as Acceptance Requirements. This study presents a case study, where a Goal Modeling-based, Systematic, Acceptance Requirements Analysis and Gamification Design process, has been applied, by using the Agon framework, to prototype a gamified tool, aiming at engaging students towards learning both theory and practice related to a "Web-Based Mobile App Development" university module. To evaluate our proposed prototype, students were involved to use our proposed gamified prototype. The results show that our gamification solution can engage and motivate students towards learning both theoretical and practical aspects of the module. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Requirements engineering

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Abnormal Behavior Detection Using Deep-Learning-Based Video Data Structuring

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Intelligent Automation & Soft Computing 2023年第8期37卷 2371-2386页

作者： Min-Jeong Kim Byeong-Uk Jeon Hyun Yoo Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea Contents Convergence Software Research Institute Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-si16227Gyeonggi-doKorea

With the increasing number of digital devices generating a vast amount of video data,the recognition of abnormal image patterns has become more ***,it is necessary to develop a method that achieves this task using object and behavior information within video *** methods for detecting abnormal behaviors only focus on simple motions,therefore they cannot determine the overall behavior occurring throughout a *** this study,an abnormal behavior detection method that uses deep learning(DL)-based video-data structuring is *** and motions are first extracted from continuous images by combining existing DL-based image analysis *** weight of the continuous data pattern is then analyzed through data structuring to classify the overall *** performance of the proposed method was evaluated using varying parameter settings,such as the size of the action clip and interval between action *** model achieved an accuracy of 0.9817,indicating excellent ***,we conclude that the proposed data structuring method is useful in detecting and classifying abnormal behaviors.

关键词： Deep learning object detection abnormal behavior recognition classification data structuring

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A Brief Overview of Deep Learning Approaches for IoT Security 4

A Brief Overview of Deep Learning Approaches for IoT Securit...

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4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Hassaan Khalid, Muhammad Sharif, Hanan Rehman, Faisal Naeem Ullah, Muhammad Shaukat, Shahbaz Maqsood, Hadia Nouman Ali, Chaudhry Hussain, Ayaz Iftikhar, Irfana Lahore Leads University Department of Computer Science & Information Technology Lahore Pakistan Mehran University of Engineering and Technology Szab Department of Software Engineering Khairpur Mir's Pakistan

ISBN: (纸本)9798350335316

The continuous increasing usage of internet devices in many areas of human life is continuously growing and demanding a proper method to protect these IoT devices from cyber-attacks and vulnerabilities. In this aspect, deep learning neural networks are a very helpful and beneficial method to provide protection to internet devices in many aspects. This study's objective is to systematically research and examine the background of research about neural networks tactics functional to different internet-based devices' safety use-cases. We put the influences we looked at into many different groups to show where this research is lacking. This particular research or findings mainly captures the articles related to 'Deep neural networks', 'Cyber risks' and 'IOT' in four databanks, namely science Direct, ACM Digital Library, IEEEXplore and Springer Link. These studies concern 3 main studies, which involve safety aspects, their own Deep Learning network designs, and the occupied datasets. The final round is to find out the loopholes to be examined in the future and vulnerabilities and drawbacks in the IoT security scenario. © 2023 IEEE.

关键词： Deep neural networks

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Post-training with Data Augmentation for Improving T5-Based Question Generator 14th

Post-training with Data Augmentation for Improving T5-Based ...

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14th International Conference on computer science and its Applications, CSA 2022 and the 16th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2022

作者： Park, Gyu-Min Hong, Seong-Eun Hong, Choong Seon Park, Seong-Bae Department of Computer Science and Engineering Kyung Hee University Yongin Korea Republic of Department of Software Convergence Kyung Hee University Yongin Korea Republic of

ISBN: (纸本)9789819912513

Post-training is known to be effective for boosting the performance of a pre-trained language model. However, in the task of question generation, question generators post-trained with a well-designed training objective show poor performance without sufficient training examples. To handle this problem, this paper proposes a novel post-training for question generation which adopts a data augmentation technique to increase the number of training examples as well as post-training objectives. As post-training objectives, this paper introduces a new training objective, wh-words deletion, in addition to the well-known question infilling. Moreover, this paper employs back-translation techniques to increase the number of instances for post-training. To prove the effectiveness of the proposed method, this paper applies the post-training strategies to T5, a large-scale pre-trained language model, on SQuAD-QG. The experimental results demonstrate that the proposed post-training is helpful for enhancing the performance of answer-aware question generation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computational linguistics

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A Study on the Application of Protein Language Models in the Analysis of Membrane Proteins 19th

A Study on the Application of Protein Language Models in...

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19th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2022

作者： Ghazikhani, Hamed Butler, Gregory Department of Computer Science and Software Engineering Concordia University Montreal Canada Centre for Structural and Functional Genomics Concordia University Montreal Canada

ISBN: (纸本)9783031232091

Membrane proteins make up around 30% of all proteins in a cell. These proteins are difficult to evaluate due to their hydrophobic surface and dependence on their original in vivo environment. There is a tremendous demand for computational approaches for predicting membrane proteins’ function and structure. Protein sequences, the language of life, can benefit from using natural language algorithms. In this study, we propose to use the BERT language model to represent membrane proteins. Our preliminary results, which utilized a Logistic Regression classifier, reveal that BERT can comprehend the context of membrane proteins and enable us to investigate better and distinguish these proteins. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Proteins

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Weapon Detection Identification and Classification for DCNN and YOLO-V5 Techniques 5

Weapon Detection Identification and Classification for DCNN ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Ashok Kumar, J. Dhivya, G. Selvaraj, P. Karthikeyan, V. Ganesh Ramesh, S.P. Lakshmi, S. Jeya Saravanakumar, R. Karpagam Academy of Higher Education Department of Computer Applications Tamil Nadu Coimbatore India School of Computer Science and Engineering Department of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India School of Computing SASTRA Deemed to Be University Thanjavur Tamil Nadu Thirumalaisamudram India Iconix Software Solution Tamil Nadu Tirunelveli India

ISBN: (纸本)9798331518097

Over 850,000 people die every year as a direct result of gun violence, yet civilians hold more than 85% of the world's weapons. Detecting weapons via manual surveillance has not been successful. It is critical to automate a system that detects weapons. A large number of convolution neural network CNN-based automated weapon identification systems have shown promising results. These methods are crucial for weapon identification but expensive. These devices produce a large amount of false negatives since monitoring videos are not high enough quality or visible enough to identify weapons. Finding the sweet spot between fast detection and a few false positives and negatives in weapon detection is the main goal of this research. In addition to defining an Area of Interest, the suggested structure uses YOLO. The models use previously processed images with backgrounds that have been blurred using a median filter. Metrics such as F1 scores, accuracy, recall, False Positive, and False Negative will be used to assess the suggested architecture. This investigation shows that YOLO-v5 can quickly identify and has a high recall rate. The deep convolution neural network DCNN required 0.17 ms for each frame, but this one only required 0.010 ms, which would indicate that security and weapon detection have good promise. Starting with a labeled collection of weapon and non-weapon photos is the initial stage in DCNN and YOLOv5 weapon detection. Use YOLOv5's ability to recognize multiple things within frames to train a real-time object localization and identification model. DCNNs categorize weapons and extract complex data effectively. DCNN classifies objects, whereas YOLOv5 detects, bounds, and tracks them. Optimize both models' hyperparameters and apply data augmentation for better performance. Install the integrated system on cutting-edge equipment for real-time weapon identification and classification. © 2024 IEEE.

关键词： Median filters

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