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2023 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, JEEIT 2023

作者： Alodibat, Sawsan Ahmad, Ashraf Azzeh, Mohammad Princess Sumaya University for Technology Department of Computer Science Amman Jordan Princess Sumaya University for Technology Department of Data Science Amman Jordan

ISBN: (纸本)9798350324051

The field of Explainable Artificial Intelligence (XAI) has gained significant momentum in recent years. This discipline is focused on developing novel approaches to explain and interpret the functioning of machine learning algorithms. As machine learning techniques increasingly adopt 'black box' methods, there is growing confusion about how these algorithms work and make decisions. This uncertainty has made it challenging to implement machine learning in sensitive and critical fields. To address this issue, research in machine learning interpretability has become crucial. One particular area that requires attention is the detection process and classification of malware. Handling and preparing data for malware detection poses significant difficulties for machine learning algorithms. Thus, explainability is a critical requirement in current research. Our research leverages XAI, a novel design of explainable artificial intelligence that uses cybersecurity data to gain knowledge about the composition of malware from the Microsoft large benchmark dataset-Microsoft Malware Classification Challenge (BIG 2015). We use the LIME explainability technique and the Secml python library to develop explainable prediction results about the class of malware. We achieved 94% accuracy using Decision Tree classifier. © 2023 IEEE.

关键词： Lime

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An optimal hybrid cascade regional convolutional network for cyberattack detection

An optimal hybrid cascade regional convolutional network for...

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作者： Alqahtani, Ali Khan, Surbhi Bhatia Department of Networks and Communications Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Data Science School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Cyber-physical systems (CPS) and the Internet of Things (IoT) technologies link urban systems through networks and improve the delivery of quality services to residents. To enhance municipality services, information and communication technologies (ICTs) are integrated with urban systems. However, the large number of sensors in a smart city generates a significant amount of delicate data, like medical records, credit card numerics, and location coordinates, which are transported across a network to data centers for analysis and processing. This makes smart cities vulnerable to cyberattacks because of the resource constraints of their technology infrastructure. Applications for smart cities pose many security challenges, such as zero-day attacks resulting from exploiting weaknesses in various protocols. Therefore, this paper proposes an optimal hybrid transit search-cascade regional convolutional neural network (hybrid TS-Cascade R-CNN) to detect cyberattacks. The proposed model combines the hybrid transit-search approach with the cascade regional convolutional neural network to create an optimal solution for cyberattack detection. The cascade regional convolutional network uses a hybrid transit search algorithm to enhance the effectiveness of cyberattack detection. By integrating these two approaches, the system can leverage both global traffic patterns and local indicators to improve the accuracy of attack detection. During the training process, the proposed model recognizes and classifies malicious input even in the presence of sophisticated attacks. Finally, the experimental analysis is carried out for various attacks based on different metrics. The accuracy rate attained by the proposed approach is 99.2%, which is acceptable according to standards. © 2023 John Wiley & Sons Ltd.

关键词： Zero-day attack

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An implementation of GPU accelerated mapreduce: using hadoop with openCL for breast cancer detection and compute-intensive jobs

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International Journal of Information Technology (Singapore) 2024年 1-8页

作者： Ouhakki, Hamza Elmoufidi, Abdelali Data4Earth Laboratory Department of Computer Science Faculty of Sciences and Technics Sultan Moulay Slimane University Beni-Mellal Morocco

Abstract-In the realm of distributed computing for large-scale data processing, MapReduce stands out for its efficiency. However, as tasks become increasingly compute-intensive, it faces challenges in single-node performance. In the context of breast cancer detection, particularly with image data, a new approach has emerged to enhance MapReduce through GPU acceleration. This implementation, executed using Hadoop and OpenCL, targets a general and cost-effective hardware platform, seamlessly integrating into Apache Hadoop. Tailored for a heterogeneous multi-machine and multicore architecture, this solution addresses the compute-intensive nature of big data applications in breast cancer image analysis. Remarkably, the implementation has achieved a significant nearly 13-fold improvement in performance, without the need for additional optimizations. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Breast cancer detection Compute-intensive GPU acceleration Hadoop MapReduce OpenCL

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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PVDM-YOLOv8l: a solution for reliable pedestrian and vehicle detection in autonomous vehicles under adverse weather conditions

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Multimedia Tools and Applications 2024年 1-26页

作者： Tahir, Noor Ul Ain Zhang, Zuping Asim, Muhammad Iftikhar, Sundas A. Abd El-Latif, Ahmed School of Computer Science and Engineering Central South University Changsha410083 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Gianforte School of Computing Montana State University Bozeman59717 United States Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Ensuring the safe navigation of autonomous vehicles in intelligent transportation system depends on their ability to detect pedestrians and vehicles. While transformer-based models for object detection have shown remarkable advancements, accurately identifying pedestrians and vehicles in adverse weather conditions remains a challenging task. Adverse weather introduces image quality degradation, leading to issues such as low contrast, reduced visibility, blurred edges, false detection, misdetection of tiny objects, and other impediments that further complicate the accuracy of detection. This paper introduces a novel Pedestrian and Vehicle Detection Model under adverse weather conditions, denoted as PVDM-YOLOv8l. In our proposed model, we first incorporate the Swin-Transformer method, which is designed for global extraction of feature of small objects to identify in poor visibility, into the YOLOv8l backbone structure. To enhance detection accuracy and address the impact of inaccurate features on recognition performance, CBAM is integrated between the neck and head networks of YOLOv8l, aiming to gather crucial information and obtain essential data. Finally, we adopted the loss function Wise-IOU v3. This function was implemented to mitigate the adverse effects of low-quality instances by minimizing negative gradients. Additionally, we enhanced and augmented the DAWN dataset and created a custom dataset, named DAWN2024, to cater to the specific requirements of our study. To verify the superiority of PVDM-YOLOV8l, its performance was compared against several commonly used object detectors, including YOLOv3, YOLOv3-tiny, YOLOv3-spp, YOLOv5, YOLOv6, and all the versions of YOLOv8 (n, m, s, l, and x) and some traditional models. The experimental results demonstrate that our proposed model achieved a 6.6%, 5.4%, 6%, and 5.1% improvement in precision, recall, F1-score and mean Average Precision (mAP) on the custom DAWN2024 dataset. This substantial improvement in accuracy ind

关键词： Vehicle detection

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Estimation of Facial Emotion Based on Landmark Points by Applying Artificial Intelligence and Machine Learning 7

Estimation of Facial Emotion Based on Landmark Points by App...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Osman Abdalraheem, Mohammed Hassan Hossain, Mohammad Alamgir Ahmed Hamdan, Alfadil Kechadi, M. Tahar Limker, Suresh Jazan University Department of Information Technology Saudi Arabia Jazan University Department of Computer Science Saudi Arabia Sudan University of Science and Technology Department of Computer Science Sudan School of Computer Science University College Dublin Dublin Ireland Aissms Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350304268

In the science of emotion processing, it is hard to figure out how to predict an emotion from naturalistic facial expressions in a multidimensional space. While many studies and works have been done to develop methods for making accurate predictions, relatively little has been done to put the uniqueness of the human face to use in reading and responding to people's expressions of emotion. This work proposes an emotional image retrieval strategy for multimodal emotion detection. This strategy is based on the fact that face features are always there and that the human face has regional redundancy that is independent of face features. Our method uses landmark points to get rid of unnecessary information about facial expressions and focus on the most attractive ones in each frame. It's possible that by making use of the landmarks, we'll be able to predict facial emotions more precisely. This is especially helpful for long-term emotion prediction since it allows us to recover time and space representations by introducing an emotional face. The preliminary results based on a benchmark dataset show that the proposed method exceeds its competitors in terms of prediction accuracy. © 2023 IEEE.

关键词： emotion prediction facial expression landmark point LBP method

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Machine Learning Approaches in Detecting Autism Spectrum Disorder 8

Machine Learning Approaches in Detecting Autism Spectrum Dis...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Daniel Dominic, Nicholas Cenggoro, Tjeng Wawan Pardamean, Bens Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Early detection of Autism Spectrum Disorder (ASD) needs to be increased to prevent further adverse impacts. Thus, the classification between ASD and Typically Development (TD) individuals is an intriguing task. This review study has collected 26 related papers to answer four research questions, i.e., what are the most used data inputs, brain atlases, and machine learning models for ASD classification, as also to discover the significant parts of the brain correlated with the ASD. It was eventually found that functional connectivity matrix, Support Vector Machine, and CC200 are the most frequently used data input, model, and brain atlas, respectively. Researchers also concluded that the posterior temporal fusiform cortex, intracalcarine cortex, cuneal cortex, subcallosal cortex, occipital pole, and lateral occipital cortex are the brain regions highly correlated with ASD. © 2023 The Authors. Published by Elsevier B.V.

关键词： autism spectrum disorder brain atlas brain region machine learning

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Document-level Clinical Entity and Relation Extraction via Knowledge Base-Guided Generation 23

Document-level Clinical Entity and Relation Extraction via K...

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23rd Meeting of the ACL Special Interest Group on Biomedical Natural Language Processing, BioNLP 2024

作者： Bhattarai, Kriti Oh, Inez Y. Abrams, Zachary B. Lai, Albert M. Institute for Informatics Data Science & Biostatistics Washington University School of Medicine United States Department of Computer Science Washington University in St. Louis United States

ISBN: (纸本)9798891761308

Generative pre-trained transformer (GPT) models have shown promise in clinical entity and relation extraction tasks because of their precise extraction and contextual understanding capability. In this work, we further leverage the Unified Medical Language System (UMLS) knowledge base to accurately identify medical concepts and improve clinical entity and relation extraction at the document level. Our framework selects UMLS concepts relevant to the text and combines them with prompts to guide language models in extracting entities. Our experiments demonstrate that this initial concept mapping and the inclusion of these mapped concepts in the prompts improves extraction results compared to few-shot extraction tasks on generic language models that do not leverage UMLS. Further, our results show that this approach is more effective than the standard Retrieval Augmented Generation (RAG) technique, where retrieved data is compared with prompt embeddings to generate results. Overall, we find that integrating UMLS concepts with GPT models significantly improves entity and relation identification, outperforming the baseline and RAG models. By combining the precise concept mapping capability of knowledge-based approaches like UMLS with the contextual understanding capability of GPT, our method highlights the potential of these approaches in specialized domains like healthcare.. ©2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Monte Carlo Tree Search-Based Agent Negotiation Framework for Shared Decision-Making 5

Monte Carlo Tree Search-Based Agent Negotiation Framework fo...

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5th International Conference on Big data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Chen, Xin Liu, Yong Lu, Ping Lin, Kai-Biao Xiamen Institute of Technology School of Data Science and Computer Science Xiamen361021 China Xiamen University of Technology School of Economic and Management Xiamen361024 China Xiamen University of Technology Department of Computer Science and Technology Xiamen361024 China

ISBN: (纸本)9798331506612

Shared Decision-Making (SDM) is a collaborative process in which patients and healthcare providers jointly make medical decisions, integrating clinical evidence with the patient's preferences and values. Although previous research has focused on SDM through decision-support tools or manual negotiations, these methods have been limited by time and cost constraints. To overcome these challenges, this study introduces an automated SDM negotiation framework using Monte Carlo Tree Search (MCTS) to support decision-making for both doctors and patients. The proposed MCTSAN model demonstrates superior performance across various scenarios with differing issue complexities. Notably, it maintains high overall satisfaction and reduces satisfaction disparities between patients and healthcare providers, even as negotiation complexity increases. The model typically reaches agreements within 7 to 15 negotiation rounds, effectively enhancing satisfaction and social welfare while minimizing the time and space costs associated with negotiations. This negotiation framework proves to be a valuable tool in facilitating and promoting SDM in clinical settings. © 2024 IEEE.

关键词： Clinical research

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Autonomous Robot Navigation in Unknown Terrains using Generative Adversarial Networks 4

Autonomous Robot Navigation in Unknown Terrains using Genera...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Chitra, P. Sridar, K. Govindan, Sudha Britto Raj, S. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. St Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Veerammal Engineering College Department of Computer Science and Engineering Dindigul India Sona College of Technology Department of Computer Science and Engineering Salem India RRASE College of Engineering Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computational Intelligence Chennai India SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798331515720

Autonomous Robot Navigation in unknown and dynamic environments is considered one of the grand challenges in robotics. These motions that guide it span from rescue operations to planetary exploration. Classical methods for path planning, including A*, Dijkstra, and RRT, were unable to balance the efficiency and adaptability of motion planning over complex terrain. This may have a powerful solution using Generative Adversarial Networks (GANs) with advanced machine learning techniques to improve the ability of robotic navigation. This study proposes a GAN-based approach for designing an effective self-adaptive path planning and obstacle avoidance system for autonomous robots. Despite performing well in structured environments, traditional techniques normally fail in analysing the unstructured terrains with real-time adjustments and dynamic changes due to the high computational costs and lack of flexibility involved. In this regard, the proposed GAN-based system resolves these issues by rating navigation paths concerning safety and efficiency. Quantitative analysis demonstrates that within a forest, desert, urban, or off-road environment, the computational times by using the developed strategy turn out to be 0.12 seconds, 0.10 seconds, 0.14 seconds, and 0.13 seconds, respectively, behaving much lower than what can be achieved with other algorithms. This approach proves much superior in terms of computational efficiency path smoothness and adaptability under different environmental conditions in applications related to autonomous robot navigation systems. © 2024 IEEE.

关键词： Generative adversarial networks

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