检索结果-内蒙古大学图书馆

Classifying distinct emotions from parents of ASD child using EEG source data by combining Bernoulli–Laplace Prior and graph neural networks

引用

Neural Computing and Applications 2025年第12期37卷 7877-7895页

作者： ArulDass, Stephen Dass Jayagopal, Prabhu School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-making, has made automatic emotion recognition and examination of a significant feature in the field of psychiatric disease treatment and cure. The problem arises from the limited spatial resolution of EEG recorders. Predetermined quantities of electroencephalography (EEG) channels are used by existing algorithms, which combine several methods to extract significant data. The major intention of this study was to focus on enhancing the efficiency of recognizing emotions using signals from the brain through an experimental, adaptive selective channel selection approach that recognizes that brain function shows distinctive behaviors that vary from one individual to another individual and from one state of emotions to another. We apply a Bernoulli–Laplace-based Bayesian model to map each emotion from the scalp senses to brain sources to resolve this issue of emotion mapping. The standard low-resolution electromagnetic tomography (sLORETA) technique is employed to instantiate the source signals. We employed a progressive graph convolutional neural network (PG-CNN) to identify the sources of the suggested localization model and the emotional EEG as the main graph nodes. In this study, the proposed framework uses a PG-CNN adjacency matrix to express the connectivity between the EEG source signals and the matrix. Research on an EEG dataset of parents of an ASD (autism spectrum disorder) child has been utilized to investigate the ways of parenting of the child's mother and father. We engage with identifying the personality of parental behaviors when regulating the child and supervising his or her daily activities. These recorded datasets incorporated by the proposed method identify five emotions from brain source modeling, which significantly improves the accurac

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

The Role of IoT in Enhancing Public Safety in Smart Cities 7

The Role of IoT in Enhancing Public Safety in Smart Cities

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Ebadinezhad, Sahar Abdalkarim, Mutaz Salah Mohammed Deen, Zainab Kiminou Nsimba, Fulbert Milrich Department of Computer Information Systems Nicosia99138 Cyprus Near East University Department of Computer Information Systems Nicosia99138 Cyprus

ISBN: (纸本)9798350359299

In the past few years, new ways have been found to use IoT technology to make smart cities safer for everyone. These include real-time monitoring, data analytics, AI integration, and advanced IoT devices for emergency reactions. Even with these improvements, problems like data protection, security, interoperability, and regulatory barriers still exist and need to be carefully thought through during implementation. The main goal of this study is to look into these problems and come up with ways to solve them so that IoT can be used to its fullest potential to make cities safer. A complete framework aims to successfully connect IoT systems for better public safety outcomes by getting around problems related to data privacy, security, and governance. The goal is to use IoT technology to make smart cities safer and more flexible by taking ethics into account, putting in place strong security measures, and working together. The study rigorously evaluates scholarly publications gathered from reliable sources such as ScienceDirect and Scopus from 2018 to 2023, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. A thorough screening procedure is utilized to assess 15 papers, with a focus on their relevance to the study's major objectives. The study's findings show that smart cities have a complex impact on numerous urban areas such as energy, transportation, and well-being, indicating their potential to improve general metropolitan function. The study, nevertheless, emphasizes the limitations and constraints inherent in the general structure of smart cities. This research intends to bring attention to the operational processes, advantages, difficulties in implementation, and potential of smart cities as a reaction to increasing urbanization and global warming trends. © 2024 IEEE.

关键词： Smart city

来源：评论

学校读者我要写书评

暂无评论

Employing cross-domain modelings for robust object detection in dynamic environment of autonomous vehicles

引用

Multimedia Tools and Applications 2024年 1-47页

作者： Rawlley, Oshin Gupta, Shashank Kathera, Hardik Katyal, Siddharth Batwara, Yashvardhan Department of Computer Science & amp Information Systems Birla Institute of Technology and Science Rajasthan Pilani India

Object detection (OD) in Advanced Driver Assistant systems (ADAS) has been a fundamental problem especially when complex unseen cross-domain adaptations occur in real driving scenarios of autonomous Vehicles (AVs). During the sensory perception of autonomous Vehicles (AV) in the driving environment, the Deep Neural Networks (DNNs) trained on the existing large datasets fail to detect the vehicular instances in the real-world driving scenes having sophisticated dynamics. Recent advances in Generative Adversarial Networks (GAN) have been effective in generating different domain adaptations under various operational conditions of AVs, however, it lacks key-object preservation during the image-to-image translation process. Moreover, high translation discrepancy has been observed with many existing GAN frameworks when encountered with large and complex domain shifts such as night, rain, fog, etc. resulting in an increased number of false positives during vehicle detection. Motivated by the above challenges, we propose COPGAN, a cycle-object preserving cross-domain GAN framework that generates diverse variations of cross-domain mappings by translating the driving conditions of AV to a desired target domain while preserving the key objects. We fine-tune the COPGAN training with an initial step of key-feature selection so that we realize the instance-aware image translation model. It introduces a cycle-consistency loss to produce instance specific translated images in various domains. As compared to the baseline models that needed a pixel-level identification for preserving the object features, COPGAN requires instance-level annotations that are easier to acquire. We test the robustness of the object detectors SSD, Detectron, and YOLOv5 (SDY) against the synthetically-generated COPGAN images, along with AdaIN images, stylized renderings, and augmented images. The robustness of COPGAN is measured in mean performance degradation for the distorted test set (at IoU threshold =

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Automated Classification and Identification of Non-Functional Requirements in Agile-Based Requirements Using Pre-Trained Language Models

引用

IEEE Access 2025年 13卷 87401-87417页

作者： Alhaizaey, Abdulrahim Al-Mashari, Majed College of Computer and Information Sciences King Saud University Department of Information Systems Riyadh Saudi Arabia King Khalid University Department of Information Systems Abha Saudi Arabia

Non-functional requirements (NFRs) are critical factors for software quality and success. A frequently reported challenge in agile requirements engineering is that NFRs are often neglected due to the focus on functional requirements (FRs) and the limited capability of agile requirements documented as user stories to represent NFRs. With the emergence of transfer learning and large pre-trained language models, various applications in requirements engineering have become feasible, alleviating several longstanding challenges. This study evaluates transformer-based models for the automated identification and classification of NFRs. We leveraged transfer learning with pre-trained transformer models to automate the identification and classification of NFRs in agile textual requirements documented as user stories. A dataset of over 10k user stories was collected and labeled, and pre-trained transformer models, including BERT, RoBERTa, XLNet, and DistilBERT, were fine-tuned to automate the identification of NFRs. We incorporated Focal Loss during training to mitigate the dominance of functionally driven requirements and class imbalances. In addition, thorough experiments on hyperparameter optimization were employed using Bayesian hyperparameter optimization to obtain the combination of hyperparameters that best correlated with the aim of enhancing each model’s performance. Our evaluation demonstrated that the finetuned pre-trained models significantly outperformed comparable prior approaches relying on rule-based techniques or traditional machine learning, with a fine-tuned BERT model achieving an F1 Score of 93.4 %. These findings highlight the potential of pre-trained language models in agile requirements engineering, enabling more efficient NFRs identification, reducing manual review burden, and facilitating a viable and efficient approach to address the neglect of NFRs in agile development processes. © 2013 IEEE.

关键词： Requirements engineering

来源：评论

学校读者我要写书评

暂无评论

Permissions-based Android malware detection using machine learning

引用

Neural Computing and Applications 2025年第6期37卷 5255-5270页

作者： Alomar, Atheer AlJarullah, Asma Abu-Ghazalah, Sarah Department of Information Systems King Khalid University Alfara Abha61421 Saudi Arabia Department of Informatics and Computer Systems King Khalid University Alfara Abha61421 Saudi Arabia

Android applications are evolving quickly, and both consumers and developers are growing in number. This popularity increases Android system vulnerabilities and attacks. Researchers have proposed various approaches to detect Android malware. One of these approaches is to utilize machine learning to detect Android malware based on permissions. This study developed a permissions-based Android malware detection model using machine learning. It aims to identify the significant permissions list that can be used to distinguish between benign and malware apps. A dataset containing permissions for both types of apps was collected to compare feature selection algorithms (IG, RFE, CHI, GA) with machine learning algorithms (SVM, DS, RF, GB, XG) to identify the best prediction model. The results showed that the SVM model achieved the highest accuracy of 98.88% using RFE with 13 permissions. The model was able to complete the detection process in 12.00 milliseconds. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Android malware

来源：评论

学校读者我要写书评

暂无评论

Interaction-aware Trust Management Scheme for IoT systems With Machine Learning-Based Attack Detection

引用

IEEE Internet of Things Journal 2025年第11期12卷 17169-17182页

作者： Farhat, Ali Eldosouky, Abdelrahman Ibnkahla, Mohamed Matrawy, Ashraf Carleton University Department of Systems and Computer Engineering OttawaON Canada Carleton University School of Information Technology OttawaON Canada

The recent Internet of Things (IoT) adoption has revolutionized various applications while introducing significant security and privacy challenges. Traditional security solutions are unsuitable for IoT systems due to their dynamicity, heterogeneity, and resource constraints. Trust-based solutions are emerging as promising alternatives due to their ability to track the dynamic behavior in IoT systems. However, existing trust management schemes are implemented at the device level, raising several challenges, including device modification that compromises certification and scalability, increased network overhead, and higher device resource utilization. To address these challenges, this paper proposes a novel trust management scheme that shifts its implementation to a higher layer in the IoT system, specifically to the IoT access layer (e.g., gateway). The proposed scheme establishes trust based on typical device interactions with the gateway without requiring additional information from the device. It relies on objective attributes spanning communication, security, and advanced dimensions to compute the trust value of an IoT device. Additionally, an Artificial Neural Network (ANN) is integrated to determine if the device acts maliciously or behaves normally. Simulation results demonstrate a notable improvement in the detection rate, primarily due to incorporating the proposed ANN, compared to the threshold-based approaches in the literature. Overall, the improvements highlight the significant advantage of the proposed scheme's robustness. © 2025 IEEE.

关键词： Gateways (computer networks)

来源：评论

学校读者我要写书评

暂无评论

An IoMT-based Federated Learning Survey in Smart Transportation

引用

Recent Advances in computer Science and Communications 2024年第4期17卷 20-39页

作者： Karnam, Geetha Vani Maddikunta, Praveen Kumar Reddy School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

Internet of Medical Things (IoMT) is a technology that encompasses medical devices, wearable sensors, and applications connected to the Internet. In road accidents, it plays a crucial role in enhancing emergency response and reducing the impact of accidents on victims. Smart Transportation uses this technology to improve the efficiency and safety of transportation systems. The current Artificial Intelligence applications lack transparency and interpretability which is of utmost importance in critical transportation scenarios, such as autonomous vehicles, air traffic control systems, and traffic management systems. Explainable Artificial Intelligence (XAI) provides a clear, transparent explanation and actions. Traditional Machine Learning techniques have enabled Intelligent Transportation systems by performing centralized vehicular data training at the server where data sharing is needed, thus introducing privacy issues. To reduce transmission overhead and achieve privacy, a collaborative and distributed machine learning approach called Federated Learning (FL) is used. Here only model updates are transmitted instead of the entire dataset. This paper provides a comprehensive survey on the prediction of traffic using Machine Learning, Deep Learning, and FL. Among these, FL can predict traffic accurately without compromising privacy. We first present the overview of XAI and FL in the introduction. Then, we discuss the basic concepts of FL and its related work, the FL-IoMT framework, and motivations for using FL in transportation. Subsequently, we discuss the applications of using FL in transportation and open-source projects. Finally, we highlight several research challenges and their possible directions in FL. © 2024 Bentham Science Publishers.

关键词： Intelligent systems

来源：评论

学校读者我要写书评

暂无评论

A review of artificial intelligence based malware detection using deep learning

引用

Materials Today: Proceedings 2023年 80卷 2678-2683页

作者： Mustafa Majid, Al-Ani Alshaibi, Ahmed Jamal Kostyuchenko, Evgeny Shelupanov, Alexander Department of Complex Information Security of Computer Systems Faculty of Security Tomsk State University of Control Systems and Radioelectronics Tomsk634050 Russia

Malware propagation by adversaries has witnessed many issues across the globe. Often it is found that malware is released in different countries for monetary gains. With the proliferation of malware spreading activities, it is made possible that now we have malware patterns that are used for training machine learning models. Thus machine learning became indispensable for malware detection. The traditional machine learning models have limitations in performance as the training depth is limited. The emergence of deep learning models paved way for more training possibilities and improvement in detection accuracy with least false positives. This paper reviews literature on deep learning techniques that are used for malware detection. The deep learning methods used for malware detection include CNN, RNN, LSTM and auto encoders. LSTM is found to have memory in the cell to have better possibilities. Auto encoders are found to have better unsupervised approach with encoding and decoding to arrive at abnormalities (malware) detection. There are many contributions found using machine learning and deep learning towards Android malware detection. This paper provides knowledge that leads to further research in deep learning which is essential to improve the state of the art. © 2021

关键词： Malware

来源：评论

学校读者我要写书评

暂无评论

Deep Convolution Neural Networks for Image-Based Android Malware Classification

引用

computers, Materials & Continua 2025年第3期82卷 4093-4116页

作者： Amel Ksibi Mohammed Zakariah Latifah Almuqren Ala Saleh Alluhaidan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadhP.O.Box 11442Saudi Arabia

The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of *** resolve this problem,efficient and flexible malware detection tools are *** work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data ***,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:***,***,***,***,*** network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained *** addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional *** success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.

关键词： Android malware detection deep convolutional neural network(DCNN) image processing CIC-AndMal2017 dataset exploratory data analysis VGG16 model

来源：评论

学校读者我要写书评

暂无评论

Unboxing BA Use to Enhance Decision-Making Effectiveness - Role of Managerial Capabilities 30

Unboxing BA Use to Enhance Decision-Making Effectiveness - R...

引用

30th Americas Conference on information systems, AMCIS 2024

作者： Im, Ghiyoung P. Cho, Sunyoung Chung, Claris Computer Information Systems University of Louisville United States Information Systems University of Texas Rio Grande Valley United States Accounting & Information Systems University of Canterbury New Zealand

ISBN: (纸本)9798331307066

Business analytics (BA) has been highlighted over the past decade as a critical tool for improving organizations' decision-making. Despite a heavy research focus on the relationship between BA and firm performance, few studies have addressed dynamic managerial capabilities and how ambidextrous BA use contributes to better decision-making in organizations. To address these research gaps, the study proposes a theoretical model that investigates the antecedents and consequences of ambidextrous BA use. Dynamic managerial capabilities, as the antecedents, include paradoxical managerial cognition (integrative complexity and holistic thinking) and data literacy. © 2024 30th Americas Conference on information systems, AMCIS 2024. All rights reserved.

关键词： Ambidexterity Business Analytics Data Literacy Decision-Making effectiveness Managerial Capabilities

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：