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An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

Computers, Materials & Continua 2023年第9期76卷 3859-3876页

作者： Weizheng Wang Xiangqi Wang Xianmin Pan Xingxing Gong Jian Liang Pradip Kumar Sharma Osama Alfarraj Wael Said College of Information Science and Engineering HunanWomen’s UniversityChangsha410138China School of Mathematics and Statistics Hunan First Normal UniversityChangsha410138China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenAB243FXUK Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazig44511Egypt

Image-denoising techniques are widely used to defend against Adversarial Examples(AEs).However,denoising alone cannot completely eliminate adversarial *** remaining perturbations tend to amplify as they propagate through deeper layers of the network,leading to ***,image denoising compromises the classification accuracy of original *** address these challenges in AE defense through image denoising,this paper proposes a novel AE detection *** proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network(CNN)network *** used detector model integrates the classification results of different models as the input to the detector and calculates the final output of the detector based on a machine-learning voting *** analyzing the discrepancy between predictions made by the model on original examples and denoised examples,AEs are detected *** technique reduces computational overhead without modifying the model structure or parameters,effectively avoiding the error amplification caused by *** proposed approach demonstrates excellent detection performance against mainstream AE *** results show outstanding detection performance in well-known AE attacks,including Fast Gradient Sign Method(FGSM),Basic Iteration Method(BIM),DeepFool,and Carlini&Wagner(C&W),achieving a 94%success rate in FGSM detection,while only reducing the accuracy of clean examples by 4%.

关键词： Deep neural networks adversarial example image denoising adversarial example detection machine learning adversarial attack

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Efficient CNN-based disaster events classification using UAV-aided images for emergency response application

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Neural computing and Applications 2024年第18期36卷 10599-10612页

作者： Bashir, Munzir Hubiba Ahmad, Musheer Rizvi, Danish Raza El-Latif, Ahmed A. Abd Department of Computer Engineering Jamia Millia Islamia New Delhi110025 India EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Kom32511 Egypt

Natural disasters can be unpredictable and catastrophic. Even after the event, the repercussions are prolonged due to the incompetence of disaster management strategies. To mitigate the effects of a natural hazard, disaster management teams have to rapidly come forth with innovative plans of action. To contain the damage that a disaster causes, the response time, and preparedness of a disaster management team is crucial. Having a forewarning about the nature of the disaster can prove to be beneficial for the management team. Remote/unreachable areas such as deep parts of the forests, far-flung rural areas, oceans and other hard to reach locations are at a higher risk of receiving poorer aid and response due to the lack of communication and connectivity with the rest of the world. Our paper provides a solution for this particular problem by suggesting UAV/Drones for surveillance and monitoring in these inaccessible, disaster struck areas. The drones not only monitor the situation but constantly click pictures and send them to a base station, where the images are used to collect insights about the type of disaster that has to be dealt with. The paper proposes a deep learning model based on feature concatenation for classification of disasters which can be deployed at the base station, and can receive data in the form of images from a UAV/drone hovering over the affected place. The proposed model is efficient and able to achieve a higher accuracy as compared to the leading CNN models and closely related recent works as well. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Human resource management

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Smart Happy Device with IoT : For those who suffer anxiety, stress, and depression 12

Smart Happy Device with IoT : For those who suffer anxiety, ...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Yakub, Fitri Azizan, Azizul Dhamanti, Inge Khudzari, Ahmad Zahran Md. Govindan, Niranjana Muljono, Muljono UTM Malaysia-Japan International Institute of Technology Electronic System Engineering Department Kuala Lumpur Malaysia Universiti Teknologi Malaysia Cyberphysical Research Group Embedded AI & ML Science Lab Faculty of Artificial Intelligence Kuala Lumpur Malaysia Universitas Airlangga Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia UTM Faculty of Engineering Department of Biosciences & Medical Engineering Johor Bahru Malaysia SRMIST Faculty of Engineering & Technology Department of Computing Technologies Chennai India Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia

ISBN: (纸本)9798350340204

Due to the rising cases of Covid-19 in the world especially in Malaysia, the impact had been hit hard for many people. Due to this all people must be isolated in their home. It has affected the well-being and working life of millions of people across the world. Due to this disease, the mental health among people has increased significantly for the past 2 years. According to MoH, out of 983 Malaysian students, about 20.4%, 6.6%, and 2.8% are experienced minimal to moderate, marked to severe, and most extreme levels of anxiety, respectively. The main stressors are the remote online teaching, and uncertainty about the future regarding study and career which can affect mental health. Thus, the main objective of this project is to develop a product that may provide different medication methods to its users by helping people who suffer anxiety, stress, and depression. We used aromatherapy, positive quotes, and music therapy in order to release stress. The user can also check his or her pulse rate by only touching the fingerprint through the sensor. The microcontrollers used in this project are Arduino Uno and Arduino Mega to build the project circuit. The experimental results can improve body health, mind and spirit which enhances both physical and emotional health. © 2024 IEEE.

关键词： COVID-19

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Bangla Text Summarization Analysis Using Machine Learning: An Extractive Approach 1

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2nd International Human engineering Symposium, HUMENS 2023

作者： Rahman, Mizanur Debnath, Sajib Rana, Masud Murad, Saydul Akbar Muzahid, Abu Jafar Md Rashid, Syed Zahidur Gafur, Abdul Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia Department of Computer Science and Engineering United International University Dhaka Bangladesh Information and Communication Engineering Noakhali Science and Technology University Noakhali Bangladesh Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh

ISBN: (数字)9789819968909

ISBN: (纸本)9789819968893

A notable expansion of information in digital space provides quick access to these massive amounts of news, literature, and so on. Although maximum news is available in English, people are interested in reading in their language. Almost all Bangla newspapers now have their online version, so people prefer to read newspapers online. Without reading the whole news, it is not possible to obtain significant information and a summary from a news article. A news summary is a process of extracting the most significant information from a news article in a precise manner. In this case, we require an effective Bangla text summarizer tool to get insight from the news easily. Text summarizers use linguistic methods to generate and interpret text before uncovering natural concepts and phrases to represent the content by compressing the original text document into a shorter text that contains the most relevant information. Summarizing lengthy and important text by enforcing specific guidelines is a challenging task. Therefore, a method based on intelligent machine learning is widely anticipated in this content. This paper uses extractive automatic text summarization techniques to condense the source text into a shorter version by preserving the significant information of the original text. Here we have adopted two machine learning-based techniques (i.e., word2vec, and similarity matrix) and a rule-based technique (word count) for Bangla text summarization. The results show that the word2vec technique performs very well compared to the summary performed by other techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Galvanic Skin Response based Stress Detection System using Machine Learning and IoT 2

Galvanic Skin Response based Stress Detection System using M...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Raju, Ayalapogu Ratna Ramadevi, R. Babu, Padamata Ramesh Vetriselvi, D. Mahatma Gandhi Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India Koneru Lakshmaiah Educational Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur Chennai India

ISBN: (纸本)9798350325799

The primary objective of this study is to construct an Internet of Things (IoT)-based stress detection system that uses Galvanic Skin Response (GSR) and pulse sensors. In today's contemporary world, stress has developed into a widespread problem and early recognition of the condition is necessary for efficient intervention and treatment. The suggested system utilizes GSR and pulse sensors in conjunction with the IoT technologies to continuously monitor and analyze an individual's stress levels. The GSR sensor analyzes changes in the electrical conductance of the skin, which is known to occur during times of stress;in contrast, the pulse sensor identifies patterns in heart rate related to stress. Signal processing methods are used to process the newly collected sensor data, after which useful characteristics are retrieved. After the data has been analyzed, it is wirelessly sent to a cloud platform so that machine learning algorithms may be used for further analysis and categorization. The system displays the current amount of stress in real-time and can send warnings if the current stress level reaches a certain threshold. To guarantee that the system conforms to the rules, the system has established privacy and data security precautions. Our goal is to contribute, via this study, to the creation of an accurate and reliable stress detection system that may aid people in efficiently controlling the amounts of stress that they are experiencing in their lives. © 2023 IEEE.

关键词： Machine learning

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Hybrid Malware Variant Detection Model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

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Computers, Materials & Continua 2023年第9期76卷 3483-3498页

作者： Asma A.Alhashmi Abdulbasit A.Darem Sultan M.Alanazi Abdullah M.Alashjaee Bader Aldughayfiq Fuad A.Ghaleb Shouki A.Ebad Majed A.Alanazi Department of Computer Science Northern Border UniversityArar9280Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Information Systems College of Computer Science and InformationJouf UniversitySakakaAljoufSaudi Arabia School of Computing University Teknologi Malaysia81310 UTM Johor BahruJohor81310Malaysia Department of Computer and Electronic Engineering Sana’a Community CollegeSana’a5695Yemen

In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining *** paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware *** model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware *** model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware *** these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under *** outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final *** strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of *** efficacy of our proposed APIbased hybrid model is evident in its performance *** outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.

关键词： API-based hybrid malware detection model static and dynamic analysis malware detection

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Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

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Computer Systems science & engineering 2022年第9期42卷 1273-1287页

作者： S.Prakash P.Vishnu Raja A.Baseera D.Mansoor Hussain V.R.Balaji K.Venkatachalam Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer Science and Engineering Kongu Engineering CollegePerundurai638060India School of Computing Science and Engineering VIT Bhopal UniversityBhopal466114India Department of Computer Science and Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of ECE Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic

Urban living in large modern cities exerts considerable adverse effectson health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanizedcountries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples isbecoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce *** relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, theiterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated usingensemble nonlinear support vector machines and random forests. Thus, instead ofusing the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vectormachines, where the product rule is used to combine probability estimates ofdifferent classifiers. Performance is evaluated in terms of the prediction accuracyand interpretability of the model and the results.

关键词： Chronic disease classification iterative weighted map reduce machine learning methods ensemble nonlinear support vector machines random forests

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Optimization of Interactive Videos Empowered the Experience of Learning Management System

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Computer Systems science & engineering 2023年第7期46卷 1021-1038页

作者： Muhammad Akram Muhammad Waseem Iqbal M.Usman Ashraf Erssa Arif Khalid Alsubhi Hani Moaiteq Aljahdali Department of Computer Science Superior University Lahore54000Pakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Computer Science King Abdulaziz UniversityJeddahSaudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia

The Learning management system(LMS)is now being used for uploading educational content in both distance and blended *** platform has two types of users:the educators who upload the content,and the students who have to access the *** students,usually rely on text notes or books and video tutorials while their exams are conducted with formal *** assessments and examination criteria are ineffective with restricted learning space which makes the student tend only to read the educational contents and videos instead of interactive *** aim is to design an interactive LMS and examination video-based interface to cater the issues of educators and *** is designed according to Human-computer interaction(HCI)principles to make the interactive User interface(UI)through User experience(UX).The interactive lectures in the form of annotated videos increase user engagement and improve the self-study context of users involved in *** interface design defines how the design will interact with users and how the interface exchanges *** findings show that interactive videos for LMS allow the users to have a more personalized learning experience by engaging in the educational *** result shows a highly personalized learning experience due to the interactive video and quiz within the video.

关键词： User interface user experience learning management system linear nonlinear video interactive video visual design

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A Unified Hypergraph Framework for Inter and Intra-Session Dynamics in Session-Based Social Recommendations

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IEEE Transactions on Big Data 2025年

作者： Khan, Bilal Wu, Jia Yang, Jian Hayat, Malik Khizar Xue, Shan The School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia

Session-based recommendations have become increasingly important in social media platforms due to the dynamic and temporal nature of user interactions. The utilization of Graph Neural Networks in these systems has grown due to their proficiency in incorporating node information and structural topology. However, current recommendation methods that use graphs focus on recommendations within a single session, neglecting the more complex dependencies between different sessions. This omission limits improvements in the accuracy of recommendations. In addition, existing GNN-based approaches generally focus on simple binary connections, neglecting to capture the intricate and heterogeneous interactions in real-world situations. Furthermore, a notable obstacle arises from the absence of node positional information for hyperedges in hypergraphs. Therefore, different item orders can lead to identical hyperedges, which limits the formation of precise session vector representations. The paper proposes a unified framework utilizing heterogeneous hypergraph neural networks for session-based social recommendations to address these limitations. This framework utilizes hypergraphs to depict complex multivariate connections among sessions, social networks, and items. It addresses the problem of hyperedge ambiguity while maintaining the sequential order of data. The methodology entails creating a linkage graph and a session-item graph, which aid in identifying similar user intentions across various sessions and potential behavior patterns within a single session. In addition, the framework utilizes a Graph Attention Network (GAT) to combine social information from users and their connections, thereby improving the representation of user interests. Empirical assessments on three datasets show that our proposed model outperforms popular recommendation models. This emphasizes its effectiveness in accurately capturing user preferences and behaviors in session-based social recommendations.

关键词： Recommender systems

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Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

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Computers, Materials & Continua 2023年第2期74卷 2379-2395页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Electrocardiogram(ECG)signal is a measure of the heart’s electrical ***,ECG detection and classification have benefited from the use of computer-aided systems by *** goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification *** addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall *** prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing ***,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA *** results confirmed the superiority and effectiveness of the proposed *** classification accuracy achieved by the proposed approach is(99.98%).

关键词： Electrocardiogram differential evolution algorithm dipper throated optimization neural networks

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