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arXiv 2024年

作者： Neves, Christopher Zeng, Yong Xiao, Yiming Department of Computer Science and Software Engineering Concordia University MontrealQC Canada Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

Parkinson’s disease (PD) is a debilitating neurodegenerative disease that has severe impacts on an individual’s quality of life. Compared with structural and functional MRI-based biomarkers for the disease, electroencephalography (EEG) can provide more accessible alternatives for clinical insights. While deep learning (DL) techniques have provided excellent outcomes, many techniques fail to model spatial information and dynamic brain connectivity, and face challenges in robust feature learning, limited data sizes, and poor explainability. To address these issues, we proposed a novel graph neural network (GNN) technique for explainable PD detection using resting state EEG. Specifically, we employ structured global convolutions with contrastive learning to better model complex features with limited data, a novel multi-head graph structure learner to capture the non-Euclidean structure of EEG data, and a head-wise gradient-weighted graph attention explainer to offer neural connectivity insights. We developed and evaluated our method using the UC San Diego Parkinson’s disease EEG dataset, and achieved 69.40% detection accuracy in subject-wise leave-one-out cross-validation while generating intuitive explanations for the learnt graph topology. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

STEP UP: Systematically Motivating the Children with Low Psychological Maturity Level and Disabled Children using Gamification and Human Computer Interaction 7

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STEP UP: Systematically Motivating the Children with Low Psy...

7th IEEE International conference for Convergence in Technology, I2CT 2022

作者： Dharmarathne, R.S.C.K. Medagedara, K.A. Madhubashinee, N.B.W.N. Maitipe, P.T. Sriyaratna, Disni Abeywardena, Kavinga Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9781665421683

Children are the future of this world. Therefore, teaching them to have a better future is very important. Also, as the adults we have to motivate them to overcome the obstacles and challenges they face throughout their lifetime. When considering about the children, there are various types of children in our society. As examples there are children with special needs and there are children who are mentally and physically stable. Children with special needs require special attention than the other children. These kinds of children with special needs have various types of development disabilities. They are children with low psychological maturity level, autism, down syndrome, genetic disorders etc. We have proposed a system to motivate these children who are with low psychological maturity level named as 'STEP-UP'. This system is a combination of four individual modules that have the common goal to motivate these kinds of children which were implemented using gamification, Image processing, machine learning and Human Computer Interaction. One individual module is focused on disabled children, and it will motivate those children using gamification. Another two modules are focusing on the children who are with low psychological maturity level. And that will motivate those kinds of children using gamification and HCI based technologies like Virtual Reality. The other module is a protocol to secure the data sent between the system and the database. The common goal of this overall STEP-UP system is to motivate the children with low psychological maturity level and disabled children. © 2022 IEEE.

关键词： Machine learning

Classification of Helpful and Unhelpful Online Customer Reviews Using XLNet and BERT Variants 1

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International Conference on Green Energy, Computing, and Intelligent Technology, GEn-CITy 2023

作者： Bilal, Muhammad Arshad, Muhammad Haseeb Ramzan, Muhammad Department of Computing and Information Systems School of Engineering and Technology Sunway University Petaling Jaya Malaysia Department of Computer Science National University of Computer and Emerging Sciences Chiniot-Faisalabad Campus Chiniot Pakistan Department of Software Engineering University of Sargodha Sargodha Pakistan School of Electronics and Computer Science University of Southampton Malaysia Iskandar Puteri Malaysia

ISBN: (数字)9789819998333

ISBN: (纸本)9789819998326

The majority of businesses have made public appearances on various social media platforms as a result of recent advances in e-commerce and the popularity of social media websites. Customers can share their experiences in the form of online customer reviews, which can assist potential customers in determining the quality of a company and making purchasing decisions. Due to the large volume of published reviews, it becomes difficult for customers to read all of the reviews and assess the quality of the business, resulting in the problem of information overload. Several solutions have been proposed in the literature by researchers using statistical and machine learning techniques to predict the helpfulness of online customer reviews. However, most of the existing solutions are based on the use of various business, review, and reviewer features, which lead to generalizability issues. Moreover, very limited studies have examined the effectiveness of state-of-the-art pre-trained language models for the classification of helpful and unhelpful reviews. Therefore, this study aims to examine the effectiveness of XLNet, Albert, DistilBert, and Roberta for review helpfulness prediction using textual features. The models were fine-tuned using a publicly available dataset of Yelp reviews. The results showed that XLNet achieved the highest F1-score of 0.730 compared to a benchmark of 0.717 achieved by the BERT base model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Sales

Hands and Palms Recognition by Transfer Learning for Forensics: A Comparative Study 23

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Hands and Palms Recognition by Transfer Learning for Forensi...

23rd International Conference on New Trends in Intelligent software Methodologies, Tools and Techniques, SoMeT 2024

作者： Fattahi, Jaouhar Fkiri, Obeb Mejri, Mohamed Ghayoula, Ridha Department of Computer Science and Software Engineering Laval University 2325 rue de l'Université QuebecQCG1V 0A6 Canada Department of Computer Science National School of Engineers of Carthage University of Carthage Tunis Tunisia Faculty of Engineering Information Systems Department University of Moncton New Brunswick Canada

ISBN: (纸本)9781643685380

In the realm of forensic science, precise identification of individuals holds paramount importance in both investigative procedures and legal proceedings. Hands and palms recognition has emerged as a valuable biometric modality within forensic applications, owing to the distinct and intricate features inherent to these anatomical regions. The elaborate patterns of veins, creases, and ridges present on palms and fingers serve as rich sources of biometric data, crucial for accurate identification purposes. Furthermore, given the frequent involvement of hands and palms in criminal activities such as theft and assault, their recognition becomes imperative for establishing links between suspects and crime scenes. However, developing robust recognition systems tailored for forensic applications poses notable challenges, including variations in hand poses, lighting conditions, and image quality. To address these hurdles, sophisticated deep learning techniques, notably transfer learning, have been employed. By harnessing pre-trained deep learning models namely NasNetLarge, NasNetMobile, and EfficientNet, initially trained on expansive datasets for general image recognition tasks, we can adapt these models to the specific task of hands and palms recognition in forensic contexts. Our findings reveal that all three models consistently achieved over 92% accuracy across all metrics evaluated, demonstrating their efficacy as strong contenders for the hands-and-palms recognition task. Notably, the EfficientNet model exhibited superior performance compared to its counterparts, boasting more than 95.8% accuracy, precision, F1-score and recall, along with more than 98.6% specificity and 99.4% AUC. © 2024 IOS Press. All rights reserved.

关键词： Palmprint recognition

A Comparative Analysis of Artificial Intelligence and Business Intelligence Using Big Data Analytics 1

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A Comparative Analysis of Artificial Intelligence and Busine...

1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Marella, Bhagath Chandra Chowdari Vegineni, Gopi Chand Addanki, Sireesha Ellahi, Ehsan Arun Kumar, K. Mandal, Rishabh Insights & Data Department of Financial Services Capgemini India Enrollment and Eligibility Team India System soft Technologies LLC India Cleveland State University Department of Information Systems OH United States Tula's Institute Department of Computer Science and Engineering UK Dehradun India India

ISBN: (纸本)9798331518578

Business intelligence (BI) encompasses the tools and uses to gather, combine, examine, and display data about a specific firm. Using historical and current efficiency comparisons, these structures assist in formulating plans and choices to achieve better outcomes in the future. Additionally, it helps firms determine which business issues and industry trends require attention. It has several benefits, such as increasing responsibility and efficiency, but it carries some disadvantages, such as being expensive, complicated, and time-consuming to adopt. Using big data-driven approaches to optimize talent recruitment is essential for improved decisions in the cutthroat world of BI. The efficacy of many machine learning algorithms to increase recruiting performance and accuracy is compared in this investigation. For optimizing, cross-validating, and hyperparameter modification were employed. The Random Forest (RF) algorithm has the greatest accuracy (92.9%), trailed by the Gradient Boosting Classifying system (92.6%) and the neural network (NNs) models (92.7%). These findings imply that sophisticated ML algorithms, in particular RF and NN, can greatly improve BI platforms' hiring procedures. Recruiters can benefit from this research's insightful recommendations for incorporating advanced ML methods into talent recruitment plans. © 2025 IEEE.

关键词： Data accuracy

Enhanced Breast Cancer Relapse Prediction using Ensemble Learning Techniques

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Enhanced Breast Cancer Relapse Prediction using Ensemble Lea...

Sustainable Expert systems (ICSES), International Conference on

作者： Mukesh Kumar Tripathi Meka Ananya V.N.L.N Murthy Tumma Shashank Jale Subash Reddy Kaushal Attaluri Department of Computer Science & Engineering Vardhaman college of Engineering Hyderabad Department of Information Technology Vardhaman college of Engineering Hyderabad Software Engineer (Machine Learning/AI Team) Pegasystems Worldwide India Pvt Ltd Hyderabad

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Breast cancer remains a significant health concern for women, and accurate prediction of recurrence is vital for effective treatment and improved outcomes. This research investigates the application of advanced ensemble learning techniques to enhance the reliability of breast cancer recurrence predictions. By combining Deep Neural Networks (DNN) and Artificial Neural Networks (ANN) with traditional machine learning methods, the proposed approach demonstrates significant improvements in accuracy, precision, sensitivity, specificity, F1-scores, and areas under the curve (AVCs). The study utilizes two breast cancer relapse datasets, UMCIO and WPBC, to evaluate the performance of the ensemble learning models. The results highlight the effectiveness of the proposed approach in accurately predicting breast cancer recurrence. This research contributes to the advancement of breast cancer diagnosis and treatment by providing a valuable tool for clinicians to assess the risk of recurrence and tailor treatment plans accordingly.

关键词： Deep learning Accuracy Sensitivity Artificial neural networks Predictive models Prediction algorithms Breast cancer Robustness Ensemble learning Medical diagnostic imaging

Modeling and Analysis of UAV-Assisted Mobile Network with Imperfect Beam Alignment

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Computers, Materials & Continua 2023年第1期74卷 453-467页

作者： Mohamed Amine Ouamri Reem Alkanhel Cedric Gueguen Manal Abdullah Alohali Sherif S.M.Ghoneim University of Rennes 1/ADOPNET team at IRISA laboratory Rennes35000France Facultéde Technologie Laboratoire d’Informatique Médicale(LIMED)Universitéde BejaiaBejaia06000Algérie Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Electrical engineering department College of EngineeringTaif University P.O.Box 11099Taif21944Saudi Arabia

With the rapid development of emerging 5G and beyond(B5G),Unmanned Aerial Vehicles(UAVs)are increasingly important to improve the performance of dense cellular *** a conventional metric,coverage probability has been widely studied in communication systems due to the increasing density of users and complexity of the heterogeneous *** recent years,stochastic geometry has attracted more attention as a mathematical tool for modeling mobile network *** this paper,an analytical approach to the coverage probability analysis of UAV-assisted cellular networks with imperfect beam alignment has been *** assumption was considered that all users are distributed according to Poisson Cluster Process(PCP)around base stations,in particular,Thomas Cluster Process(TCP).Using thismodel,the impact of beam alignment errors on the coverage probabilitywas ***,the ProbabilityDensity Function(PDF)of directional antenna gain between the user and its serving base station was ***,association probability with each tier was achieved.A tractable expression was derived for coverage probability in both Line-of-Sight(LoS)andNon-Line-of-Sight(NLoS)condition *** results demonstrated that at low UAVs altitude,beam alignment errors significantly degrade coverage ***,for a small cluster size,alignment errors do not necessarily affect the coverage performance.

关键词： Unmanned aerial vehicles coverage analysis stochastic geometry millimeter wave imperfect beam alignment

IoT Device Identification Based on Network Communication Analysis Using Deep Learning

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arXiv 2023年

作者： Kotak, Jaidip Elovici, Yuval Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

Attack vectors for adversaries have increased in organizations because of the growing use of less secure IoT devices. The risk of attacks on an organization’s network has also increased due to the bring your own device (BYOD) policy which permits employees to bring IoT devices onto the premises and attach them to the organization’s network. To tackle this threat and protect their networks, organizations generally implement security policies in which only white-listed IoT devices are allowed on the organization’s network. To monitor compliance with such policies, it has become essential to distinguish IoT devices permitted within an organization’s network from non-white-listed (unknown) IoT devices. In this research, deep learning is applied to network communication for the automated identification of IoT devices permitted on the network. In contrast to existing methods, the proposed approach does not require complex feature engineering of the network communication, because the’communication behavior’ of IoT devices is represented as small images which are generated from the device’s network communication payload. The proposed approach is applicable for any IoT device, regardless of the protocol used for communication. As our approach relies on the network communication payload, it is also applicable for the IoT devices behind a network address translation (NAT) enabled router. In this study, we trained various classifiers on a publicly accessible dataset to identify IoT devices in different scenarios, including the identification of known and unknown IoT devices, achieving over 99% overall average detection accuracy. © 2023, CC BY.

关键词： Internet of things

D-Score: An Expert-Based Method for Assessing the Detectability of IoT-Related Cyber-Attacks

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arXiv 2023年

作者： Meidan, Yair Benatar, Daniel Bitton, Ron Avraham, Dan Shabtai, Asaf The Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

IoT devices are known to be vulnerable to various cyber-attacks, such as data exfiltration and the execution of flooding attacks as part of a DDoS attack. When it comes to detecting such attacks using network traffic analysis, it has been shown that some attack scenarios are not always equally easy to detect if they involve different IoT models. That is, when targeted at some IoT models, a given attack can be detected rather accurately, while when targeted at others the same attack may result in too many false alarms. In this research, we attempt to explain this variability of IoT attack detectability and devise a risk assessment method capable of addressing a key question: how easy is it for an anomaly-based network intrusion detection system to detect a given cyber-attack involving a specific IoT model? In the process of addressing this question we (a) investigate the predictability of IoT network traffic, (b) present a novel taxonomy for IoT attack detection which also encapsulates traffic predictability aspects, (c) propose an expert-based attack detectability estimation method which uses this taxonomy to derive a detectability score (termed 'D-Score') for a given combination of IoT model and attack scenario, and (d) empirically evaluate our method while comparing it with a data-driven method. © 2023, CC BY.

关键词： Internet of things

IC-SECURE: Intelligent System for Assisting Security Experts in Generating Playbooks for Automated Incident Response

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arXiv 2023年

作者： Kremer, Ryuta Wudali, Prasanna N. Elovici, Yuval Shabtai, Asaf Momiyama, Satoru Araki, Toshinori Furukawa, Jun Department of Software and Information Systems Engineering Ben-Gurion University of the Negev NEC Corporation Japan

Security orchestration, automation, and response (SOAR) systems ingest alerts from security information and event management (SIEM) system, and then trigger relevant playbooks that automate and orchestrate the execution of a sequence of security activities. SOAR systems have two major limitations: (i) security analysts need to define, create and change playbooks manually, and (ii) the choice between multiple playbooks that could be triggered is based on rules defined by security analysts. To address these limitations, recent studies in the field of artificial intelligence for cybersecurity suggested the task of interactive playbook creation. In this paper, we propose IC-SECURE, an interactive playbook creation solution based on a novel deep learning-based approach that provides recommendations to security analysts during the playbook creation process. IC-SECURE captures the context in the form of alert data and current status of incomplete playbook, required to make reasonable recommendation for next module that should be included in the new playbook being created. We created three evaluation datasets, each of which involved a combination of a set of alert rules and a set of playbooks from a SOAR platform. We evaluated IC-SECURE under various settings, and compared our results with two state-of-the-art recommender system methods. In our evaluation IC-SECURE demonstrated superior performance compared to other methods by consistently recommending the correct security module, achieving precision@1 > 0.8 and recall@3 > 0.92. Copyright © 2023, The Authors. All rights reserved.

关键词： Integrated circuits