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Correction: Agile meets quantum: a novel genetic algorithm model for predicting the success of quantum software development project

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Automated software engineering 2024年第1期32卷 1-11页

作者： Khan, Arif Ali Akbar, Muhammad Azeem Lahtinen, Valtteri Paavola, Marko Niazi, Mahmood Alatawi, Mohammed Naif Alotaibi, Shoayee Dlaim M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Quanscient Oy Tampere Finland VTT Technical Research Ctr of Finland Ltd Oulu Finland Department of Information and Computer Science King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Centre for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Information Technology Department Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Hail Hail Saudi Arabia

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FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

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Fire Ecology 2023年第1期19卷 1-19页

作者： Ahmad, Khubab Khan, Muhammad Shahbaz Ahmed, Fawad Driss, Maha Boulila, Wadii Alazeb, Abdulwahab Alsulami, Mohammad Alshehri, Mohammed S. Ghadi, Yazeed Yasin Ahmad, Jawad Faculty of Engineering and Technology Multimedia University Melaka Malaysia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Department of Cyber Security Pakistan Navy Engineering College NUST Karachi Pakistan Robotics and Internet of Things Laboratory Prince Sultan University Riyadh Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Software Engineering Computer Science Al Ain University Abu Dhabi United Arab Emirates

Forests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset. The test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with t

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Discovering Complex Correlations Among Multiple IoT Devices in Smart Environments

Discovering Complex Correlations Among Multiple IoT Devices ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Andrew D'Angelo Chenglong Fu Xiaojiang Du Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems University of North Carolina at Charlotte Charlotte NC USA Air Force Research Laboratory Rome NY USA

The ubiquity of the Internet of Things (IoT) in a vast range of consumer applications is unparalleled. Unfortunately, despite the benefits of IoT, its widespread integration comes with significant security challenges. Considering IoT devices' capability to interact with the physical environment, there is an urgent need for effective anomaly detection. The state-of-the-art anomaly detection method, HAWatcher, models the normal behaviors of smart homes with inter-device correlations and demonstrates great results. Nonetheless, it is limited to capturing only simple one-to-one correlations between two events or states, which undermines its capability to detect anomalies in more complicated environments. To address this issue, we present a novel correlation discovering method to mine complex two-to-one correlations in such complicated IoT-enabled environments. We conduct experiments over two weeks on four smart home testbeds and obtain 70 two-to-one correlations. The correlations are applied to 9 anomaly scenarios, which show significant improvements in detecting anomalies over one-to-one correlations.

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VoiceGuard: An Effective and Practical Approach for Detecting and Blocking Unauthorized Voice Commands to Smart Speakers

VoiceGuard: An Effective and Practical Approach for Detectin...

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International Conference on Dependable systems and Networks (DSN)

作者： Xuening Xu Chenglong Fu Xiaojiang Du E. Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems UNC Charlotte Charlotte NC USA Information Directorate Air Force Research Laboratory Rome NY USA

Smart speakers bring convenience to people's daily lives. However, various attacks can be launched against smart speakers to execute malicious commands, which may cause serious safety or security issues. The existing solutions against sophisticated attacks such as voice replay attacks and voice synthesis attacks require intrusive modifications of the smart speaker hardware and/or software, which are impractical for general users. In this work, we present a novel security scheme- VoiceGuard that can effectively detect and block unauthorized voice commands to smart speakers. VoiceGuard does not require any modification to smart speakers' hardware or software. We implement a prototype of VoiceGuard on two popular smart speakers: Amazon Echo Dot and Google Home Mini, and evaluate the scheme in three real-world testbeds, which include both single-user and multi-user scenarios. The experimental results show that VoiceGuard achieves an accuracy of 97% in blocking malicious voice commands issued by illegitimate sources while having a negligible impact on the user experience.

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A Melting Pot of Evolution and Learning

arXiv

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arXiv 2023年

作者： Sipper, Moshe Elyasaf, Achiya Halperin, Tomer Haramaty, Zvika Lapid, Raz Segal, Eyal Tzruia, Itai Tamam, Snir Vitrack Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva8410501 Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel DeepKeep Tel-Aviv Israel

We survey eight recent works by our group, involving the successful blending of evolutionary algorithms with machine learning and deep learning: 1. Binary and Multinomial Classification through Evolutionary Symbolic Regression, 2. Classy Ensemble: A Novel Ensemble Algorithm for Classification, 3. EC-KitY: Evolutionary Computation Tool Kit in Python, 4. Evolution of Activation Functions for Deep Learning-Based Image Classification, 5. Adaptive Combination of a Genetic Algorithm and Novelty Search for Deep Neuroevolution, 6. An Evolutionary, Gradient-Free, Query-Efficient, Black-Box Algorithm for Generating Adversarial Instances in Deep Networks, 7. Foiling Explanations in Deep Neural Networks, 8. Patch of Invisibility: Naturalistic Black-Box Adversarial Attacks on Object Detectors. Copyright © 2023, The Authors. All rights reserved.

关键词： Genetic programming

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Hybrid Convolutional Neural Networks for Enhanced Detection of Mango Leaf Diseases

Hybrid Convolutional Neural Networks for Enhanced Detection ...

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Shweta Bhattacharjee Porna Md Firoz Kabir Md Imran Chowdhury Rana Md Saiful Islam Sajol Tonmoy Roy Mohammad Aman Ullah Khan Md Al Adnan Girigula Durga Bhavani Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh Master’s in Information Technology University of the Cumberlands USA Bachelor of Business Administration International American university Los Angeles California USA Department of Computer Science and Engineering Louisiana State University Louisiana USA Data Analytics & Information Systems Utah State University Logan USA Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Bsc in software engineering Noakhali Science And Technology University School of Computer Science & Engineering (SCOPE) VIT-AP University Amaravati Andhra Pradesh India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

The classification of mango leaf diseases is critical for effective disease management and ensuring high-quality yields in mango cultivation. This paper presents a comprehensive study on using deep learning techniques to classify various mango leaf diseases, leveraging convolutional neural networks (CNNs) and hybrid models. A total of 7,524 images were used in our study. These included 4,000 training samples and 3,524 testing samples. The images were split into eight groups, which were powdery mildew, cutting weevil, anthracnose, bacterial canker, sooty mold, gall midge, healthy, and die back. The suggested method starts with feature extraction using VGG19 and MobileNetB1, then classification using both standalone models (ResNet50V2 + EfficientNetB1 and VGG16 + MobileNetB1). We employed data augmentation techniques like random brightness adjustment, rotation, and flipping to enhance the robustness of the model. We conducted hyperparameter tuning using hyperband and Bayesian optimization to optimize the model’s performance. Experimental results demonstrate that the hybrid models achieved superior performance, with ResNet50V2 and EfficientNetB1 attaining a perfect accuracy of $100 \%$ on the test set. These findings highlight the potential of deep learning techniques to improve the accuracy and reliability of mango leaf disease diagnosis, contributing significantly to the advancement of precision agriculture.

关键词： Deep learning Training Precision agriculture Accuracy Robustness Convolutional neural networks Tuning Optimization Diseases Testing

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Application of Artificial Intelligence in Rock Tunnel engineering: A Survey on Where and How

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Expert systems 2025年第7期42卷

作者： Yu, Xiaojie He, Ben-Guo Xu, Xu Zhou, Yicong Diaz, Miguel A. Chen, Junxin Camacho, David School of Software Dalian University of Technology Dalian China Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines Northeastern University Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China Department of Computer and Information Science University of Macau Macao Department of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

Rock tunnel engineering (RTE) plays a crucial role in modern infrastructure development. The development of artificial intelligence (AI) is able to drive transformative advances in RTE. This review provides an in-depth analysis of the AI application in RTE. Through a comprehensive examination of existing literature, we explore how AI technologies have revolutionised various aspects of RTE, including construction methodology, rock parameter estimation, hazard disaster management during construction, and tunnel operation. In addition, we provide an in-depth study of the synergies between various AI algorithms and related open datasets. This work also outlines promising future research directions for the AI application in RTE, aiming to inspire further advancements in this emerging field. In conclusion, this review underscores the positive influence of AI on RTE, emphasising its capacity to elevate efficiency, accuracy, and safety standards throughout various phases of tunnel projects. The convergence of AI with RTE holds immense promise for advancing the field and ensuring the success and sustainability of future tunnel infrastructure endeavours. © 2025 John Wiley & Sons Ltd.

关键词： artificial intelligence construction methodology rock tunnel engineering tunnel operation

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Blockchain-Enhanced Framework for Secure Third-Party Vendor Risk Management and Vigilant Security Controls

Blockchain-Enhanced Framework for Secure Third-Party Vendor ...

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IEEE International Conference on Big Data

作者： Deepti Gupta Lavanya Elluri Avi Jain Shafika Showkat Moni Omer Aslan Dept. of Computer Information Systems Texas A&M University - Central Texas Texas USA College of Business University of Tampa Florida USA Dept. of Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Florida USA Department of Software Engineering Bandırma Onyedi Eylül University Bandırma Turkey

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In an era of heightened digital interconnectedness, businesses increasingly rely on third-party vendors to enhance their operational capabilities. However, this growing dependency introduces significant security risks, making it crucial to develop a robust framework to mitigate potential vulnerabilities. This paper proposes a comprehensive secure framework for managing third-party vendor risk, integrating blockchain technology to ensure transparency, traceability, and immutability in vendor assessments and interactions. By leveraging blockchain, the framework enhances the integrity of vendor security audits, ensuring that vendor assessments remain up-to-date and tamperproof. This proposed framework leverages smart contracts to reduce human error while ensuring real-time monitoring of compliance and security controls. By evaluating critical security controls—such as data encryption, access control mechanisms, multi-factor authentication, and zero-trust architecture—this approach strengthens an organization’s defense against emerging cyber threats. Additionally, continuous monitoring enabled by blockchain ensures the immutability and transparency of vendor compliance processes. In this paper, a case study on iHealth’s transition to AWS Cloud demonstrates the practical implementation of the framework, showing a significant reduction in vulnerabilities and marked improvement in incident response times. Through the adoption of this blockchain-enabled approach, organizations can mitigate vendor risks, streamline compliance, and enhance their overall security posture. Our findings highlight the importance of employing blockchain to enforce security controls and maintain compliance with healthcare regulations such as HIPAA. In this paper, we present a comprehensive set of security controls and demonstrate how blockchain technology enhances their effectiveness, ensuring greater transparency, accountability, and automation in vendor assessments. By reducing human error, enabl

关键词： Multi-factor authentication Smart contracts Organizations Real-time systems Regulation Blockchains Security Time factors Risk management Resilience

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Rain Predictive Model using Machine learning Techniques

Rain Predictive Model using Machine learning Techniques

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Multi-disciplinary Conference in Emerging Research Trends (IMCERT), International

作者： Muhammad Shahbaz Muneer Syed Muhammad Nabeel Mustafa Syeda Sundus Zehra Haniya Maqsood Dpertment of Computational Finance NED University of Engineering and Technology Karachi Pakistan Dept. of Computer Science & I.T NED University of Engineering and Technology Karachi Pakistan Dept. of Software Engineering Sir Syed Uuniversity of Engineering and Technology Karachi Pakistan Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

Climate is rapidly changing around the world. Over time, there have been significant changes in the weather. Rainfall is now erratic due to climate change. The frequency of extreme weather events like droughts and floods has increased due to climate change, necessitating the need for more precise and timely rainfall forecasts. For strategic reasons including agriculture, water resource management, and architectural design, rain forecasting is crucial. The naturally occurring non-stationary component in the rainfall time series impairs model performance for practical hydrologists and drought risk assessors. We present a rain predicting model based on machine learning to address the forecasting issue. In our work, we predict the possibility of rain the next day on the basis of last 10 years' data. The variables that were calculated during the experiments were humidity, pressure, evaporation, sunshine, rainfall, and so on. Random Forest gave the 90% accuracy with 0.904 Area under Curve, highest out of all the algorithms. The model's performance will significantly aid in the rain forecast.

关键词： Climate change Rain Machine learning algorithms Droughts Weather forecasting Predictive models Prediction algorithms

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Surface Recognition from Wheelchair-induced Noisy Vibration Data: A Tale of Many Cities

Surface Recognition from Wheelchair-induced Noisy Vibration ...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Rochishnu Banerjee Md Fourkanul Islam Shaswati Saha Vaskar Raychoudhury Md Osman Gani Department of Computer Science & Software Engineering Miami University Oxford OH USA Department of Information Systems University of Maryland Baltimore County Baltimore MD USA

Despite the active legislation in many countries supporting the accessibility of public spaces by mobility-impaired users, the reality is far from ideal. Wheelchair users often struggle to navigate the built environment let alone the natural areas. While barriers to wheeled mobility can be caused by broken/uneven surfaces, steep slopes, and unfavorable weather conditions, the effects of many such factors and others are not properly investigated. In this paper, we aim to classify various built and natural surfaces through their characteristic vibration patterns using different deep learning algorithms. The surface vibration data is collected from various cities in Europe (including Paris (FR), Mannheim (DE), Dresden (DE), Munich, Nuremberg (DE), and Salzburg (AT)) while a user drives a manual wheelchair attached with three differently oriented smartphones placed at different heights. Extensive experiments show that a Deep Neural Network model classifies surfaces using a denoised dataset with a 98.9% accuracy which is significantly higher than our previous state-of-the-art.

关键词： Vibrations Deep learning Navigation Wheelchairs Urban areas Neural networks Sensors

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