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

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

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Recent Trends in Compact Planar Antennas at 5G Sub-6 GHz and mm Wave Frequency Bands for Automotive Wireless Applications: A Review

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Progress In Electromagnetics Research C 2024年 143卷 169-180页

作者： Kumar, Ashish Aljaidi, Mohammad Singh, Manpreet Alshammari, Mohammed Alsuwaylimi, Amjad A. Alenezi, Sami M. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Faculty of Information Technology Zarqa University Zarqa13110 Jordan Department of Computer Sciences Faculty of Computing and Information Technology Northern Border University Rafha91911 Saudi Arabia Department of Information Technology College of Computing and Information Technorlogy Northern Border University Arar91431 Saudi Arabia

5G wireless communication offers higher channel capacity, high data rate, sufficient bandwidth, enhanced coverage, and reliable link as compared to the previous generation mobile networks. Also, 5G becomes more relevant with the recent launches of low earth orbit satellites by various ventures like starlink, amazon, one web, etc. As these satellites have heights up to the range from 300 km to 1000 km, the free space path loss decreases drastically which in turn improves the signal reliability and efficient communication at dead spots. With these advancements, antenna researchers have the freedom to design compact, easy to manufacture, with adequate gain user equipment terminal antennas which can be easily integrated in the modern passenger car body. This paper will focus on the recent development in the design considerations of the compact antenna design at sub-6 GHz (n78 frequency band) and mm wave (n278 frequency band) according to 3gpp standards. This manuscript also discusses various design specifications like selection of material for antennas, design complexity, feeding methods, fabrication and measurement challenges and performance parameters which include reflection coefficient, gain, polarization, axial ratio, cross polarization discrimination, radiation efficiency, radiation pattern shape, etc. The proposed antenna design considerations will facilitate the possible integration into the various parts of the car body according to the recent vehicular applications for uninterrupted communication. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Radiation efficiency

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Efficient Authentication Scheme for UAV-Assisted Mobile Edge Computing

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computers, Materials & Continua 2023年第5期75卷 2727-2740页

作者： Maryam Alhassan Abdul Raouf Khan Department of Computer Science College of Computer Science and Information TechnologyKing Faisal UniversityAlahssa31982Kingdom of Saudi Arabia

Preserving privacy is imperative in the new unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC)architecture to ensure that sensitive information is protected and kept secure throughout the ***,efficiency must be considered while developing such a privacy-preserving scheme because the devices involved in these architectures are resource *** study proposes a lightweight and efficient authentication scheme for *** proposed scheme is a hardware-based password-less authentication mechanism that is based on the fact that temporal and memory-related efficiency can be significantly improved while maintaining the data security by adopting a hardwarebased solution with a simple *** proposed scheme works in four stages:system initialization,EU registration,EU authentication,and session *** is implemented as a single hardware chip comprising registers and XOR gates,and it can run the entire process in one clock ***,the proposed scheme has significantly higher efficiency in terms of runtime and memory consumption compared to other prevalent methods in the *** are conducted to evaluate the proposed authentication *** results show that the scheme has an average execution time of 0.986 ms and consumes average memory of 34 *** hardware execution time is approximately 0.39 ns,which is a significantly less than the prevalent schemes,whose execution times range in ***,the security of the proposed scheme is examined,and it is resistant to brute-force *** 1.158×10^(77) trials are required to overcome the system’s security,which is not feasible using fastest available processors.

关键词： MEC UAVs drones security efficiency authentication hardware-based solution password-less authentication

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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Optimized AI and IoT-Driven Framework for Intelligent Water Resource Management

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IEEE Access 2025年 13卷 97628-97646页

作者： Rokaya, Mahmoud Hemdan, Dalia I. Alajmani, Samah Hazzaa Alyami, Raneem Yousif Elmarhomy, Ghada Hashim, Hasan Atlam, El-Sayed College of Computers and Information Technology Taif University Department of Information Technology P.O. Box 11099 Taif21944 Saudi Arabia Taif University Faculty of Science Department of Food Science and Nutrition P.O. Box 11099 Taif21944 Saudi Arabia College of Computer Science and Engineering Taibah University Department of Information Systems Saudi Arabia Tanta University Department Computer Science Faculty of Science Tanta31527 Egypt Taibah University College of Computer Science and Engineering Department of Computer Science Yanbu6421 Saudi Arabia

The scheme of water resources management is a necessity for reducing water scarcity in arid areas and improving water availability in general [1]. However, water leak detection and irrigation scheduling traditional AI models are often computationally intensive and require complex hyperparameter tuning, making them less scalable. This study presents an artificial intelligence-based optimization framework that improves forecasting accuracy, computational speed, and real-time adaptability. The architecture combines the ensemble-learning algorithms (XGBoost, LightGBM), hybrid AIs (XGBoost + Autoencoder), and metaheuristic feature selection (GA, PSO, SA) for making intelligent decisions. Moreover, ontology-based feature structuring enhances interpretability, while hyperparameter tuning (GridSearchCV, Bayesian Optimization) and model compression techniques (pruning, quantization, knowledge distillation) ensure computational efficiency. A large number of experiments on real-world IoT sensor data testify to the effectiveness of the framework. It achieves 0.992 AUC-ROC scores for leak detection, an RMSE of 0.227 hours for irrigation scheduling, and an overall accuracy of 94.8%. Additional performance measures comprise precision (89.0%), recall (95.2%), F1-score (0.92), and inference speed (0.003 ms/sample). Although quantization has reduced the computational overhead, we still see a 13.02% increase in the model size as seen in Experiment 6, leading to a trade-off that needs to be optimized further. This study offers a deployable AI-based model for sustainable water management by tackling the issues of scalability, computational cost, and limitations in benchmark evaluation. By virtue of the empirical validation and comparative analysis of the framework, it has been shown to perform better than the regular methods, proving that the methodology can act as a step forward in the field of real-time, AI-assisted irrigation and leak detection systems. © 2013 IEEE.

关键词： Resource allocation

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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Healthcare Privacy-Preserving Federated Transfer Learning using CKKS-Based Homomorphic Encryption and PYHFEL Tool

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2024年第3期5卷 473-488页

作者： Al-Janabi, Anmar A. Al-Janabi, Sufyan Al-Khateeb, Belal Computer Science Department University of Technology – Iraq Baghdad10066 Iraq University of Anbar Ramadi Iraq College of Computer Science & Information Technology Anbar Ramadi31001 Iraq

Digitization of healthcare data has shown an urgent necessity to deal with privacy concerns within the field of deep learning for healthcare organizations. A promising approach is federated transfer learning, enabling medical institutions to train deep learning models collaboratively through sharing model parameters rather than raw data. The objective of this research is to improve the current privacy-preserving federated transfer learning systems that use medical data by implementing homomorphic encryption utilizing PYthon for Homomorphic Encryption Libraries (PYFHEL). The study leverages a federated transfer learning model to classify cardiac arrhythmia. The procedure begins by converting raw Electrocardiogram (ECG) scans into 2-D ECG images. Then, these images are split and fed into the local models for extracting features and complex patterns through a finetuned ResNet50V2 pre-trained model. Optimization techniques, including real-time augmentation and balancing, are also applied to maximize model performance. Deep learning models can be vulnerable to privacy attacks that aim to access sensitive data. By encrypting only model parameters, the Cheon-Kim-Kim-Song (CKKS) homomorphic scheme protects deep learning models from adversary attacks and prevents sensitive raw data sharing. The aggregator uses a secure federated averaging method that averages encrypted parameters to provide a global model protecting users’ privacy. The system achieved an accuracy rate of 84.49% when evaluated using the MIT-BIH arrhythmia dataset. Furthermore, other comprehensive performance metrics were computed to gain deeper insights, including a precision of 72.84%, recall of 51.88%, and an F1-score of 55.13%, reflecting a better understanding of the adopted framework. Our findings indicate that employing the CKKS encryption scheme in a federated environment with transfer cutting-edge technology achieves relatively high accuracy but at the cost of other performance metrics, which is lower

关键词： Federated learning

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Blockchain-Driven Secure Data Sharing Framework for Edge Computing Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 978-997页

作者： Fuad A.M.Al-Yarimi Ramzi Salah Khaled Mohamoud Department of Computer Science King Khalid UniversityMuhayel Aseer 62529Saudi Arabia Faculty of Information Science and Technology University Kebangassan MalaysiaBangi 43600Malaysia Department of Information System King Khalid UniversityMuhayel Aseer 62529Saudi Arabia

This study examines secure and effective data sharing methods for edge computing *** methods of sharing data at the edge have issues with security,speed,and *** goal is to develop a Blockchain-based Secure Data Sharing Framework(BSDSF)capable of improving data integrity,latency,and overall network efficiency for edge-cloud computing *** proposes using blockchain technology with Byzantine Fault Tolerance(BFT)and smart contract-based validation as a new method of secure data *** has a two-tiered consensus protocol to meet the needs of edge computing,which requires instantaneous *** employs Byzantine fault tolerance to deal with errors and protect against *** contracts automate validation and consensus operations,while edge computing processes data at the attack *** validation and failure detection methods monitor network quality and dependability,while system security ensures secure communication between *** is an important step toward digital freedom and trust by protecting security and improving transaction *** framework demonstrates a reduction in transaction latency by up to 30％and an increase in throughput by 25％compared to traditional edge computing models,positioning BSDSF as a pivotal solution for fostering digital freedom and trust in edge computing environments.

关键词： Byzantine Fault Tolerance(BFT) blockchain-driven edge computing networks consensus contract data validation contract node validation contract

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DeepOCL:A deep neural network for Object Constraint Language generation from unrestricted nature language

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CAAI Transactions on Intelligence technology 2024年第1期9卷 250-263页

作者： Yilong Yang Yibo Liu Tianshu Bao Weiru Wang Nan Niu Yongfeng Yin School of Software Beihang UniversityBeijingChina College of Computer Science and Technology Guizhou UniversityGuiyangGuizhouChina Faculty of Information Technology Beijing University of TechnologyBeijingChina Department of Electrical Engineering and Computer Sciences University of CincinnatiCincinnatiOhioUSA

Object Constraint Language(OCL)is one kind of lightweight formal specification,which is widely used for software verification and validation in NASA and Object Management Group *** OCL provides a simple expressive syntax,it is hard for the developers to write correctly due to lacking knowledge of the mathematical foundations of the first-order logic,which is approximately half accurate at the first stage of devel-opment.A deep neural network named DeepOCL is proposed,which takes the unre-stricted natural language as inputs and automatically outputs the best-scored OCL candidates without requiring a domain conceptual model that is compulsively required in existing rule-based generation *** demonstrate the validity of our proposed approach,ablation experiments were conducted on a new sentence-aligned dataset named *** experiments show that the proposed DeepOCL can achieve state of the art for OCL statement generation,scored 74.30 on BLEU,and greatly outperformed experienced developers by 35.19%.The proposed approach is the first deep learning approach to generate the OCL expression from the natural *** can be further developed as a CASE tool for the software industry.

关键词： deep learning OCL software engineering

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Optimizing Electric Vehicle Charging Infrastructure through Machine Learning: A Study of Charging Patterns and Energy Consumption

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第21期18卷 149-170页

作者： Alsarhan, Ayoub Alnatsheh, Athari Aljaidi, Mohammad Al Makkawi, Tuqa Aljamal, Mahmoud Alsarhan, Tamam Department of Information Technology Faculty of Prince Al-Hussien bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan King Abdullah II School of Information Technology The University of Jordan Amman Jordan

The rapid adoption of electric vehicles (EVs) has created a pressing need for efficient charging infrastructure. but challenges such as inconsistent demand and poor placement remain. An effective distribution of sufficient EV charging stations (CSs) is one of the major obstacles preventing the market penetration of EVs and the realization of a sustainable transportation system in urban areas. In this paper, a new machine learning technique is proposed in order to optimize the placement of EV charging stations (EVCSs) in metropolitan areas based on an energy consumption prediction model. A dataset from 148,136 charging transactions in Boulder, Colorado, is used with the proposed model. Key algorithms such as KNeighborsRegressor and RandomForestRegressor were incorporated to solve the placement problem. The analysis revealed significant demand fluctuations during peak commute hours, with the KNeighborsRegressor model demonstrating superior prediction accuracy. These insights can guide more effective infrastructure planning and resource allocation, ultimately enhancing the efficiency and user experience of EV charging networks and promoting sustainable urban transportation. © 2024 by the authors of this article.

关键词： Charging stations

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