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Millimeter Wave Massive MIMO Heterogeneous Networks Using Fuzzy-Based Deep Convolutional Neural Network (FDCNN)

Intelligent Automation & Soft Computing 2023年第4期36卷 633-646页

作者： Hussain Alaaedi Masoud Sabaei Department of Computer Engineering at Computer Networks and Architecture Amirkabir University of TechnologyTehran***Iran

Enabling high mobility applications in millimeter wave(mmWave)based systems opens up a slew of new possibilities,including vehicle communi-cations in addition to wireless virtual/augmented *** narrow beam usage in addition to the millimeter waves sensitivity might block the coverage along with the reliability of the mobile *** this research work,the improvement in the quality of experience faced by the user for multimedia-related applications over the millimeter-wave band is *** high attenuation loss in high frequencies is compensated with a massive array structure named Multiple Input and Multiple Output(MIMO)which is utilized in a hyperdense environment called heterogeneous networks(HetNet).The optimization problem which arises while maximizing the Mean Opinion Score(MOS)is analyzed along with the QoE(Quality of Experience)metric by considering the Base Station(BS)powers in addition to the needed Quality of Service(QoS).Most of the approaches related to wireless network communication are not suitable for the millimeter-wave band because of its problems due to high complexity and its dynamic *** a deep reinforcement learning framework is developed for tackling the same opti-mization *** this work,a Fuzzy-based Deep Convolutional Neural Net-work(FDCNN)is proposed in addition to a Deep Reinforcing Learning Framework(DRLF)for extracting the features of highly correlated *** investigational results prove that the proposed method yields the highest satisfac-tion to the user by increasing the number of antennas in addition with the small-scale antennas at the base *** proposed work outperforms in terms of MOS with multiple antennas.

关键词： Multiple-input and multiple-output quality of experience quality of service(qos) fuzzy-based deep convolutional neural network

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DXDSENet-CM model: an ensemble learning model based on depthwise Squeeze-and-Excitation ConvMixer architecture for the classification of multi-class skin lesions

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Multimedia Tools and Applications 2025年第12期84卷 9903-9938页

作者： Fırat, Hüseyin Üzen, Hüseyin Department of Computer Engineering Faculty of Engineering Dicle University Diyarbakır Turkey Department of Computer Engineering Faculty of Engineering and Architecture Bingol University Bingol Turkey

Skin cancer is acknowledged as the most prevalent form of cancer on a global scale. Failure to detect it in its initial phases can lead to fatality, underscoring the significance of early diagnosis. While visible to the naked eye, the considerable similarities between different classes and the variations within a single class create substantial hurdles for accurate detection. Given the widespread occurrence of this ailment, numerous deep learning-driven automated systems have been created to aid dermatologists in promptly identifying skin anomalies. Within this investigation, an ensemble learning architecture based on Xception, DenseNet201 and Depthwise Squeeze-and-Excitation Network and ConvMixer (DSENet-ConvMixer) architectures is presented for skin lesion classification. Xception, a pre-trained CNN network, utilizes depthwise separable convolutions and residual connections, offering efficient feature extraction from skin lesion images. DenseNet201’s parameter efficiency, feature reuse, and multi-scale information capture make it a valuable addition. DSENet-ConvMixer integrates these components, efficiently merging low-level and high-level features to enhance training and network efficiency. ConvMixer excels at feature extraction, enhancing spatial detail capture. ISIC 2018 and 2019 datasets were employed to assess the classification performance of the proposed architecture. Individual accuracies for Xception, DenseNet201, and DSENet-ConvMixer in ISIC 2018 were 85.91%, 85.71%, 82.02%, respectively, and in ISIC 2019 were 80.93%, 80.50%, 79.08%, respectively. However, the ensemble architecture with these three architectures achieved 88.21% for ISIC 2018 and 92.34% for ISIC 2019, demonstrating superior performance. Literature reviews on both datasets also confirmed the proposed architecture’s effectiveness compared to recent studies. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Contrastive Learning

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Malware Detection on Linux Using Runtime Opcode Tracing 37

Malware Detection on Linux Using Runtime Opcode Tracing

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37th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2024

作者： Alonso, Marti Costa, Juan Jose Morancho, Enric Department of Computer Architecture Barcelona Spain

ISBN: (纸本)9798350366884

The fast-paced evolution of cyberattacks to digital infrastructures requires new protection mechanisms to counter-attack them. Malware attacks, a type of cyberattacks ranging from viruses and worms to ransomware and spyware, have been traditionally detected using signature-based methods. But with new versions of malware, this approach is not good enough, and new machine learning tools look promising. In this paper we evaluate five machine learning models (SVM, KNN, Naive Bayes, Decision Tree and Random Forest) to detect Linux malware on applications during its runtime by tracking their executed instructions (opcodes). We show the methodology, the initial dataset preparation, the infrastructure used to obtain the traces of executed instructions, and the evaluation of the results for the different models used. The obtained results show that Random Forest classifier gets the best results, with 90% accuracy or higher. © 2024 IEEE.

关键词： Adversarial machine learning

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Ransomware on cyber-physical systems: Taxonomies, case studies, security gaps, and open challenges

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 186-202页

作者： Benmalek, Mourad Computer Engineering Department College of Engineering and Architecture Al Yamamah University Riyadh Saudi Arabia

Ransomware attacks have emerged as one of the most significant cyberthreats faced by organizations worldwide. In recent years, ransomware has also started to target critical infrastructure and Cyber-Physical Systems (CPS) such as industrial control systems, smart grids, and healthcare networks. The unique attack surface and safety-critical nature of CPS introduce new challenges in defending against ransomware. This paper provides a comprehensive overview of ransomware threats to CPS. We propose a dual taxonomy to classify ransomware attacks on CPS based on infection vectors, targets, objectives, and technical attributes. Through an analysis of 10 real-world incidents, we highlight attack patterns, vulnerabilities, and impacts of ransomware campaigns against critical systems and facilities. Based on the insights gained, we identify open research problems and future directions to improve ransomware resilience in CPS environments. © 2024 The Author

关键词： Cyber Physical System

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Ensuring privacy in provenance information for images 24

Ensuring privacy in provenance information for images

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24th International Conference on Digital Signal Processing, DSP 2023

作者： Fotos, Nikolaos Delgado, Jaime Computer Architecture Department Barcelona Spain

ISBN: (纸本)9798350339598

Current technologies facilitate the generation of falsified images that spread misinformation and endanger trust between users and media. Technological and academic institutions have contributed towards building frameworks that try to combat the aforementioned issue by establishing verifiable mechanisms to annotate metadata regarding the creation and modifications of an image (i.e., provenance). However, this type of metadata might conceal information about a specific person or place which threatens the privacy of individuals and locations. To address this problem, we propose the extension of existing provenance schemas in order to provide privacy-enabling features that allow users to gain control over the verifiable data that they intend to share. The proposal examines the provenance schema of the Coalition for Content Authenticity and Provenance (C2PA) specification and extends it using the JPEG Privacy and Security standard (ISO/IEC 19566-4) in order to support the protection of privacy-related information in images. Part of this work has been submitted as a contribution to the standardization activities of the ISO/IEC JTC 1/SC 29/WG 1 "JPEG Coding of digital representations of images"committee. © 2023 IEEE.

关键词： Metadata

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Implementation of Safety-Related Intrusion Detection for an Industrial Robotic Arm

Implementation of Safety-Related Intrusion Detection for an ...

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2025 International Conference on Control, Automation, and Instrumentation, IC2AI 2025

作者： Haydar, Georgio Abou Telawi, Samer Borcsok, Josef Architecture and System Programming University of Kassel Department of Computer Kassel Germany

ISBN: (纸本)9798331529604

Ensuring the safety of human workers collaborating with industrial robots is paramount. This research work presents a novel approach by developing a safety-related intruder detection system for the operational zones of industrial robots, following the IEC 61508 standard and adopting the related redundant architecture. The hardware integrates light curtains and radar sensors in a dual-layer configuration, while the software utilizes a safety system-on-chip to implement real-time diagnostics and fault tolerance. Protecting employees and ensuring a smooth and safe work environment are significant concerns for all companies. As a result, numerous standards and regulations have been established to provide safe collaboration between humans and machines. However, despite these measures, accidents continue to occur, resulting in varying degrees of injuries. The proposed system is depicted through a comprehensive schematic, emphasizing the integration of safety measures within the robotic environment. The detailed prototype demonstrates practical application, show-casing the system's ability to detect intrusions, initiate emergency responses, and transition to fail-safe states during component failures. This research work advances safety protocols in human-robot collaboration within industrial settings, fostering a safe working environment through a well-defined safety framework. © 2025 IEEE.

关键词： Industrial robots

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Deep convolutional neural networks for age and gender estimation using an imbalanced dataset of human face images

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Neural Computing and Applications 2024年第34期36卷 21839-21858页

作者： Akgül, İsmail Department of Computer Engineering Faculty of Engineering and Architecture Erzincan Binali Yıldırım University Erzincan Turkey

Automatic age and gender estimation provides an important information to analyze real-world applications such as human–machine interaction, system access, activity recognition, and consumer profile detection. While it is easy to estimate a person’s gender from human facial images, estimating their age is difficult. In such previous challenging studies, traditional convolutional neural network (CNN) methods have been used for age and gender estimation. With the development of deep convolutional neural network (DCNN) architectures, more successful results have been obtained than traditional CNN methods. In this study, two state-of-the-art DCNN models have been developed in the field of artificial intelligence (AI) to make age and gender estimation on an imbalanced dataset of human face images. Firstly, a new model called fast description network (FINet) was developed, which has a parametrically changeable structure. Secondly, the number of parameters has been reduced by using the layer reduction approach in InceptionV3 and NASNetLarge DCNN model structures, and a second model named inception Nasnet fast identify network (INFINet) was developed by concatenating these models and the FINet model as a triple. FINet and INFINet models developed for age and gender estimation were compared with many other state-of-the-art DCNN models in AI. The most successful accuracy results in terms of both age and gender were obtained with the INFINet model (age: 61.22%, gender: 80.95% in the FG-NET dataset, age: 72.00%, gender: 90.50% in the UTKFace dataset). The results obtained in age and gender estimation with the INFINet model are much more effective than other recent state-of-the-art works. In addition, the FINet model, which has a much smaller number of parameters than the compared models, showed a classification performance that can compete with state-of-the-art methods for age and gender estimation. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of

关键词： Convolutional neural networks

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A New Hybrid Approach Using GWO and MFO Algorithms to Detect Network Attack

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computer Modeling in Engineering & Sciences 2023年第8期136卷 1277-1314页

作者： Hasan Dalmaz Erdal Erdal Halil Muratünver Department of Computer Engineering Faculty of Engineering and ArchitectureKırıkkale UniversityKırıkkale71450Turkey

This paper addresses the urgent need to detect network security attacks,which have increased significantly in recent years,with high accuracy and avoid the adverse effects of these *** intrusion detection system should respond seamlessly to attack patterns and *** use of metaheuristic algorithms in attack detection can produce near-optimal solutions with low computational *** achieve better performance of these algorithms and further improve the results,hybridization of algorithms can be used,which leads to more successful ***,many studies are conducted on this *** this study,a new hybrid approach using Gray Wolf Optimizer(GWO)and Moth-Flame Optimization(MFO)algorithms was developed and applied to widely used data sets such as NSL-KDD,UNSW-NB15,and CIC IDS 2017,as well as various benchmark *** ease of hybridization of the GWO algorithm,its simplicity,its ability to perform global optimal search,and the success of the MFO algorithm in obtaining the best solution suggested that an effective solution would be obtained by combining these two *** these reasons,the developed hybrid algorithm aims to achieve better results by using the good aspects of both the GWO algorithm and the MFO *** reviewing the results,it was found that a high level of success was achieved in the benchmark *** achieved better results in 12 of the 13 benchmark functions *** addition,the success rates obtained according to the evaluation criteria in the different data sets are also *** the 97.4%,98.3%,and 99.2% classification accuracy results obtained in the NSL-KDD,UNSW-NB15,and CIC IDS 2017 data sets with the studies in the literature,they seem to be quite successful.

关键词： Network attack detection hybrid GWO MFO

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A Study on Efficiency Improvements of DNN Accelerators via Denormalized Arithmetic Elimination 38

A Study on Efficiency Improvements of DNN Accelerators via D...

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38th International Conference on VLSI Design, VLSID 2025

作者： Kharitonov, Alexander Kundu, Sandip Institute of Computer Architecture & Computer Engineering University of Stuttgart Stuttgart Germany University of Massachusetts Department of Electrical & Computer Engineering Amherst United States

ISBN: (纸本)9798331522445

Deep Neural Networks (DNNs) excel in tasks such as vision and speech processing, driving their widespread use. A key operation in DNNs is convolution, which is often implemented using General Matrix Multiplication (GEMM). Modern CPUs incorporate hardware accelerators for faster GEMM operation. Reduced precision floating-point (RPF) formats such as bfloat16 and TensorFloat32 are popular in machine learning, offering a balance between classification accuracy, dynamic range for training gradients, and hardware efficiency compared to traditional IEEE 754 32-bit or 64-bit floating-point numbers. While RPF is well-studied in the context of DNNs, a major complexity arises from handling denormalized numbers. This paper investigates their impact on DNNs and finds that removing denormalized number support from hardware accelerators does not affect classification accuracy, while allowing significant improvements in hardware efficiency and performance. With proliferation of data centers to accommodate the growing number of DNN applications, power reductions at the GEMM level can significantly improve cluster-wide efficiency, promoting sustainability. Our results indicate that eliminating denormalized arithmetic leads to significant improvements in multiplier circuit efficiency, with reductions of up to 46% in area (LUT count), 28% in longest path delay, and 59% in power consumption, all while maintaining DNN classification accuracy. © 2025 IEEE.

关键词： Deep neural networks

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Training neuro-fuzzy using flower pollination algorithm to predict number of COVID-19 cases: situation analysis for twenty countries

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Neural Computing and Applications 2024年第26期36卷 16365-16393页

作者： Baştemur Kaya, Ceren Kaya, Ebubekir Department of Computer Technologies Nevsehir Vocational School Nevsehir Haci Bektas Veli University Nevşehir Turkey Department of Computer Engineering Engineering Architecture Faculty Nevsehir Haci Bektas Veli University Nevşehir Turkey

Predicting the number of COVID-19 cases offers a reflection of the future, and it is important for the implementation of preventive measures. The numbers of COVID-19 cases are constantly changing on a daily. Adaptive methods are needed for an effective estimation instead of traditional methods. In this study, a novel method based on neuro-fuzzy and FPA is proposed to estimate the number of COVID-19 cases. The antecedent and conclusion parameters of the neuro-fuzzy model are determined by using FPA. In other words, neuro-fuzzy training is carried out with FPA. The number of COVID-19 cases belonging to twenty countries including USA, India, Brazil, Russian, France, UK, Italy, Spain, Argentina, Germany, Colombia, Mexico, Poland, Turkey, Iran, Peru, Ukraine, South Africa, the Netherlands and Indonesia is estimated. Time series is created using the number of COVID-19 cases. Daily, weekly and monthly estimates are realized by utilizing these time series. MSE is used as the error metric. Although it varies according to the example and problem type, the best training error values between 0.000398027 and 0.0286562 are obtained. These best test error values are between 0.0005607 and 0.409867. The best training and test error values are 0.000398027 and 0.0005607, respectively. In addition to FPA, the number of cases is also predicted with the algorithms such as particle swarm optimization, harmony search, bee algorithm, differential evolution and their performances are compared. Success score and ranking are created for all algorithms. The scores of FPA for the daily, weekly and monthly forecast are 71, 77 and 62, respectively. These scores have shown that neuro-fuzzy training based on FPA is successful than other meta-heuristic algorithms for all three prediction types in the short- and medium-term estimation of COVID-19 case numbers. © The Author(s) 2024.

关键词： COVID-19

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