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3rd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2023

作者： Al Abbas, Marwa A. Khammas, Ban M. Collage of Information Engineering Al-Nahrain University Department of Computer Networks Engineering Jadriya Baghdad Iraq

ISBN: (纸本)9798350327762

The Internet of Things (IoT) has gained significant popularity and attention as a research topic due to its widespread use and associated concerns. Previous security measures, which focused on specific threats using limited and outdated datasets, need to be revised to address the growing size and diversity of IoT applications. Consequently, there is a pressing need to prioritize security in IoT systems. Various deep learning (DL)-based algorithms have been developed to address this challenge for IoT attack detection. In this study, we proposed two deep learning models for malware classification integrated with natural language processing (NLP) techniques, such as padding and word embedding. The first model utilizes a Recurrent Neural Network (RNN), and the second employs a one-dimensional Convolutional Neural Network (CNN). Both proposed systems were trained and evaluated on 3971 opcode instruction files representing attacks on nine CPU architectures. The proposed methods were evaluated based on accuracy, F1-score, recall, and precision and were compared with the latest approaches. The experimental results demonstrate the effectiveness of the proposed models in accurately identifying and categorizing IoT attacks. The CNN classifier achieved an accuracy of 96.7%, while the RNN classifier achieved an impressive accuracy of 99.3%. © 2023 IEEE.

关键词： Internet of things

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A lightweight deep learning model with knowledge distillation for pulmonary diseases detection in chest X-rays

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Multimedia Tools and Applications 2024年第15期84卷 14885-14913页

作者： Asham, Mohammed A. Al-Shargabi, Asma A. Al-Sabri, Raeed Meftah, Ibrahim School of Computer Science and Engineering Central South University Hunan Changsha410083 China Department of Information Technology Collage of Computer Qassim University Buraydah51452 Saudi Arabia College of Mechanical and Electrical Engineering Central South University Changsha410017 China

Accurate and timely diagnosis of pulmonary diseases is critical in the field of medical imaging. While deep learning models have shown promise in this regard, the current methods for developing such models often require extensive computing resources and complex procedures, rendering them impractical. This study focuses on the development of a lightweight deep-learning model for the detection of pulmonary diseases. Leveraging the benefits of knowledge distillation (KD) and the integration of the ConvMixer block, we propose a novel lightweight student model based on the MobileNet architecture. The methodology begins with training multiple teacher model candidates to identify the most suitable teacher model. Subsequently, KD is employed, utilizing the insights of this robust teacher model to enhance the performance of the student model. The objective is to reduce the student model's parameter size and computational complexity while preserving its diagnostic accuracy. We perform an in-depth analysis of our proposed model's performance compared to various well-established pre-trained student models, including MobileNetV2, ResNet50, InceptionV3, Xception, and NasNetMobile. Through extensive experimentation and evaluation across diverse datasets, including chest X-rays of different pulmonary diseases such as pneumonia, COVID-19, tuberculosis, and pneumothorax, we demonstrate the robustness and effectiveness of our proposed model in diagnosing various chest infections. Our model showcases superior performance, achieving an impressive classification accuracy of 97.92%. We emphasize the significant reduction in model complexity, with 0.63 million parameters, allowing for efficient inference and rapid prediction times, rendering it ideal for resource-constrained environments. Outperforming various pre-trained student models in terms of overall performance and computation cost, our findings underscore the effectiveness of the proposed KD strategy and the integration of the Conv

关键词： Diagnosis

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Internet of Things (IoT): survey of most important security risks 3

Internet of Things (IoT): survey of most important security ...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Alaskri, Ahmed Salem Ahmed, Nuredin Ali Shaari, Hala Collage of Computer Technology Software Engineering Dep. Tripoli Libya Faculty of Engineering Computer Engineering Department Tripoli Libya Faculty of Information Web Technologies Department Tripoli Libya

ISBN: (纸本)9798350319897

The Internet of things (IoT) is getting more and more intrusive into our lives until the day comes when everything becomes connected to the Internet. Due to the limited resources and heterogeneous Internet of Things (IoT) devices, the traditional means of protection are useless and cannot be used to protect these devices. The most important security risks, their causes and the consequences of their occurrence have been listed, scheduled and categorized. The study concluded that there are real security risks that cannot be ignored, and they need to find innovative solutions to eliminate them or reduce their damage to a minimum. This paper showed the main risks addressed in previews research, and outlined the gaps in this field of technology, also producing a brief summary information about the most important solution to avoid many threats against the IoT field. © 2023 IEEE.

关键词： Internet of things

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Frequency Dispersion of the Signal in the Recursive Digital Section of the Second Order

Frequency Dispersion of the Signal in the Recursive Digital ...

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2019 Imam Al-Kadhum International Conference for Modern Applications of information and Communication Technology, MAICT 2019

作者： Abd, Mohammad Hasan Raheem, Osamah Adil Kh-Madhloom, Jamal Bugrov, Vladimir Collage of Computer Science and Information Technology University of Wasit Iraq College of Radio Physics Engineering University of Nizhny Novgorod Russia

The problems of modeling the signal and dispersion properties of a second order recursive section in the integer parameter space are considered. The formulation and solution of the section synthesis problem by selective and dispersive criteria using the methods of integer nonlinear mathematical programming are given. The availability of obtaining both positive and negative frequency dispersion of a signal in a recursive section, as well as the possibility of minimizing dispersion distortions in the system, is shown. © Published under licence by IOP Publishing Ltd.

关键词： Integer programming

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Skeleton extraction of color cerbrovascular image based on topological nodes

Skeleton extraction of color cerbrovascular image based on t...

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International Congress on Image and Signal Processing

作者： Li, Mingzhao Qi, Aipeng Dept. of Computer Information and Engineering Luoyang Institute of Science and Technology LIT Luoyang China Dept. of Computer Engineering Henan Industry and Trade Vocational Collage Zhengzhou China

ISBN: (纸本)9781424465149

In this paper, a new continuous skeleton extraction method on color image was proposed. This method uses a new level set model in color space to extract the topology node of an object using a skeleton point as source-point which spreading a moderate wave-front, and then using these topology nodes as new source nodes, it spreads the second high-speed transmission wave front in order to identify skeleton points. All the parameters are obtained from the analysis, avoiding human intervention. Experiment and analysis can verify the validity and the robustness of this method, and it is insensitive to color gradient and boundary noise. ©2010 IEEE.

关键词： Topology

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Depth information aided constrained correlation filter for visual tracking 6

Depth information aided constrained correlation filter for v...

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6th Annual 2018 International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2018

作者： Li, Guanqun Huang, Lei Zhang, Peichang Li, Qiang Huo, Yongkai Collage of Information Engineering Shenzhen University NanHai Ave.3688 NanShan Dist Shenzhen China Collage of Computer Science and Software Engineering Shenzhen University NanHai Ave.3688 NanShan Dist Shenzhen China

In this paper, we proposed a novel visual tracking system by constructing the Constrained Correlation Filter (CCF) with Depth information. More specifically, in our proposed system, to avoid the boundary effects and the fixed shape assumption of conventional Discriminative Correlation Filter (DCF), the shape of target is extracted from depth image provided by RGB-D sensor to construct the CCF, which may prevent the filter from being disturbed by the background noise at the learning stage and enlarge the search region. Moreover, in order to avoid the drifting problem, the update of the model is stopped once part of the target is occluded. The feature weighting coefficients, which reflect the discriminability of the feature channels, are used at the location stage to improve the discriminability. The experimental results show that our method is capable of achieving state-of-the-art performance on Princeton RGB-D tracking benchmark among all public tracking algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Benchmarking

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A cost-effective approach using generative AI and gamification to enhance biomedical treatment and real-time biosensor monitoring

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Scientific Reports 2025年第1期15卷 1-16页

作者： Khan, Abdullah Ayub Laghari, Asif Ali Alsafyani, Majed Baqasah, Abdullah M. Kryvinska, Natalia Almadhor, Ahmad Alroobaea, Roobaea Gregus, Michal Department of Computer Science Bahria University Karachi Campus Karachi 75260 Pakistan Software Collage Shenyang Normal University Shenyang China Department of Computer Science College of Computers and Information Technology Taif University P. O. Box 11099 Taif 21944 Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University Taif 21974 Saudi Arabia Department of Information Management and Business Systems Faculty of Management Comenius University Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakaka 72388 Saudi Arabia

Biosensors are crucial to the diagnosis process since they are designed to detect a specific biological analyte by changing from a biological entity into electrical signals that can be processed for further inspection and analysis. The method provides stability while evaluating cancer cell imaging and real-time angiogenesis monitoring, together with a robust, accurate, and successful identification. Nevertheless, there are several advantages to using nanomaterials in biological therapies like cancer therapy. In support of this strategy, gamification creates a new framework for therapeutic training that provides patients and first aid responders with immunological, photothermal, photodynamic, and chemo-like therapy. Multimedia systems, gamification, and generative artificial intelligence enable us to set up virtual training sessions. In these sessions, game-based training is being developed to help with skin cancer early detection and treatment. The study offers a new, cost-effective solution called GAI, which combines gamification and general awareness training in a virtual environment, to give employees and patients a hierarchy of first aid instruction. The goal of GAI is to evaluate a patient’s performance at each stage. Nonetheless, the following is how the scaling conditions are defined: learners can be divided into three categories: passive, moderate, and active. Through the use of simulations, we argue that the proposed work’s outcome is unique in that it provides learners with therapeutic training that is reliable, effective, efficient, and deliverable. The examination shows good changes in training feasibility, up to 22%, with chemo-like therapy being offered as learning opportunities. © The Author(s) 2025.

关键词： Biomedical treatment Biosensors Cancer diagnosis Gamification Generative artificial intelligence (GenAI) Machine learning (ML)

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Modeling and Performance Assessment of a Millimeter-Wave CVD Film-Graphene Based Antenna 2

Modeling and Performance Assessment of a Millimeter-Wave CVD...

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2nd International Conference of Modern Applications on information and Communication Technology, ICMAICT 2020

作者： Gatte, Mohammed Taih Soh, Ping Jack Kadhim, Rasim Azeez Abd, H.J. Department of Electrical Engineering Collage of Engineering University of Babylon Iraq Information Security Department College of Information Technology University of Babylon Iraq School of Computer and Communication University Malaysia Perlis Malaysia

The transfer of graphene onto dielectric substrates to form an antenna needs to be performed with care, as tears and cracks may appear on the samples transferred improperly. This paper studies a detailed method in estimating the performance of a millimeter-wave (MMW) antenna built using commercially-available graphene layers. In contrast to the widely studied monolayer graphene, the proposed antenna is produced based on the chemical vapor deposition (CVD) technique, which is to be secured onto the antenna substrate via adhesive material without removal of the transition metal substrate from the graphene layer sample. The antenna performance modeled using this technique indicated quite similar results to a model using ideal monolayer graphene in terms of gain, radiated power and radiation efficiency. Its performance benchmarked against a copper antenna operating at the same MMW frequency also indicated similar performance improvements as with another antenna designed using monolayer graphene. © Published under licence by IOP Publishing Ltd.

关键词： Monolayers

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Improving the Performance of Semantic Text Similarity Tasks on Short Text Pairs 20

Improving the Performance of Semantic Text Similarity Tasks ...

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20th International Conference on Language engineering, ESOLEC 2022

作者： Gamal, Mohamed Taher El-Kafrawy, Passent M. School of Information Technology and Computer Science CIS Research Center Nile University Computer Science Department Giza Egypt Effat Collage of Engineering Jedah Saudi Arabia

ISBN: (纸本)9781665453226

Training semantic similarity model to detect duplicate text pairs is a challenging task as almost all of datasets are imbalanced, by data nature positive samples are fewer than negative samples, this issue can easily lead to model bias. Using traditional pairwise loss functions like pairwise binary cross entropy or Contrastive loss on imbalanced data may lead to model bias, however triplet loss showed improved performance compared to other loss functions. In triplet loss-based models data is fed to the model as follow: Anchor sentence, positive sentence and negative sentence. The original data is permutated to follow the input structure. The default structure of training samples data is 363,861 training samples (90% of the data) distributed as 134,336 positive samples and 229,524 negative samples. The triplet structured data helped to generate much larger amount of balanced training samples 456,219. The test results showed higher accuracy and f1 scores in testing. We fine-Tunned RoBERTa pre trained model using Triplet loss approach, testing showed better results. The best model scored 89.51 F1 score, and 91.45 Accuracy compared to 86.74 F1 score and 87.45 Accuracy in the second-best Contrastive loss-based BERT model. © 2022 IEEE.

关键词： Semantics

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Necklace: A novel long text detection model

Necklace: A novel long text detection model

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3rd Asian Conference on Artificial Intelligence Technology, ACAIT 2019

作者： Qin, Xiao Chu, Xutao Yuan, Changan Zhao, Qi School of Computer and Information Engineering Guangxi Teachers Education University Nanning China Guangxi Collage of Education Nanning China

This study proposes a new text detection model and a novel tag generation method for long text detection in natural scenes. The background suppression module is added to the network architecture to integrate feature mapping of each channel, weaken background features, enhance text features, and improve detection accuracy. In the design of the regression frame, the use of elliptical geometric regression, through the formation of long text through a plurality of elliptical connections, better fit the shape of the text line to solve the problem of long text detection. The proposed method has achieved the performance of state-of-art in the popular datasets. © 2020 Institution of engineering and Technology. All rights reserved.

关键词： Network architecture

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