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Novel Intelligent and Leading Emerging Sciences Conference (NILES)

作者： Amany A. Abdelrahman Wafaa S. El-Kassas Hoda K. Mohamed Department of Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computers and Information Technology The National Egyptian E-Learning University (EELU) Giza Egypt

Automatic Text Summarization (ATS) for long documents is a very challenging task. A long document includes more than one topic, so it is required to construct a summary that covers the most important contextual information from the different topics in the input document. There are many ATS techniques, but the produced summaries are much less accurate than the human summaries. This paper proposes a supervised extractive Neural-based summarization model that focuses on long documents summarization by integrating both the local and global contexts of the document. The proposed system employs two bidirectional Gated Recurrent Units (GRUs) as word and sentence encoders, respectively, and uses Word2Vec embeddings as word representation. Two ensembles are proposed by applying the average ensemble method to the proposed system with two other Neural-based systems. The proposed system uses the PubMed dataset to evaluate the two ensembles using the ROUGE evaluation metrics. The results of the evaluation show that there are promising and remarkable improvements of 0.14% and 0.97% for ROUGE-1, 0.33% and 1.12% for ROUGE-2, and 0.25% for ROUGE-L.

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Survey on compressed sensing over the past two decades

Memories - Materials, Devices, Circuits and Systems

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Memories - Materials, Devices, Circuits and systems 2023年 4卷

作者： Hosny, Sherif El-Kharashi, M. Watheq Abdel-Hamid, Amr T. Department of Computer and Systems Engineering Ain Shams University Cairo Egypt STMicroelectronics Cairo Egypt Department of Electrical and Computer Engineering University of Victoria VictoriaBC Canada Faculty of Information Engineering and Technology German University in Cairo Cairo Egypt

Compressed Sensing (CS) is a novel data acquisition theorem exploiting the signals sparsity differing from traditional Nyquist theorem in the ability of obtaining all information of such signal in fewer samples. CS can enable full use of sparsity, where the sparse signal can be reconstructed using fewer measurements. Over the past decade, several papers have investigated the feasibility of deploying CS in current applications. A lot of developments are performed in this area in order to enhance the performance and re-usability. The CS algorithm involves many phases at the transmitter side, including: transformation, compression, encoding, encryption, and modulation. Meanwhile the receiver involves: demodulation, decryption, decoding, and reconstruction. This work assembles most of the published papers in the CS area, listing the important details and showing their contributions. Each building block of the CS system is studied solely and compared with its reference in the literature. A comparative study is performed reviewing the work in the literature with respect to compression metrics, deployed reconstruction algorithm, system complexity. Tabulated results are studied with respect to hardware and memory computation complexity. Recommendations and conclusions are illustrated at the end of our work. © 2023 The Author(s)

关键词： Compressed sensing

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An Efficient Sound and Data Steganography Based Secure Authentication System

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computers, Materials & Continua 2021年第4期67卷 723-751页

作者： Debajit Datta Lalit Garg Kathiravan Srinivasan Atsushi Inoue G.Thippa Reddy M.Praveen Kumar Reddy K.Ramesh Nidal Nasser School of Computer Science and Engineering Vellore Institute of Technology(VIT)Vellore632014India Faculty of Information and Communication Technology University of MaltaMsidaMSD2080Malta School of Information Technology and Engineering Vellore Institute of Technology(VIT)Vellore632014India Department of Information Systems&Business Analytics Eastern Washington UniversitySpokaneWA99202USA Department of Computer Science Karnataka State Akkamahadevi Women’s UniversityVijayapuraIndia College of Engineering Alfaisal UniversityRiyadh1153Saudi Arabia

The prodigious advancements in contemporary technologies have also brought in the situation of unprecedented ***,the pin-based security system is an inadequate mechanism for handling such a *** reason is that hackers use multiple strategies for evading security systems and thereby gaining access to private *** research proposes to deploy diverse approaches for authenticating and securing a connection amongst two devices/gadgets via sound,thereby disregarding the pins’manual ***,the results demonstrate that the proposed approaches outperform conventional pin-based authentication or QR authentication ***,a random signal is encrypted,and then it is transformed into a wave file,after which it gets transmitted in a short burst via the device’s ***,the other device/gadget captures these audio bursts through its microphone and decrypts the audio signal for getting the essential data for ***,this model requires two devices/gadgets with speakers and a microphone,and no extra hardware such as a camera,for reading the QR code is *** first module is tested with realtime data and generates high scores for the widely accepted accuracy metrics,including precision,Recall,F1 score,entropy,and mutual information(MI).Additionally,this work also proposes a module helps in a secured transmission of sensitive data by encrypting it over images and other *** steganographic module includes two-stage encryption with two different encryption algorithms to transmit data by embedding inside a *** encryption algorithms and their combinations are taken for this system to compare the resultant file *** these systems engender high accuracies and provide secure connectivity,leading to a sustainable communication ecosystem.

关键词： Cyber-attacks signals privacy security organization encryption decryption authentication effective communication steganography

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A Review of 5G technology: Architecture and Challenges 8

A Review of 5G Technology: Architecture and Challenges

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8th IEEE International Conference on Computing, engineering and Design, ICCED 2022

作者： Attarbashi, Zainab S. Altaleb, Abdulazeez E. Mohd Aman, Azana Hafizah Hassan Abdalla Hashim, Aisha Eker, Sebahattin International Islamic University Malaysia Information Communication Technology Kuala Lumpur Malaysia Informatics Institute Istanbul Technical University Communication Systems Department Istanbul Turkey University Kebangsaan Malaysia Faculty of Information Science Technology Bangi Malaysia International Islamic University Malaysia Computer Information Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9781665453899

5G Networks are considered as a new evolution since it aims to increase the wireless capacity 1000 times the previous generations. By 2025, it is expected that 5G will be connecting around 3 billion people and 41.6 billion devices enabling the Internet of Things (IoT) and the Internet of Everything (IoE) while increasing energy efficiency 90% more than 4G and reducing 20% of carbon emissions. In this paper, the deployment requirements and challenges of 5G Networks are reviewed. Starting with the overall network architecture and network slicing framework including the service-type slicing and the infrastructure slicing are investigated. Later, the spectrum and multiple access techniques are explained. Finally, the challenges and the available solutions have been discussed. © 2022 IEEE.

关键词： 5G mobile communication systems

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Enhancing Extractive Text Summarization of Long Documents Using Features Dimensionality Reduction

Enhancing Extractive Text Summarization of Long Documents Us...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

Automatic Text Summarization (ATS) task is considered one of the most challenging tasks in Natural Language Processing field, especially for long documents. There are many techniques for ATS, including Artificial Intelligence (AI) approaches, statistical-based approaches, graph-based approaches, etc., but the generated summaries are still less accurate than human summaries. This research presents an extractive Neural-based model to summarize long documents using both attentive global and local contexts, Word2Ve $c$ embeddings, and a bidirectional Gated Recurrent Unit (GRU) as a Sentence Encoder. The proposed model uses a Multi-Layer Perceptron (MLP) that consists of two linear layers with Rectified Linear Activation Units (RELUs) to reduce features dimensionality. The proposed model is evaluated on PubMed and arXiv datasets using ROUGE metrics, and the evaluation results show remarkable and promising improvements for ROUGE-1, ROUGE-2, and ROUGE-L.

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Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining

Rock Mechanics Bulletin

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Rock Mechanics Bulletin 2025年第1期4卷 29-52页

作者： Yewuhalashet Fissha Prashanth Ragam Hajime Ikeda N.Kushal Kumar Tsuyoshi Adachi P.S.Paul Youhei Kawamura Department of Geosciences Geotechnologyand Materials Engineering for ResourcesGraduate School of International Resource SciencesAkita UniversityAkita010-8502Japan Department of Mining Engineering Aksum UniversityAksum7080TigrayEthiopia School of Computer Science and Engineering VIT-AP UniversityAmaravatiAndhra PradeshIndia Department of Systems Controland Information EngineeringNational Institute of TechnologyAsahikawa College2-2-1-6 Syunkodai Asahikawa city Hokkaido071-8142Japan Department of Mining Engineering Indian Institute of Technology(Indian School of Mines)Dhanbad826004India Faculty of Engineering Hokkaido UniversityKita 8Nishi 5Kita-KuSapporo0608628Japan

The vibrations generated by rock blasting are a serious and hazardous outcome of these activities,causing harmful effects on the surrounding environment as well as the nearby *** the local ecology and human communities suffer from the consequences of these *** the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity(PPV)and frequency,which are essential parameters for measuring vibration *** prediction of vibration occurrence is critically ***,this study employs five machine learning models for predicting the PPV and frequency resulting from quarry *** work compares five machine learning models(XGBoost,Catboost,Bagging,Gradient Boosting,and Random Forest Regression)to choose the most efficient performance *** performance evaluation of each five machine learning models demonstrates each model achieved a performance of more than 0.90 during the testing phase,there was a strong correlation observed between the actual and the predicted *** analysis of performance metrics shows Catboost regression model demonstrate better performance prediction comparing with the other models with R^(2)=0.983,MSE=0.000078,RMSE=0.008,NRMSE=0.019,MAD=0.004,MAPE=35.197 in the PPV prediction,and R^(2)=0.975,MSE=0.000243,RMSE=0.015,NRMSE=0.031,MAD=0.008,MAPE=37.281 for the frequency *** study will help mining engineers and blasting experts to select the best machine learning model and its hyperparameters in estimating ground vibration,and *** the context of the mining and civil industry,the application of this study offers significant potential for enhancing safety protocols and optimizing operational *** employing machine learning models,this research aims to accurately predict and assess ground vibrations with frequency resulting from rock blasting.

关键词： PPV Frequency Machine learning Blasting Regression Catboost XGboost

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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Enhancing IoT Intelligence: A Transformer-based Reinforcement Learning Methodology

Enhancing IoT Intelligence: A Transformer-based Reinforcemen...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Gaith Rjoub Saidul Islam Jamal Bentahar Mohammed Amin Almaiah Rana Alrawashdeh Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science Khalifa University Abu Dhabi UAE King Abdullah II School of Information Technology The University of Jordan Amman Jordan Faculty of Information Technology Applied Science Private University Amman Jordan Department of Information and Computer Science King Fahd University of Petroleum & Minerals Dhahran KSA

ISBN: (数字)9798350361261

ISBN: (纸本)9798350361278

The proliferation of the Internet of Things (IoT) has led to an explosion of data generated by interconnected devices, presenting both opportunities and challenges for intelligent decision-making in complex environments. Traditional Reinforcement Learning (RL) approaches often struggle to fully harness this data due to their limited ability to process and interpret the intricate patterns and dependencies inherent in IoT applications. This paper introduces a novel framework that integrates transformer architectures with Proximal Policy Optimization (PPO) to address these challenges. By leveraging the self-attention mechanism of transformers, our approach enhances RL agents’ capacity for understanding and acting within dynamic IoT environments, leading to improved decision-making processes. We demonstrate the effectiveness of our method across various IoT scenarios, from smart home automation to industrial control systems, showing marked improvements in decisionmaking efficiency and adaptability. Our contributions include a detailed exploration of the transformer’s role in processing heterogeneous IoT data, a comprehensive evaluation of the framework’s performance in diverse environments, and a benchmark against traditional RL methods. The results indicate significant advancements in enabling RL agents to navigate the complexities of IoT ecosystems, highlighting the potential of our approach to revolutionize intelligent automation and decision-making in the IoT landscape.

关键词： Intelligent automation Navigation Decision making Ecosystems Smart homes Reinforcement learning Transformers

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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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Decision Aid Sciences and Application (DASA), International Conference on

作者： Amjed Abbas Ahmed Zainab Mohammed Hussein Ali Hamza Najim Khattab M. Ali Alheeti Nurhizam Safie Mohd Satar Aisha Hassan Abdallah Hashim Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Imam AlKadhum College (IKC) Baghdad Iraq Department of Computer Science Information System University of Technology Imam AIKadhum College (IKC) Baghdad Iraq Department of Computer Technical Engineering Imam Al-Kadhum College (IKC) Al-Diwaniyah Iraq Computer Networking Systems Department College of Computer and Information Technology University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Bangi Malaysia Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (数字)9798350369106

ISBN: (纸本)9798350369113

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100–500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19–24 %), energy consumption (32-36 % ), packet loss (16-190/0), and end-to-end latency (21-26 % ), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance.

关键词： Wireless communication Wireless sensor networks Energy consumption Packet loss Routing Throughput Routing protocols Energy efficiency Encryption Security

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Sustainability of Load Balancing Techniques in Fog Computing Environment: Review 18

Sustainability of Load Balancing Techniques in Fog Computing...

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18th International Conference on Mobile systems and Pervasive Computing, MobiSPC 2021, The 16th International Conference on Future Networks and Communications, FNC 2021 and the 11th International Conference on Sustainable Energy information technology, SEIT 2021

作者： Sulimani, Hamza Alghamdi, Wael Y. Jan, Tony Bharathy, Gnana Prasad, Mukesh Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW Australia Faculty of Computer Science and Information Technology Ta 'If University Ta'if Saudi Arabia School of IT and Engineering Melbourne Institute of Technology Sydney Australia School of Information Systems and Modelling University of Technology Sydney Sydney Australia

The extreme workloads on the fog layer caused a misalignment in some fog nodes that affect its efficiency and degenerate fog technology's primary goal. Therefore, creating a balanced computing environment via the offloading process is the key. However, there are many obstacles to balance computing nodes in the fog environment, such as offloading strategy and its consequences due to the extreme offloading processes, and when need to offload and where. These obstacles are vital concerns among researchers. Thus, several studies have been conducted to enhance the fog system performance to increase the entire system's throughput. This paper explores the recent articles to determine the possible research gaps and opportunities to implement an efficient solution for load balancing in fog environments after analyzing and assessing the existing solutions. While most of the proposed solutions involve short-term solution, this literature review reveals the need to find out a sustainable resolution for load balancing to avoid listed obstacles by using the offloading technique with maintaining the bandwidth of the network. © 2021 Elsevier B.V.. All rights reserved.

关键词： Fog

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