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Advancements in Computing (ICAC), International Conference on

作者： Tharindu Gunasekara Disira Thihan Sansika Kodithuwakku Dinuka Dissanayake Harinda Fernando Samitha Vidhanaarachchi Archchana Sindhujan dept. of Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka dept. of Computer Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka dept. of Computer Science and Electronic Engineering University of Surrey United Kingdom

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news detection. This approach captures the emotional cues in the text through a Sentiment Analysis expert, which is based on bidirectional encoder representations from Transformers (BERT) models with sentiment vectors generated using SentiWordNet and Integrated Gradients. It combines a sarcasm detection expert based on BERT, recognizing sarcasm and its type to help classify fake news. By fusing these experts through a Mixture of Experts gateway, subtle linguistic cues often found in fake news are more effectively analyzed, leading to improved accuracy in detecting misinformation. Experimental results are presented as 96% for the Sarcasm expert with the BERT base model and 83% for the Sentiment Analysis expert with the distilled version of the BERT (DistilBERT) base model, proving the effectiveness of the proposed approach in beating traditional methods.

关键词： Sentiment analysis Analytical models Accuracy Bidirectional control Logic gates Linguistics Transformers Encoding Vectors Fake news

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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An Evaluation of Different Deep Feature Extraction Approaches for MRI-Based Brain Tumor Detection

An Evaluation of Different Deep Feature Extraction Approache...

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Artificial Intelligence Science and Applications in Industry and Society (CAISAIS), International Conference on

作者： Nehal Sakr Fatma El-Zahraa El Gamal Reem El-Deeb Sara El-Metwally Sara Elhishi Information Technology Deptarment Mansoura University Mansoura Egypt Computer Science Department Mansoura University Mansoura Egypt Information Systems Department Mansoura University Mansoura Egypt

Brain tumor is a leading cause of death globally and Magnetic Resonance Imaging (MRI) is a powerful tool for its diagnosis. However, the ability to extract more representative features for efficient characterization of brain tumors is still a highly targeted topic. This paper aims to study the power of different deep learning approaches such as traditional Convolutional Neural Network (CNN), transfer learning via three well-known models (VGG16, InceptionV3, and ResNet50), and convolutional auto-encoder in extracting deep representative features for efficient characterization of brain tumors. The performance evaluation was done using two public datasets of MRI images and various benchmarking evaluation metrics. Both traditional CNN and convolutional auto-encoder achieved 100% accuracy, with the auto-encoder producing the lowest loss and highly productive with balanced datasets. Transfer learning models achieved an overall performance of 99% in different evaluation metrics and were found to be preferred with unbalanced datasets.

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Interface Protection Against Transient Faults

Interface Protection Against Transient Faults

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IEEE Design and Diagnostics of Electronic Circuits and systems (DDECS)

作者： Ján Mach Lukáš Kohútka Pavel Čičák Institute of Computer Engineering and Applied Informatics Slovak University of Technology Bratislava Slovakia Institute of Informatics Information Systems and Software Engineering Slovak University of Technology Bratislava Slovakia

Increasing bus frequency to fulfill the performance requirements of dependable applications may increase the susceptibility of the system to transient faults. This paper describes an integration of protection against transient bus faults into the interface of the Hardisc RISC-V core. The protection is based on information redundancy with spatial redundancy features. It enables uninterrupted execution in the presence of transient faults and provides a hardware-software interface for its reporting. The benchmarking results indicate that most of the applications will be impacted minimally. The protection has a negligible impact on the maximal frequency and 8% area and power consumption overhead.

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Deep learning and ensemble methods for anomaly detection in ICS security

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International Journal of information Technology (Singapore) 2025年第3期17卷 1761-1775页

作者： Hossain, Md. Alamgir Hasan, Tahmid Karovic, jr, Vincent Abdeljaber, Hikmat A. M. Haque, Md. Alimul Ahmad, Sultan Zafar, Aasim Nazeer, Jabeen Mishra, B.K. Department of Computer Science and Engineering State University of Bangladesh South Purbachal Kanchan Dhaka 1461 Bangladesh Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Information Systems Faculty of Management Comenius Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman 11931 Jordan MEU Research Unit Middle East University Amman 11831 Lebanon Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Computer Science Aligarh Muslim University U.P Aligarh 202002 India P.G. Department of Physics Veer Kunwar Singh University Bihar Ara India

This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to enhance anomaly detection. Our method utilizes a tailored autoencoder architecture to efficiently select features, followed by a Random Forest classifier to ensure reliable and generalizable detection of anomalies. Evaluated on the HAI Security Dataset, the proposed approach demonstrates exceptional performance, with significant improvements in detecting anomalous activities in ICS. The results highlight the method’s potential to enhance ICS security by providing a scalable and adaptable framework that evolves with emerging threats. This study not only advances cybersecurity methodologies for industrial systems but also lays the groundwork for future research, emphasizing the need for innovative and effective security solutions in the face of evolving cyber challenges. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anomaly detection in ICS Cyber-resilience in critical infrastructure Deep learning for feature selection Industrial control systems security Random forest classifier for ICS

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An energy-aware routing scheme based on a virtual relay tunnel in flying ad hoc networks

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Alexandria Engineering Journal 2024年 91卷 249-260页

作者： Hosseinzadeh, Mehdi Ali, Saqib Mohammed, Adil Hussein Lansky, Jan Mildeova, Stanislava Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Hassan Ahmed, Omed Rahmani, Amir Masoud Mehmood, Asif Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Biomedical Engineering Gachon University Gyeonggi-do Seongnam-si13120 Korea Republic of

Flying ad hoc networks (FANETs) tackle diverse challenges, for example, dynamic topological structure, high mobility of nodes, low density, and energy restrictions. These challenges make problems in designing reliable and stable routing in FANETs. Hence, the design of a routing algorithm is essential for transferring data between drones. In this paper, an energy-aware routing scheme based on a virtual relay tunnel (EARVRT) is proposed in flying ad hoc networks. EARVRT includes a virtual relay tunnel (VRT) that manages relay nodes in the route discovery process and lowers network overhead. Moreover, three metrics, namely route energy, hop counts, and a new metric called path correlation are regarded in the path selection operation. Note that correlation is defined with regard to two scales, including the cosine similarity of two nodes and their distance. EARVRT is evaluated according to delay, network longevity, consumed energy, and packet delivery rate. Then, our scheme is compared with three methods, namely LoCaL, O-LAR, and PSO-GLFR. These results show the superiority and success of EARVRT in comparison with other algorithms. © 2024 The Author(s)

关键词： Drones

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ROAD TRAFFIC Management SOLUTIONS

ROAD TRAFFIC Management SOLUTIONS

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2021 International Conference on information Technology, ICIT 2021

作者： Alkhatib, Ahmad Aa Mohammad, Zeyad Maria, Eman Abu Al Zaytoonah University of Jordan Department of Computer Information Systems Amman Jordan Al Zaytoonah University of Jordan Department of Computer Science Amman Jordan

ISBN: (纸本)9781665428705

Researchers have presented new ideas to effectively handle the traffic jams on the roads and junctions throughout the world, which are expected to increase manifold in the upcoming decades. They have come up with the useful mechanisms, through which, traffic signals can be controlled through active traffic management aiming to bring efficiency in flow of traffic besides enhancing road capacity. The vehicle waiting time and traffic congestion can be curtailed through the smart traffic lights. Hence, gas emissions and fuel consumption can also be reduced. This work discovers the proposed techniques to reduce traffic congestion and optimize road traffic. © 2021 IEEE.

关键词： Traffic signals

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Deep Learning-Based Joint Channel Equalization and Symbol Detection for Air-Water Optoacoustic Communications

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Mahmud, Muntasir Younis, Mohamed Ahmed, Masud Choa, Fow-Sen University of Maryland Baltimore County Baltimore Department of Computer Science and Electrical Engineering BaltimoreMD United States University of Maryland Baltimore County Department of Information Systems BaltimoreMD United States

The optoacoustic effect is triggered by directing an optical signal in the air (using laser) to the surface of water, leading to the generation of a corresponding acoustic signal inside the water. Careful modulation of the laser signal would enable an innovative method for direct communication in air-water cross-medium scenarios experienced in many civil and military applications. In order to achieve a high data rate, a multilevel amplitude modulation scheme can be used to generate different acoustic signals to transmit multiple symbols. However, accurately demodulating these acoustic signals can be challenging due to multipath propagation within the harsh underwater environment, inducing inter-symbol interferences. This paper proposes a deep learning-based demodulation technique that uses a U-Net for signal equalization and a Residual Neural Network for symbol detection. In addition, fine-tuning at the receiver side is also considered to increase the demodulation robustness. The proposed deep learning model has been trained with our laboratory constructed dataset containing eight levels of optoacoustic signals captured from three different underwater positions. The model is validated using two datasets containing severe interference due to multipath-generated echoes and reverberations. The results show that our demodulation model achieves 96.6% and 91.7% accuracy for the two datasets, respectively, which significantly surpasses the 72.9% and 65.30% accuracy achieved by the conventional peak detection-based technique. © 2015 IEEE.

关键词： Intersymbol interference

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Lightweight Spiking Neural Network Based Detector for Interweave Cognitive Radios

Lightweight Spiking Neural Network Based Detector for Interw...

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IEEE International Conference on Communications (ICC)

作者： Md Mehedi Hassan Galib Mohamed Younis Sultan Ahmed Dept. of Computer Science and Electrical and Engineering University of Maryland Baltimore County Baltimore Maryland Information Systems Department University of Maryland Baltimore County Baltimore Maryland

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The major advances in wireless communication technology have led to increased adoption across almost all application domains. However, the massive growth has caused spectrum scarcity despite the fact that many of the frequency bands are not fully-utilized. Cognitive radios have emerged as a viable means to support dynamic spectrum access. Particularly, supporting opportunistic access through passive spectrum monitoring is of great interest. Existing techniques for detecting white space either require modification to commodity radio transceivers, or involve computationally complex models that do not suit resource-constrained devices. This paper opts to fill the technical gap by proposing a novel lightweight white space detector that employs spiking neural networks (SNN). SNN is a bio-inspired technique for creating data-driven models. The proposed design relies on the sensed energy in the medium to determine whether a primary user is active. The validation results using live LTE data demonstrate the effectiveness of our novel detector. Suitability for edge devices is confirmed through implementation on a Raspberry-PI platform.

关键词： Accuracy Biological system modeling Image edge detection Computational modeling Dynamic spectrum access Detectors Spiking neural networks

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Business Start-up: 'CLEVA' application design for employee collaboration training based on android 6

Business Start-up: 'CLEVA' application design for employee c...

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6th International Conference on information Management and Technology, ICIMTech 2021

作者： Oktavia, Tanty Erin Widjaya, Christhoper Arya Hansel Lee, Eugenius Gaol, Ford Lumban Hosoda, Takaaki Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Advanced Institute of Industrial Technology Decision Science Department Tokyo Japan

ISBN: (纸本)9781665449373

E-learning is used as one of the innovations in online learning, but in its implementation e-learning has limitations, especially in terms of flexibility, so that new innovations emerge, namely learning using m-learning because it is more flexible and can be accessed anywhere and anytime. The purpose of writing this study is to analyse problems and business opportunities related to the design of a mobile app-based start-up business in an e-learning system in a company and to design an information system in the form of an Android-based application to answer the analysis of existing problems and opportunities. The methodology used in this study is Rational Unified Process (RUP) with an Object-Oriented Analysis and Design (OOAD) approach which is described by the notation of the Unified Modelling Language (UML). The business analysis used in the writing of this study is the Business Model Canvas (BMC), Marketing Mix, Porter's Five Forces which is complemented by Financial Analysis using the Payback Period (PP), Break Even Point (BEP), and Return on Investment (ROI). In this case, Cleva creates an application system that helps companies carry out training for their employees and helps employees to learn about technical skills and soft skills through mobile-based applications © 2021 IEEE.

关键词： Android (operating system)

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