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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

SN computer Science 2024年第5期5卷 1-11页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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Stereo ORB-SLAM3 Underwater Performance Evaluation

Stereo ORB-SLAM3 Underwater Performance Evaluation

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electrical engineering (ICEENG), International Conference on

作者： Hossam Elkeshky Samer A. Mohamed Mohammed I. Awad Hossam Abd El Munim Mechatronics Engineering Dept. Faculty of Engineering Ain Shams University Cairo Egypt Electrical and Electronic Engineering Dept. Faculty of Engineering and Design University of Bath Bath UK Computer Systems and Engineering Dept. Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9798331519018

ISBN: (纸本)9798331519025

Site exploration and navigation missions in hazardous locations are examples of the widespread applications of autonomous mobile robots. Efficient simultaneous localization and mapping (SLAM) is essential for the functionality of such robots. Visual SLAM (VSLAM) has become a cost-effective and attractive solution for all types of vehicles in the field. ORB-SLAM3 and its preceding versions are prevailing visual SLAM algorithms in recent literature. ORB-SLAM3 builds a three-dimensional map based on images and supplies a continuous estimate of the vehicle’s pose. This paper demonstrates an experimental evaluation of stereo ORB-SLAM3 performance in scenes with diverse backgrounds. Online ORB-SLAM3 is tested in multiple aerial and underwater environments. The performance of the algorithm is evaluated by comparing against ground-truth estimates provided by offline structure-from-motion techniques. Results show that ORB-SLAM3 can function in structured environments providing acceptable accuracy and satisfactory performance given the right conditions. The algorithm achieves average standard deviation values of 4 cm in underwater scenes depending on the complexity of the followed trajectory.

关键词： Visualization Simultaneous localization and mapping Accuracy Three-dimensional displays Structure from motion Pose estimation Trajectory Complexity theory State estimation Standards

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Interactive Framework for Cybersecurity Education and Future Workforce Development

Interactive Framework for Cybersecurity Education and Future...

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Frontiers in Education (FIE) Conference

作者： Sujan Ghimire Muhtasim Alam Chowdhury Ryan Tsang Richard Yarnell Emma Heckert Jaeden Carpenter Yu-Zheng Lin Muntasir Mamun Ronald F. DeMara Setareh Rafatirad Pratik Satam Soheil Salehi Department of Systems and Industrial Engineering University of Arizona Tucson AZ Department of Electrical and Computer Engineering University of Arizona Tucson AZ Department of Electrical and Computer Engineering University of California Davis Davis CA Department of Electrical and Computer Engineering University of Central Florida Orlando FL Department of Computer Science University of California Davis Davis CA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research-to-practice paper presents a novel pedagogical tool for hardware cybersecurity education and workforce development. The growing importance of hardware security has made it essential for individuals and organizations to understand hardware security principles and best practices. However, the current educational curriculum falls short of fulfilling these emerging demands due to the rapidly changing hardware security landscape and limited opportunities for hands-on training. To address these challenges, we propose and have developed the Interactive Hardware and Cybersecurity (I-HaC) Educational Framework, a pedagogical educational framework that supplements existing courses by leveraging generative AI for individualized instruction related to hardware and cybersecurity, data mining, and applied Machine Learning (ML), as well as data visualization to enhance cybersecurity education and workforce development. The framework is designed to be utilized by graduate and undergraduate electrical and computer engineering (ECE) and computer Science (CS) students for a comprehensive introduction to cybersecurity exploits and countermeasures in an interactive manner with hands-on components. Using I-HaC, we have developed tailored lab components for a diverse range of students and intend to release I-HaC as open-source for the benefit of the ECE and CS education community.

关键词： Training Generative AI Databases Hardware security Data visualization Organizations Machine learning Ontologies computer security Protection

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Modeling the effect of hatched grounds on the radio-frequency performance of microstrip lines on flexible substrate

Modeling the effect of hatched grounds on the radio-frequenc...

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作者： Abdelatty, Mohamed Y. Umar, Ashraf Obeidat, Abdullah S. Enakerakpo, Emuobosan Alhendi, Mohammed Poliks, Mark D. Dept. of Systems Science and Industrial Engineering State University of New York Binghamton United States Dept. of Electrical and Computer Engineering State University of New York Binghamton United States

Cross-hatching of PCB grounds refers to a process in which certain ground planes appear as copper lattices;regular openings are placed at regular intervals. Nowadays, the efficacy of ground hatching on rigid PCBs is minimal. However, it is becoming essential for flexible hybrid electronics. Utilizing hatched grounds for flexible applications could offer some benefits. Hatched grounds can perform dual roles, act as ground surfaces, and offer structural support. Hatched grounds are more durable for bending and stretching applications. Also, hatched grounds are desirable in terms of material conservation. In this paper, we focus on studying how hatched grounds affect the RF performance of a straight microstrip line. Simulations show that cross-hatched grounds with more than 50% filling produces good radio-frequency performance. We will study the effect hatching pattern as well as the density of the hatching on the RF performance of the microstrips. The theoretical modeling is considered in this work and fabricating aerosol jet silver ink microstrip lines on PET substrates will be considered as an extension of this work. © 2022 IMAPS-International Microelectronics and Packaging Society. All rights reserved.

关键词： Microstrip lines

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Optimal Deep Learning Based Intruder Identification in industrial Internet of Things Environment

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computer systems Science & engineering 2023年第9期46卷 3121-3139页

作者： Khaled M.Alalayah Fatma S.Alrayes Jaber S.Alzahrani Khadija M.Alaidarous Ibrahim M.Alwayle Heba Mohsen Ibrahim Abdulrab Ahmed Mesfer Al Duhayyim Department of Computer Science College of Science and ArtsSharurahNajran UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Computer Department Applied CollegeNajran UniversityNajran66462Saudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj16273Saudi Arabia

With the increased advancements of smart industries,cybersecurity has become a vital growth factor in the success of industrial *** industrial Internet of Things(IIoT)or Industry 4.0 has revolutionized the concepts of manufacturing and production *** industry 4.0,powerful IntrusionDetection systems(IDS)play a significant role in ensuring network *** various intrusion detection techniques have been developed so far,it is challenging to protect the intricate data of *** is because conventional Machine Learning(ML)approaches are inadequate and insufficient to address the demands of dynamic IIoT ***,the existing Deep Learning(DL)can be employed to identify anonymous ***,the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection(HGSODLID)model for the IIoT *** presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful *** HGSO algorithm is employed for Feature Selection(HGSO-FS)to reduce the curse of ***,Sparrow Search Optimization(SSO)is utilized with a Graph Convolutional Network(GCN)to classify and identify intrusions in the ***,the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN *** proposed HGSODL-ID model was experimentally validated using a benchmark dataset,and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.

关键词： industrial IoT deep learning network security intrusion detection system attribute selection smart factory

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Assessment of the significance of features for the identification of domestic appliances

Assessment of the significance of features for the identific...

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27th International Conference on Electricity Distribution (CIRED 2023)

作者： L. Ma M. Schmidt P. Schegner Technische Universitaet Dresden Institute of Electrical Power Systems and High Voltage engineering Faculty of Electrical and Computer Engineering Dresden Germany

The load identification technologies in the domestic field, also called Non-Intrusive Load Monitoring (NILM), aim to break down the total electricity consumption at household level to the appliance level. This detailed information helps to identify the behaviour of users, deduce energy saving concepts, or provide live assistance for the residents. The NILM approaches are carried out with appliance features, such as the absorbed power, the voltage and current profile. They are defined as appliance-specific characteristics or features, which supposed to make the appliance clearly distinguishable from others. However, most of the publications focus on the identification algorithms. There is barely a contribution to analyse the significance of features. Hereby, the significance means, how consistent and reliable a feature differs from one appliance to another. This paper works on this question. Based on analyses of publications and own measurements, this paper gives an overview of the significance of representative features. Furthermore, suitable features for one-second data are selected and NILM is carried out.

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IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

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computers, Materials & Continua 2023年第2期74卷 3085-3100页

作者： Saeed Masoud Alshahrani Fatma S.Alrayes Hamed Alqahtani Jaber S.Alzahrani Mohammed Maray Sana Alazwari Mohamed A.Shamseldin Mesfer Al Duhayyim Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to *** has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security *** is one of the dreadfulmalicious entities that has affected many users for the past few *** is challenging to recognize Botnet since it has excellent carrying and hidden *** approaches have been employed to identify the source of Botnet at earlier *** Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection *** spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet *** current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat SwarmOptimizer with Deep Learning(BDC-RSODL)*** presented BDC-RSODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter ***,the network data is pre-processed to make it compatible for further ***,RSO algorithm is exploited for effective selection of subset of ***,Long Short TermMemory(LSTM)algorithm is utilized for both identification and classification of ***,Sine Cosine Algorithm(SCA)is executed for fine-tuning the hyperparameters related to LSTM *** order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RSODL system,a comprehensive comparison analysis was *** obtained results confirmed the supremacy of BDCRSODL model over recent approaches.

关键词： Internet of things cloud computing long short termmemory deep learning sine cosine algorithm feature selection

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Modeling the effect of bend radius on the performance of a conformal dual-band mmwave patch antenna array

Modeling the effect of bend radius on the performance of a c...

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作者： Umar, Ashraf Abdelatty, Mohamed Y. Obeidat, Abdullah Enakerakpo, Emuobosan Alhendi, Mohammed Poliks, Mark D. Dept. of Electrical and Computer Engineering State University of New York Binghamton United States Dept. of Systems Science and Industrial Engineering State University of New York Binghamton United States

There is growing interest in the use of additive manufacturing for the fabrication of RF devices due to fast prototyping capabilities and the use of less material as opposed to traditional fabrication techniques. In addition to the previously mentioned advantages, the aerosol jet printing method in particular allows for the conformal printing of RF components. This work models the effect of the bend radius on the return loss and radiation pattern of a dual-band (28 GHz and 39 GHz) mm wave patch antenna array design that will be aerosol jet printed. The antenna is modeled on a 5-mil thick flexible substrate (PET) and the simulation is run from 27 GHz to 40 GHz using HFSS. The return loss and radiation pattern results of the antenna are compared for various bend radii (0.25 in to 2 in) of the conformal surface the antenna is to be printed upon. Simulation results show a maximum frequency deviation of about 400 MHz on the return loss for the low (28 GHz) frequency band of the antenna when it is bent from a bend radius of 0 in to 2 in. © 2022 IMAPS-International Microelectronics and Packaging Society. All rights reserved.

关键词： Microstrip antennas

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An Optimal Text Watermarking Method for Sensitive Detecting of Illegal Tampering Attacks

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computers, Materials & Continua 2022年第3期70卷 5583-5600页

作者： Anwer Mustafa Hilal Fahd N.Al-Wesabi Mohammed Alamgeer Manar Ahmed Hamza Mohammad Mahzari Murad A.Almekhlafi Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of English College of Science&HumanitiesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Faculty of Engineering Department of Electrical EngineeringCommunication EngineeringSana’a UniversityYemen

Due to the rapid increase in the exchange of text information via internet networks,the security and authenticity of digital content have become a major research *** main challenges faced by researchers are how to hide the information within the text to use it later for authentication and attacks tampering detection without effects on the meaning and size of the given digital *** this paper,an efficient text-based watermarking method has been proposed for detecting the illegal tampering attacks on theArabic text transmitted online via an Internet *** this purpose,the accuracy of tampering detection and watermark robustness has been improved of the proposed method as compared with the existing *** the proposed method,both embedding and extracting of the watermark are logically implemented,which causes no change in the digital *** is achieved by using the third level and alphanumeric strategy of the Markov model as a text analysis technique for analyzing the Arabic contents to obtain its features which are considered as the digital *** digital watermark will be used later to detecting any tampering of illegal attack on the received Arabic *** extensive set of experiments using four data sets of varying lengths proves the effectiveness of our approach in terms of detection accuracy,robustness,and effectiveness under multiple random locations of the common tampering attacks.

关键词： Text analysis text-watermarking tampering detection text authentication

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Power-limited Modulation-Estimation with a Helper

Power-limited Modulation-Estimation with a Helper

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IEEE International Symposium on Information Theory

作者： Anatoly Khina Neri Merhav Department of Electrical Engineering-Systems Tel Aviv University Tel Aviv Israel Viterby Faculty of Electrical and Computer Engineering Technion-Israel Institute of Technology Haifa Israel

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

The problem of transmitting a parameter value over an additive white Gaussian noise (AWGN) channel is considered, where, in addition to the transmitter and the receiver, there is a helper that observes the noise non-causally and provides a description of limited rate $R_{\mathrm{h}}$ to the transmitter and/or the receiver. We derive upper and lower bounds on the optimal achievable $\alpha-\mathbf{th}$ moment of the estimation error and show that they coincide for small values of $\alpha$ and for high values of $R_{\mathrm{h}}$ . The upper bound relies on a recently proposed channel-coding scheme that effectively conveys $R_{\mathrm{h}}$ bits essentially error-free and the rest of the rate—over the same AWGN channel without help, with the error-free bits being allocated to the most significant bits of the quantized parameter.

关键词： AWGN channels Estimation error Upper bound Transmitters Noise Receivers Information theory

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