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2021 International Conference on System, Computation, Automation and Networking, ICSCAN 2021

作者： Kesavan, Suresh Manic Al Naimi, Imad Al Attar, Feras Rajinikanth, Venkatesan Kadry, Seifedine National University of Science and Technology College of Engineering Department of Electrical and Communication Engineering Muscat Oman OMR St. Joseph's College of Engineering Department of Electronics and Instrumentation Engineering Chennai600119 India Noroff University College Faculty of Applied Computing and Technology Kristiansand Norway

ISBN: (纸本)9781665439862

COVID19 is one of the hash lung infections;which causes severe pneumonia in humans and untreated infection will lead to death. The goal of this study is to employ an automated Infection-Segmentation-Scheme (ISS) to extract and evaluate the COVID19 lesion on CT scans of the Lungs. This work implemented a Convolution-Neural-Network (CNN) scheme called Res-UNet to study the CT slices of the lungs. The various phases of this research involve in;(i) 3D to 2D conversion and resizing, (ii) Implementation of CNN segmentation scheme, (iii) Comparison of mined COVID19 lesion with Ground-Truth (GT) and (iv) Validation. In this study, 200 CT images (10 patients x 20 slices/patient) of dimension 224× 224× 3 pixels are considered for the assessment and the Image-Quality-Measures (IQM), like Jaccard, Dice ad Accuracy are computed between extracted lesion and the GT. The experimental outcome confirms that the result of Res-UNet is better on sagittal-view of CT compared to axial and coronal. © 2021 IEEE.

关键词： Computerized tomography

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Leveraging K-Means Clustering for Analysis of Arabic Hate Speech Tweets

Leveraging K-Means Clustering for Analysis of Arabic Hate Sp...

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Emerging Technologies (GCET), Global Congress on

作者： Said Salloum Khalaf Tahat Ahmed Mansoori Raghad Alfaisal Dina Tahat School of Science Engineering and Environment University of Salford Manchester UK Media & Creative Industries United Arab Emirates University and Yarmouk University Jordan Media & Creative Industries Department United Arab Emirates University UAE Faculty of Computing and Meta-Technology Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia Applied Sociology Department Al Ain University UAE

ISBN: (数字)9798331542603

ISBN: (纸本)9798331542610

As hate speech is becoming common on social media platforms, it is important to detect, and curb hate speech in order to provide a better and safe environment online. Given the heavy usage of manual methods of hate speech detection, researchers started putting efforts in the direction of machine-learning-based automated methods sooner or later. Many available datasets and models on hate speech detection are largely inadequate for Arab hate speech because of the complexity of language and cultural nuances. In this paper, the researchers eased these difficulties, as they, used the K-Means to apply compilation for Arab hate speech in the L-HSAB dataset on the tweets. Methodology consisted of Prospecting and Pre-processing, Term Frequency Inverse Document frequency (TF-IDF) dimension reductions, through Principal Component Analysis (PCA), and assembly via K-Means. They helped to identify different sets of hate speech tweets and understand the common topics and topics. It has led to new understandings of how Arab hate speech flows on the Internet and now offers the potential for tailored interventions. Automated hate speech analysis via machine learning would allow policymakers to formulate tailored modification strategies focused on making the Internet safer and the community more harmonious.

关键词： Dimensionality reduction Social networking (online) Hate speech Manuals Internet Complexity theory Cultural differences Assembly Principal component analysis

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Strengthening the relationship between community resilience and health emergency communication: a systematic review

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BMC global and public health 2024年第1期2卷 79页

作者： Tushna Vandrevala Elizabeth Morrow Tracey Coates Richard Boulton Alison F Crawshaw Emma O'Dwyer Carrie Heitmeyer Centre for Applied Health and Social Care Research Faculty of Health Science Social Care and Education Kingston University Kingston Upon Thames UK. t.vandrevala@kingston.ac.uk. Independent Researcher & Expert in Residence Inclusive Research and Cultures Kingston University Kingston Upon Thames UK. School of Built Environment and Geography Faculty of Engineering Computing and the Environment Kingston University Kingston Upon Thames UK. Centre for Applied Health and Social Care Research Faculty of Health Science Social Care and Education Kingston University Kingston Upon Thames UK. School of Health and Medical Sciences City St George's University of London London UK. Institute for Infection and Immunity St George's University of London London UK. Institute for Lifecourse Development University of Greenwich London UK. Department of Anthropology London School of Economics and Political Science London UK.

BACKGROUND:Community resilience and health emergency communication are both crucial in promoting a community's ability to endure crises and recover from emergency events. Yet, a notable gap in theory and evidence exists in the relationship between them. We aim to explore the relationship between community resilience and health emergency communication and to identify strategies and interventions to strengthen their usefulness to each other. Based on the results, a secondary aim was to develop a model of community-centred resilience and health emergency communication. METHODS:A systematic review of literature published between January 1990 and February 2024 was undertaken following Joanna Briggs Institute guidelines. Electronic databases (Web of science, Social science Citation Index, PubMed/MEDLINE) were searched using key terms. Eligibility criteria were developed from the literature and the knowledge of the multidisciplinary team. Inductive thematic analysis generated key themes. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were applied to present the findings. RESULTS:The searches identified 300 articles, of which 86 met the inclusion criteria. Two main themes were identified from the literature: (i) the relationship between emergency communication and community resilience, including subthemes: building trust and collaboration within communities, identifying resources and their distribution, tailoring communication strategies, considering inclusion and equity, and community engagement and feedback and (ii) strategies and interventions, including subthemes: facilitating community structures as channels for communication, respecting personal and private boundaries in health communication, targeting outreach for effective crisis communication, building resilience through training and communication initiatives, and demonstrating commitment to equity and inclusion. CONCLUSIONS:There is a small, yet valuable, body of evidenc

关键词： Community resilience Emergency communication Equitable recovery Health emergency Inclusive risk communication Vulnerability

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RETRACTED ARTICLE: Intrusion detection based on machine learning in the internet of things, attacks and counter measures

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The Journal of Supercomputing 2022年第6期78卷 8890-8924页

作者： Rehman, Eid Haseeb-ud-Din, Muhammad Malik, Arif Jamal Khan, Tehmina Karmat Abbasi, Aaqif Afzaal Kadry, Seifedine Khan, Muhammad Attique Rho, Seungmin Department of Software Engineering Foundation University Islamabad Islamabad Pakistan Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway Department of Computer Science HITEC University Taxila Taxila Pakistan Chung-Ang University - Seoul Campus: Chung-Ang University Seoul Korea

Globally, data security and privacy over the Internet of Things (IoT) are necessary due to its emergence in daily life. As the IoT will soon invade each part of our lives, attention to IoT security is significant. The nature of attacks is dynamic, and addressing this requires designing dynamic methods and a self-adaptable scheme to discover security attacks from malicious use of IoT equipment. The best detection mechanism against attacks from compromised IoT devices includes machine learning techniques. This study emphasizes the latest literature on attack types and uses a scheme based on machine learning for network support in IoT and intrusion detection. Therefore, the current work includes a thorough analysis of multiple intelligence methods and their deployed architectures of network intrusion detection, focusing on IoT attacks and machine learning-based intrusion detection schemes. Moreover, it explores methods based on machine learning appropriate for identifying IoT devices associated with cyber attacks.

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Analysis of Fraudulent Job Postings Using Machine Learning

Analysis of Fraudulent Job Postings Using Machine Learning

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Intelligent computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Said Salloum Khalaf Tahat Ahmed Mansoori Raghad Alfaisal Dina Tahat School of Science Engineering and Environment University of Salford Manchester UK Media & Creative Industries United Arab Emirates University and Yarmouk University Jordan Media & Creative Industries department United Arab Emirates University UAE Faculty of Computing and Meta-Technology Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia Applied sociology department Al Ain University UAE

ISBN: (数字)9798350354690

ISBN: (纸本)9798350354706

In the age of digital recruitment, the proliferation of fraudulent job postings poses significant challenges for job seekers and legitimate employers alike. These deceptive listings not only waste time and resources but also endanger personal data and propagate scams. Addressing this issue, we present a comprehensive machine learning methodology to accurately discern between genuine and counterfeit job opportunities. Leveraging a rich dataset procured from Kaggle, this paper details the deployment of a logistic regression classifier, judiciously trained on a fusion of textual and meta-features extracted from job advertisements. The classifier underwent rigorous evaluation, manifesting an impressive accuracy of 96.78% in segregating authentic posts from fraudulent ones. The implementation of Term Frequency-Inverse Document Frequency (TF-IDF) vectorization on textual data, alongside meta-features such as job description length, enabled the model to learn and predict with high precision. The implications of this research are substantial, offering a scalable and efficient tool for job platforms to safeguard their users and ensure the integrity of their listings.

关键词： Logistic regression Accuracy Computational modeling Machine learning Predictive models Data models Recruitment

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Deep Learning Scheme for Modulation Classification under Fading Channels

Deep Learning Scheme for Modulation Classification under Fad...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： Sajjad A. Ghauri Mubashar Sarfraz Gunjan Chhabra Keshav Kaushik Asim Zafar Inam Ullah Khan School of Engineering & Applied Sciences ISRA University Islamabad Pakistan Faculty of Engineering and Computing NUML University Islamabad Pakistan Department of CSE Graphic Era Hill University Graphic Era Deemed to be University Dehradun Uttarakhand India Amity School of Engineering and Technology Amity University Punjab India Department of Computer Science National University of Technology Islamabad Pakistan

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The field of automatic modulation classification using deep neural networks has undergone significant development in recent years. In this research, M-QAM and N-PSK modulation formats have been considered for classification. The deep neural network architecture is directly fed with raw signals that are often corrupted with channel noise and fading effects such as Rayleigh and Nakagami. The proposed classifier structure also incorporates normalized cumulants of higher order from the received signal. The proposed DNN architecture’s performance is evaluated by comparing its efficiency for raw data and extracted features-based classification. We have validated the classifier efficiency through three different cases. Our proposed classifier demonstrates remarkable capability in efficiently classifying modulation formats.

关键词： Nakagami distribution Deep learning Noise Modulation Artificial neural networks Computer architecture Rayleigh channels Feature extraction Robustness Data mining

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Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data

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Computer Systems science & Engineering 2023年第12期47卷 2741-2757页

作者： Abdelwahed Motwakel Hala J.Alshahrani Jaber S.Alzahrani Ayman Yafoz Heba Mohsen Ishfaq Yaseen Amgad Atta Abdelmageed Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and *** of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so *** is expressed in several means,like facial and speech expressions,gestures,and written *** recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)*** article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English Twitter Data in this *** presented DHODBN-EC model aims to examine the existence of distinct emotion classes in *** the introductory level,the DHODBN-EC technique pre-processes the tweets at different ***,the word2vec feature extraction process is applied to generate the word embedding *** emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class ***,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN *** extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.

关键词： Deer hunting optimization deep belief network emotion classification Twitter data sentiment analysis english corpus

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Backward Radiation Reduction and Bandwidth Enhancement of Metamaterial Antenna for UWB Applications

Backward Radiation Reduction and Bandwidth Enhancement of Me...

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2021 Emerging Technology in computing, Communication and Electronics, ETCCE 2021

作者： Uddin, M. Jasim Ullah, M. Habib Bentley, Barry L. Shakib, M.N. Islam, Syed Zahurul Rahman, Md Arafatur Cardiff Metropolitan University Cardiff School of Technologies Department of Applied Computing and Engineering United Kingdom University of Malaya Faculty of Engineering Department of Electrical Engineering Kuala Lumpur50603 Malaysia Cardiff Metropolitan University Cardiff School of Technologies Department of Computer Science United Kingdom Green University of Bangladesh Faculty of Science and Engineering Dept. of Electrical and Electronics Engineering Dhaka1207 Bangladesh University Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Power Integration Systems Johor Malaysia Faculty of Science and Engineering University of Wolverhampton United Kingdom

ISBN: (纸本)9781665483643

A metamaterial inspired planar-patterned microstrip patch antenna, feeding on a Duroid substrate inset bottom ground soft-surface, is presented for ultra-wide band (UWB) applications. The present antenna is configured on a step-edge periodic unit-cell pattern, with a tiny air gap etched on the metal patch and finite ground plane. By employing these structures an ultra-wide band from 4 GHz ~ 16.2 GHz is achieved. The important advantage is retaining the suppression of back radiation while removing grounded edge metallic parts and replacing them with the finite cut-off square unit cell. The feeding ground plane and radiating patch are specially configured to develop artificial structures using series capacitance and shunt inductance. The efficiency of the proposed antenna is above 99.1%. To enhance gain and bandwidth, this antenna has etched Jerusalem crossed slots on the ground plane and star-slots on the patch, with the enhanced gain reaching a maximum of 8 dBi at 9.2 GHz. The antenna's overall dimensions exhibit 0.72λ × 0.83λ × 0.05λ at a 10.1 GHz cut-off frequency. The high absorption gain, directivity, and enhanced frequency bandwidth, verified by numerical modeling and experimentation, ensure the proposed antenna is well suited to any UWB application. © 2021 IEEE.

关键词： Metamaterial antennas

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Application level performance evaluation of wearable devices for stress classification with explainable AI

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Pervasive and Mobile computing 2022年 87卷

作者： Chalabianloo, Niaz Can, Yekta Said Umair, Muhammad Sas, Corina Ersoy, Cem Department of Computer Engineering Boğaziçi University Istanbul Turkey Faculty of Applied Computer Science Universität Augsburg Augsburg Germany Newcastle University Newcastle Upon Tyne United Kingdom School of Computing and Communications Lancaster University Lancaster United Kingdom

Stress has become one of the most prominent problems of modern societies and a key contributor to major health issues. Dealing with stress effectively requires detecting it in real-time, informing the user, and giving instructions on how to manage it. Over the past few years, wearable devices equipped with biosensors that can be utilized for stress detection have become increasingly popular. Since they come with various designs and technologies and acquire biosignals from different body locations, choosing a suitable device for a particular application has become a challenge for researchers and end-users. This study compares seven common wearable biosensors for stress detection applications. This was accomplished by collecting physiological sensor data during Baseline, Stress, Recovery, and Cycling sessions from 32 participants. Machine learning algorithms were used to classify four stress classes, and the results obtained from all wearables were compared. Following this, a state-of-the-art explainable artificial intelligence method was employed to clarify our models’ predictions and investigate the influence different features have on the models’ outputs. Despite the results showing that ECG wearables perform slightly better than the rest of the devices, adding a second biosignal (EDA) improved the results significantly, tipping the balance toward multisensor wearables. Finally, we concluded that although the output results of each model can be affected by various factors, in most cases, there is no significant difference in the accuracy of stress detection by different wearables. However, the decision to select a particular wearable for stress detection applications must be made carefully considering the trade-off between the users’ expectations and preferences and the pros and cons of each device. © 2022

关键词： Stresses

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Probing high school students’ understanding of interference and diffraction of light using standard wave optics experiments

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Physical Review Physics Education Research 2023年第2期19卷 020118-020118页

作者： Karolina Matejak Cvenic Lana Ivanjek Maja Planinic Katarina Jelicic Ana Susac Martin Hopf Mateja Cindric Brkic Department of Physics Faculty of Science University of Zagreb Bijenicka 32 HR—10000 Zagreb Croatia School of Education JKU Linz Altenberger Straße 69 A-4040 Linz Austria Department of Applied Physics Faculty of Electrical Engineering and Computing University of Zagreb Unska 3 10000 Zagreb Croatia University of Vienna Austrian Educational Competence Centre Physics & Faculty of Physics Porzellangasse 4/2/2 1090 Wien Austria Gornjogradska gimnazija Trg Katarine Zrinske 5 HR-10000 Zagreb Croatia

Demonstration interviews with 27 high school students (18–19 years old) were conducted in Zagreb, Croatia, using several standard experiments on interference and diffraction of light. Students were asked for their predictions, observations, and explanations of the experiments. In this process, many student difficulties were identified, both regarding their understanding of interference and diffraction of light, but also regarding their skills of systematic observation and description of experimental patterns. The observed difficulties were analyzed in the resource-based model, suggesting the activation of some p prims, as well as other cognitive resources in the process of students’ attempts to predict, describe, and explain the interference and diffraction patterns.

关键词： Concepts & principles

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