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Tracking Dengue on Twitter Using Hybrid Filtration-Polarity and Apache Flume

computer Systems Science & engineering 2022年第3期40卷 913-926页

作者： Norjihan Binti Abdul Ghani Suraya Hamid Muneer Ahmad Younes Saadi N.Z.Jhanjhi Mohammed A.Alzain Mehedi Masud Department of Information Systems Faculty of Computer Science&Information TechnologyUniversiti Malaya50603Kuala LumpurMalaysia School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

The world health organization(WHO)terms dengue as a serious illness that impacts almost half of the world’s population and carries no specific *** and accurate detection of spread in affected regions can save precious *** the severity of the disease,a few noticeable works can be found that involve sentiment analysis to mine accurate intuitions from the social media text ***,the massive data explosion in recent years has led to difficulties in terms of storing and processing large amounts of data,as reliable mechanisms to gather the data and suitable techniques to extract meaningful insights from the data are *** research study proposes a sentiment analysis polarity approach for collecting data and extracting relevant information about dengue via Apache *** method consists of two main parts:the first part collects data from social media using Apache Flume,while the second part focuses on querying and extracting relevant information via the hybrid filtration-polarity algorithm using Apache *** overcome the noisy and unstructured nature of the data,the process of extracting information is characterized by pre and post-filtration *** a result,only with the integration of Flume and Hive with filtration and polarity analysis,can a reliable sentiment analysis technique be offered to collect and process large-scale data from the social *** introduce how the Apache Hadoop ecosystem–Flume and Hive–can provide a sentiment analysis capability by storing and processing large amounts of *** important finding of this paper is that developing efficient sentiment analysis applications for detecting diseases can be more reliable through the use of the Hadoop ecosystem components than through the use of normal machines.

关键词： Big data analysis data filtration text analysis sentiment analysis social media event detection

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Enhanced Physical Layer Security for Full-Duplex Symbiotic Radio with AN Generation and Forward Noise Suppression

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IEEE Transactions on Communications 2024年第7期72卷 3905-3918页

作者： Jin, Chi Chang, Zheng Hu, Fengye Chen, Hsiao-Hwa Hamalainen, Timo University of Jyväskylä Faculty of Information Technology Jyväskylä40014 Finland University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Jilin University College of Communication Engineering Changchun130012 China National Cheng Kung University Department of Engineering Science Tainan70101 Taiwan

Due to the constraints on power supply and limited encryption capability, data security based on physical layer security (PLS) techniques in backscatter communications has attracted a lot of attention. In this work, we propose to enhance PLS in a full-duplex symbiotic radio (FDSR) system with a proactive eavesdropper, which may overhear the information and interfere legitimate communications simultaneously by emitting attack signals. To deal with the eavesdroppers, we propose a security strategy based on pseudo-decoding and artificial noise (AN) injection to ensure the performance of legitimate communications through forward noise suppression. A novel AN signal generation scheme is proposed using a pseudo-decoding method, where AN signal is superimposed on data signal to safeguard the legitimate channel. The phase control in the forward noise suppression scheme and the power allocation between AN and data signals are optimized to maximize security throughput. The formulated problem can be solved via problem decomposition and alternate optimization algorithms. Simulation results demonstrate the superiority of the proposed scheme in terms of security throughput and attack mitigation performance. 0090-6778 © 2024 IEEE.

关键词： Backscattering

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Movie Recommendation System Using Euclidean Distance

Movie Recommendation System Using Euclidean Distance

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Khan, Muneeb Sami Hussain, Zunnurain Amaad, Muhammad Irtaza Hasan, Muhammad Zulkifl Khalid, Aqsa Chuhan, Saad Hussain Awan, Rimsha Yaqub, Muhammad Atif Ashraf, Farhan Information Technology University Department of Computer Engineering Lahore Pakistan Bahria University Lahore Campus Dept. of Computer Science Pakistan University of Central Punjab Faculty of Information Technology India National College of Business Administration and Economics Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350373783

In this study, we pivot from the commonly used cosine similarity to the Euclidean distance as the foundation for our film recommendation algorithm. Initially, data pertaining to users, films, and ratings are loaded using the panda's library. The algorithm then computes normalized ratings, tabulates the number of films in each genre, and visualizes this distribution. The frequency of ratings for each genre is similarly plotted. A dictionary is maintained to store movie IDs for each genre. These values are then integrated into the normalized rating matrix. For each genre, the normalized ratings of movies are cataloged in a lexicon. Using the normalized ratings matrix and movie IDs, a Data Frame is generated. Subsequently, the movie and ratings datasets are merged. A pivot table captures the number of films each user has watched per genre. The study also be happy with their purchase if these recommendations are tailored to their needs. As a result, the client would utilize this application once more. Because of the significant money gained determines the average number of films a user watches in each genre. This pivot table is refined to display movies in each genre that a user has watched, but only those exceeding the genre's average viewership. Instead of cosine similarity, the Euclidean distance is computed to gauge the similarity between movies, offering a fresh perspective on recommendation systems. © 2024 IEEE.

关键词： ContentBased Filtering Data Normalization Euclidean Distance Genre Analysis Machine Learning Algorithms Movie Recommendation System Personalized Recommendations Similarity Measurement User Preferences User Rating Analysis

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"Hand Model" - A Static Sinhala Sign Language Translation Using Media-Pipe and SVM Compared with Hybrid Model of KNN, SVM and Random Forest Algorithms 5

"Hand Model" - A Static Sinhala Sign Language Translation Us...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Gedaragoda, G.H.M.S.C. Wijesinghe, W.R.S. Abeywickrama, T.R. Lokuliyana, Shashika Mahaadikara, Hansika Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

The study of sign language recognition has been a thriving area of research for nearly twenty years. SSL translation using computer vision relies on extensive training using large number of images or video sequences. Yet, it poses a challenge for languages with limited resources, like Sinhala Sign Language. Due to the absence of accessible datasets for such investigations and lack of SSL recognition systems for real-time scenarios, the authors of this research have developed a completed system that consist of three components which are, static sign recognition, dynamic sign recognition and natural language processing to output a meaningful sentence. This paper describes the models and the results which are developed for static sign recognition. The final model for the Sinhala Static recognition has developed using media-pipe and SVM algorithm. And to compare the results and performance authors have used CNN, SVM, KNN and Random-forest algorithms to compare the results and performance. In this research, authors have made a valuable contribution by introducing a new dataset and developing a system for translating static SSL that gives 99.67% of accuracy and best performance when compared to the other two models and algorithms described in this paper. © 2023 IEEE.

关键词： Support vector machines

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Deep Learning-Based Approach for Arabic Visual Speech Recognition

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computers, Materials & Continua 2022年第4期71卷 85-108页

作者： Nadia H.Alsulami Amani T.Jamal Lamiaa A.Elrefaei Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical Engineering Department Faculty of Engineering at ShoubraBenha UniversityCairo11629Egypt

Lip-reading technologies are rapidly progressing following the breakthrough of deep *** plays a vital role in its many applications,such as:human-machine communication practices or security *** this paper,we propose to develop an effective lip-reading recognition model for Arabic visual speech recognition by implementing deep learning *** Arabic visual datasets that have been collected contains 2400 records of Arabic digits and 960 records of Arabic phrases from 24 native *** primary purpose is to provide a high-performance model in terms of enhancing the preprocessing ***,we extract keyframes from our ***,we produce a Concatenated Frame Images(CFIs)that represent the utterance sequence in one single ***,the VGG-19 is employed for visual features extraction in our proposed *** have examined different keyframes:10,15,and 20 for comparing two types of approaches in the proposed model:(1)the VGG-19 base model and(2)VGG-19 base model with batch *** results show that the second approach achieves greater accuracy:94%for digit recognition,97%for phrase recognition,and 93%for digits and phrases recognition in the test ***,our proposed model is superior to models based on CFIs input.

关键词： Convolutional neural network deep learning lip reading transfer learning visual speech recognition

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Automated Waste Classification using Convolutional Neural Network 14

Automated Waste Classification using Convolutional Neural Ne...

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14th International Conference on Software technology and engineering, ICSTE 2024

作者： Thinakaran, Rajermani Somasekar, J. Neerugatti, Vikram Guru Saran, P. INTI International University Faculty of Data Science and Information Technology Negeri Sembilan Malaysia Department of Computer Science and Engineering Karnataka Banglore India

ISBN: (纸本)9798350378955

Efficient waste management relies on precise waste classification, which facilitates recycling, composting, and appropriate disposal. Conventional waste classification methods often involve manual sorting or simple rule-based systems, which are labor intensive and prone to errors. This study investigates the application of Convolutional Neural Networks (CNNs) for automated waste classification using image data. CNNs have shown significant success in various image classification tasks due to their ability to learn hierarchical features directly from pixel values. The methodology outlined covers data collection, preprocessing, model architecture design, and training procedures for the CNN model. Experimental results demonstrate the effectiveness of the CNN approach in accurately categorizing various waste types. Additionally, we discuss the practical implications of CNN-based waste classification systems for real-world waste management applications. By leveraging CNNs, waste management processes can be optimized, leading to better resource utilization and environmental sustainability. The authors also highlight potential avenues for future research, including exploring advanced CNN architectures and integrating emerging technologies like Internet of Things (IoT) sensors for improved waste management solutions. Overall, this study emphasizes the potential of CNNs to transform waste classification practices and enhance the efficiency of waste management systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Dataset of Large Gathering Images for Person Identification and Tracking

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computers, Materials & Continua 2023年第3期74卷 6065-6080页

作者： Adnan Nadeem Amir Mehmood Kashif Rizwan Muhammad Ashraf Nauman Qadeer Ali Alzahrani Qammer H.Abbasi Fazal Noor Majed Alhaisoni Nadeem Mahmood Faculty of Computer and Information System Islamic University of MadinahMadinah42351Saudi Arabia Department of Computer Science and Information Technology Sir Syed University of Engineering and TechnologyKarachi75300Pakistan Department of Computer Science Federal Urdu University of ArtsScience&TechnologyIslamabad45570Pakistan Department of Physics Federal Urdu University of ArtsScience&TechnologyKarachi75300Pakistan James Watt School of Engineering University of GlasgowGlasgowG128QQUK Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science University of KarachiKarachi75270Pakistan

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi,Madinah,Saudi *** dataset consists of raw and processed images reflecting a highly challenging and unconstraint *** methodology for building the dataset consists of four core phases;that include acquisition of videos,extraction of frames,localization of face regions,and cropping and resizing of detected face *** raw images in the dataset consist of a total of 4613 frames obtained fromvideo *** processed images in the dataset consist of the face regions of 250 persons extracted from raw data images to ensure the authenticity of the presented *** dataset further consists of 8 images corresponding to each of the 250 subjects(persons)for a total of 2000 *** portrays a highly unconstrained and challenging environment with human faces of varying sizes and pixel quality(resolution).Since the face regions in video sequences are severely degraded due to various unavoidable factors,it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research *** have also gathered and displayed records of the presence of subjects who appear in presented frames;in a temporal *** can also be used as a temporal benchmark for tracking,finding persons,activity monitoring,and crowd counting in large crowd scenarios.

关键词： Large crowd gatherings a dataset of large crowd images highly uncontrolled environment tracking missing persons face recognition activity monitoring

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Forecasting the Impact of Deep Learning on Data Science Practices: A Comprehensive Analysis 15

Forecasting the Impact of Deep Learning on Data Science Prac...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Acharjya, Kalyan Devaraj Verma, C. Umamaheswari, B. Shelke, Priya M. Kumar, Mukesh Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Vishwakarma Institute of Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India

ISBN: (纸本)9798350370249

It comprehensively discusses the effect of deep mastering on data technology practices. Deep studying has advanced to become a powerful tool for information mining, pattern recognition, and feature engineering. It is miles information scientists are increasingly using the accuracy and efficiency of information science initiatives. Furthermore, the era has stepped forward hastily to one of these excessive levels. Many leading facts scientists throughout numerous domains have begun to contain deep mastering into their facts technological know-how workflows. In this paper, we examine and forecast the modern-day and destiny impact of deep gaining knowledge of data technology practices. We start by analyzing the distinct advantages of deep learning, including progressed accuracy, reduced guide attempt, more suitable automation, and advanced scalability. We then become aware of extraordinary applications in which deep learning is being applied and evaluate its impact on the respective information science pipelines. Sooner or later, we provide a comprehensive forecast of the capability applications of deep getting to know within the records technology subject inside the near and far destiny. Notably, we recall the capacity of deep studying to force essential modifications in how information mining, analytical choice-making, and large records packages. Through our analysis, we will forecast the ability of deep studying to revolutionize statistics technological know-how practices in extraordinary ways. © 2024 IEEE.

关键词： Automation Practices Processing technology Unprecedented

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A deep learning model framework for diabetic retinopathy detection

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International Journal of Networking and Virtual Organisations 2022年第2期27卷 107-124页

作者： Padmapriya, M. Pasupathy, S. Sumathi, R. Punitha, V. Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Tamilnadu Annamalai Nagar India Department of Information Technology Saranathan College of Engineering Tamilnadu India

Diabetic retinopathy (DR) is the typical diabetic eye issue and a main reason of blindness around the world. As per the International Diabetes Federation (IDF), the rates of diabetes would rise to 552 million by 2034. Breakthroughs in computer science techniques inclusive of artificial intelligence (AI) and deep learning (DL) have multiplied opportunities for early detection of DR. This indicates that the likelihood of a patient's healing will improve, and the risk of eyesight loss could be minimised in due course. A deep learning model (ResNet) for medical DR detection was examined in this article. The dataset of Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 was used to train and test the DL model. To demonstrate the vitality of the chosen ResNet model, performance measures and testing accuracy like recall, precision, and F1 score were determined. The modified ResNet model attained a testing accuracy of around 84% even with only a few dataset images. Also, training time and computational complexity were reduced with this simpler model. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Statistical tests

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Developing Engagement in the Learning Management System Supported by Learning Analytics

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computer Systems Science & engineering 2022年第7期42卷 335-350页

作者： Suraya Hamid Shahrul Nizam Ismail Muzaffar Hamzah Asad W.Malik Department of Information System Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Faculty of Computing and Informatics Universiti Malaysia SabahSabahMalaysia Department of Computing School of Electrical Engineering and Computer Science(SEECS)National University of Sciences and Technology(NUST)IslamabadPakistan

Learning analytics is an emerging technique of analysing student par-ticipation and *** recent COVID-19 pandemic has signiﬁcantly increased the role of learning management systems(LMSs).LMSs previously only complemented face-to-face teaching,something which has not been possible between 2019 to *** date,the existing body of literature on LMSs has not analysed learning in the context of the pandemic,where an LMS serves as the only interface between students and ***,productive results will remain elusive if the key factors that contribute towards engaging students in learning are notﬁrst identiﬁ***,this study aimed to perform an exten-sive literature review with which to design and develop a student engagement model for holistic involvement in an *** required data was collected from an LMS that is currently utilised by a local Malaysian *** model was validated by a panel of experts as well as discussions with *** is our hope that the result of this study will help other institutions of higher learning determine factors of low engagement in their respective LMSs.

关键词： Engagement analysis learning analytics learning management system student engagement

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