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Dengue Cases Prediction and Analysis using LSTM and Region-specific Data

Dengue Cases Prediction and Analysis using LSTM and Region-s...

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

作者： Ud Deen Shafique, Hafiz Noor Ali, Nauman Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Ashraf, Farhan Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Lahore Campus Dept. of Computer Science Zunnurain Pakistan University of Central Punjab Lahore Faculty of Information Technology Department of Computer Science Pakistan National College of Business Administration and Economics Department of Computer Science Lahore Pakistan Information Technology University Lahore Pakistan

ISBN: (纸本)9798350373783

Dengue Fever presents a substantial global health challenge, necessitating the development of accurate predictive models for effective disease management. This research paper conducts a comprehensive analysis of a Long ShortTerm Memory (LSTM) neural network-based model for forecasting Dengue cases in the Philippines. The study includes data preprocessing, model development using TensorFlow and Keras, and thorough evaluation using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Rsquared (R2). The dataset, 'Dengue Cases in the Philippines,' provides a detailed historical record of Dengue fever cases and fatalities across various regions, enabling a thorough analysis of temporal and spatial dynamics. Data preprocessing involves label encoding, normalization, and careful data splitting to ensure accurate model training and testing. The LSTM neural network architecture is optimized through hyperparameter tuning, including learning rate, batch size, and epochs, to improve prediction accuracy. Model performance is rigorously evaluated, with an MAE of 913, indicating areas for further improvement. While the model shows promise, there is room for refinement. This study contributes to the field by exploring the feasibility of LSTM models for Dengue prediction, with insights that may inform future research in disease forecasting and public health strategies. Limitations, ethical considerations, and recommendations for further work are discussed. © 2024 IEEE.

关键词： Electronic health record

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Text Augmentation-Based Model for Emotion Recognition Using Transformers

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computers, Materials & Continua 2023年第9期76卷 3523-3547页

作者： Fida Mohammad Mukhtaj Khan Safdar Nawaz Khan Marwat Naveed Jan Neelam Gohar Muhammad Bilal Amal Al-Rasheed Department of Computer Science The University of HaripurHaripur22620Pakistan Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and Technology PeshawarPeshawar25120Pakistan Department of Electronics Engineering Technology University of TechnologyNowshera24100Pakistan Department of Computer Science Shaheed Benazir BhuttoWomen UniversityPeshawar25000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Emotion Recognition in Conversations(ERC)is fundamental in creating emotionally ***-BasedNetwork(GBN)models have gained popularity in detecting conversational contexts for ERC ***,their limited ability to collect and acquire contextual information hinders their *** propose a Text Augmentation-based computational model for recognizing emotions using transformers(TA-MERT)to address *** proposed model uses the Multimodal Emotion Lines Dataset(MELD),which ensures a balanced representation for recognizing human *** used text augmentation techniques to producemore training data,improving the proposed model’s *** encoders train the deep neural network(DNN)model,especially Bidirectional Encoder(BE)representations that capture both forward and backward contextual *** integration improves the accuracy and robustness of the proposed ***,we present a method for balancing the training dataset by creating enhanced samples from the original *** balancing the dataset across all emotion categories,we can lessen the adverse effects of data imbalance on the accuracy of the proposed *** results on the MELD dataset show that TA-MERT outperforms earlier methods,achieving a weighted F1 score of 62.60%and an accuracy of 64.36%.Overall,the proposed TA-MERT model solves the GBN models’weaknesses in obtaining contextual data for ***-MERT model recognizes human emotions more accurately by employing text augmentation and transformer-based *** balanced dataset and the additional training samples also enhance its *** findings highlight the significance of transformer-based approaches for special emotion recognition in conversations.

关键词： Emotion recognition in conversation graph-based network text augmentation-basedmodel multimodal emotion lines dataset bidirectional encoder representation for transformer

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Query Optimization Framework for Graph Database in Cloud Dew Environment

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computers, Materials & Continua 2023年第1期74卷 2317-2330页

作者： Tahir Alyas Ali Alzahrani Yazed Alsaawy Khalid Alissa Qaiser Abbas Nadia Tabassum Department of Computer Science Lahore Garrison UniversityLahorePakistan Faculty of Computer Science and Information Systems Islamic University Madinah Madinah42351Saudi Arabia Networks and Communications Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science Virtual University of PakistanLahorePakistan

The query optimizer uses cost-based optimization to create an execution plan with the least cost,which also consumes the least amount of *** challenge of query optimization for relational database systems is a combinatorial optimization problem,which renders exhaustive search impossible as query sizes *** in CPU performance have surpassed main memory,and disk access speeds in recent decades,allowing data compression to be used—strategies for improving database performance *** performance enhancement,compression and query optimization are the two most *** reduces the volume of data,whereas query optimization minimizes execution *** the database reduces memory requirement,data takes less time to load into memory,fewer buffer missing occur,and the size of intermediate results is more *** paper performed query optimization on the graph database in a cloud dew environment by considering,which requires less time to execute a *** factors compression and query optimization improve the performance of the *** research compares the performance of MySQL and Neo4j databases in terms of memory usage and execution time running on cloud dew servers.

关键词： Query optimization compression cloud dew decompression graph database

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Fully Mixed Max-Average Pooling: A Comparative Study for a Convolutional Neural Network 6

Fully Mixed Max-Average Pooling: A Comparative Study for a C...

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6th International Conference on Intelligent Systems and computer Vision, ISCV 2024

作者： Ait Skourt, Brahim Nikolov, Nikola S. Majda, Aicha Computer Science Department Faculty of Science and Technology University of Sidi Mohammed Ben Abdellah Fez Morocco Department of Computer Science and Information Systems University of Limerick Limerick Ireland

ISBN: (纸本)9798350350180

As an integral part of a convolutional neural network, the pooling layers are responsible for the down-sampling operation which aims at preventing overfitting. Besides the conventional pooling methods (max and average), various methods that involve mixing max pooling and average pooling have been proposed. In this work, we propose a new mixed pooling method, called fully mixed max-average pooling (FMMAP), and evaluate its performance within a comparative study of various conventional and state-of-the-art pooling methods. FMMAP consists of fully mixing max pooling and average pooling features instead of stochastically selecting them as done in other popular mixed-pooling methods. The experimental results suggest that FMMAP outperforms the conventional pooling methods in accuracy, and while being very close (within 0.05%) to the other mixed-pooling methods accuracy-wise, it significantly outperforms them in terms of running time, being at least 1.7 times and up to 2.7 times faster. © 2024 IEEE.

关键词： Convolutional neural networks

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Pinch Force Measurement Using a Geomagnetic Sensor

Pinch Force Measurement Using a Geomagnetic Sensor

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Yamamoto, Sarii Ikematsu, Kaori Kato, Kunihiro Sugiura, Yuta Keio University Faculty of Information and Computer Science Kanagawa Japan Ly Corporation Tokyo Japan Tokyo University of Technology Faculty of Media Science Tokyo Japan

ISBN: (纸本)9798350312072

Hand muscle strength, such as grip strength, pinch strength, and holding strength is an important indicator when determining hand diseases, treatment, and rehabilitation methods. However, pinch measurement devices for one type of hand muscle strength are expensive and primarily for medical purposes, so the general public does not typically use them. In this study, we propose a pinch measurement method that uses only a smartphone and a measurement device with an embedded magnet. We created a measurement device in which the magnet position changes when force is applied. We also used a magnetic sensor built into a smartphone to estimate pinch force based on the amount of change in the magnetic field from the distance between the magnet and the smartphone. Magnetic fields can be easily obtained from existing applications. This makes it possible to routinely measure pinch force using an inexpensive measuring device rather than a conventional pinch measuring device. © 2024 IEEE.

关键词： Smartphones

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Assessment Software Reliability in Component-Based Software Systems: A Comprehensive Review 6

Assessment Software Reliability in Component-Based Software ...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Tagen, Ibrahim Ayman Wassif, Khaled T. Sayed, Sabah Moawad, Ramadan Department of Computer Science Faculty of Computers and Artificial Intelligence Cairo University Giza Egypt Department of Computer Science Faculty of Computers and Information Technology Future University in Egypt New Cairo Egypt

ISBN: (纸本)9798350373875

The reliability of Component-Based Software Systems (CBSS) is a critical factor in the success and longevity of software applications across various domains. This comprehensive review synthesizes methodologies and findings from thirteen key studies, offering a panoramic view of the landscape of CBSS reliability assessment. We explore a spectrum of approaches, from traditional statistical models to advanced computational intelligence techniques, evaluating their effectiveness, challenges, and contexts of applicability. Our review reveals a trend towards the integration of machine learning and fuzzy logic in reliability estimation, reflecting the complexity and dynamic nature of CBSS. Moreover, we identify a significant emphasis on predictive models that cater to early detection of reliability issues, underscoring the proactive aspect of modern software reliability engineering. The findings suggest a need for more adaptable and context-sensitive methods that can accommodate the evolving nature of software components and their interactions. This review not only charts the current state of CBSS reliability assessment but also highlights future directions for research, including the integration of real-time data and the development of more robust models for uncertainty management. Our study serves as a crucial resource for researchers and practitioners seeking to enhance the reliability of CBSS, ultimately contributing to the development of more dependable software systems. © 2024 IEEE.

关键词： Machine learning

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Improving Sentiment Analysis in Election-Based Conversations on Twitter with ElecBERT Language Model

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computers, Materials & Continua 2023年第9期76卷 3345-3361页

作者： Asif Khan Huaping Zhang Nada Boudjellal Arshad Ahmad Maqbool Khan School of Computer Science and Technology Beijing Institute of TechnologyBeijing100081China The Faculty of New Information and Communication Technologies University Abdel-Hamid Mehri Constantine 2Constantine25000Algeria Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan

Sentiment analysis plays a vital role in understanding public opinions and sentiments toward various *** recent years,the rise of social media platforms(SMPs)has provided a rich source of data for analyzing public opinions,particularly in the context of election-related ***,sentiment analysis of electionrelated tweets presents unique challenges due to the complex language used,including figurative expressions,sarcasm,and the spread of *** address these challenges,this paper proposes Election-focused Bidirectional Encoder Representations from Transformers(ElecBERT),a new model for sentiment analysis in the context of election-related ***-related tweets pose unique challenges for sentiment analysis due to their complex language,sarcasm,*** is based on the Bidirectional Encoder Representations from Transformers(BERT)language model and is fine-tuned on two datasets:Election-Related Sentiment-Annotated Tweets(ElecSent)-Multi-Languages,containing 5.31 million labeled tweets in multiple languages,and ElecSent-English,containing 4.75million labeled tweets in *** outperforms othermachine learning models such as Support Vector Machines(SVM),Na飗e Bayes(NB),and eXtreme Gradient Boosting(XGBoost),with an accuracy of 0.9905 and F1-score of 0.9816 on ElecSent-Multi-Languages,and an accuracy of 0.9930 and F1-score of 0.9899 on *** performance of differentmodels was compared using the 2020 United States(US)Presidential Election as a case *** ElecBERT-English and ElecBERT-Multi-Languages models outperformed BERTweet,with the ElecBERT-English model achieving aMean Absolute Error(MAE)of *** paper presents a valuable contribution to sentiment analysis in the context of election-related tweets,with potential applications in political analysis,social media management,and policymaking.

关键词： Sentiment analysis social media election prediction machine learning transformers

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Smart Energy Management System Using IoT for Real-Time Monitoring and Theft Detection 9

Smart Energy Management System Using IoT for Real-Time Monit...

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9th International Conference on information technology and Digital Applications, ICITDA 2024

作者： Thinakaran, Rajermani Jalari, Somasekar Neerugatti, Vikram Cholla, Ravindra Raman Rao, K Nagendra Inti International University Faculty of Data Science and Information Technology Malaysia Faculty of Engineering and technology Department of Computer Science and Engineering Bangalore India S V College of Engineering Department of Computer Science and Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798331510732

The internet of things technology has developed almost all the sectors including energy management. In traditional energy management system meters are used to recording the number of units and electricity used but they are unable to provide the real time information or updates or any theft of electricity. By focusing this problem, we have developed a system which is able to overcome all the problems in traditional electric management system. It monitors the energy usage and real time theft detection of electricity as it cost lot of money. In our system we have used a current sensor which continuously checks the electricity flow. The microcontroller measures the actual energy usage as well as expected values. It compares both the values. If a big difference found, then it could mean the theft of electricity. So, the GSM module is used to send the alert to the user hence they can take required action. The Wi-Fi module sends the data over the cloud for data analytics. Also, user can see the real time data from the cloud platform. Hence user can track the energy usage and get timely alerts so that user can be aware of energy consumption or energy theft. The system is developed by setting up hardware parts as well as integration with software. The system can be operated or monitored from remote place, as Wi-Fi module connects the system with the internet. System can detect the unusual activities like theft of electricity by analyzing the data. Using the IoT technology for energy management, it can become cost effective as well as smart solution of energy management to avoid any theft. Hence it can overcome the loss. © 2024 IEEE.

关键词： Energy utilization

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An Intrusion Detection System for Multiclass Classification Across Multiple Datasets in Industrial IoT Using Machine Learning and Neural Networks Integrated with Edge Computing 2

An Intrusion Detection System for Multiclass Classification ...

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2nd International Conference on Data, information and Computing science, CDICS 2024

作者： Zhukabayeva, Tamara Ahmad, Zulfiqar Karabayev, Nurdaulet Baumuratova, Dilaram Ali, Mushtaq Faculty of Information Technology L.N. Gumilyov Eurasian National University Astana Kazakhstan Department of Computer Science and Information Technology Hazara University Mansehra21300 Pakistan

ISBN: (纸本)9781643685762

With the rapid expansion of industrial IoT (IIoT), maintaining robust cybersecurity is essential for the smooth operation of industrial processes. Industrial environments require adaptive solutions to effectively mitigate evolving cyber threats and protect sensitive operations. This research aims to improve the cybersecurity of industrial IoT environments. The research intends to design and implement an adaptive and real-time intrusion detection system with edge computing integration that improves the reliability of the operations in industrial IoT. We incorporated machine learning approaches to classify cyber threats using XGBoost and Deep Neural Networks (DNN). A comparative analysis of results obtained from two datasets shows that the XGBoost model was slightly more accurate than the DNN model, with an accuracy of 79% for dataset D1 and approximately 99.42% for data set D2. This analysis also clearly demonstrates the usefulness of these machine learning approaches and the need to select a model depending on the requirements for detecting particular attacks. Confusion matrix analysis shows that both models have several advantages in terms of recognizing different types of cyber threats. © 2025 The Authors.

关键词： Network intrusion

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RobustFace: a novel image restoration technique for face adversarial robustness improvement

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Multimedia Tools and Applications 2025年第11期84卷 8965-8993页

作者： Sadu, Chiranjeevi Das, Pradip K. Yannam, V Ramanjaneyulu Nayyar, Anand Department of Computer Science and Engineering RGUKT Nuzvid Andhra Pradesh Nuzvid India Department of Computer Science and Engineering IIT Guwahati Assam Guwahati India School of Technology Woxsen University Telangana Hyderabad India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

Machine Learning (ML) models, particularly Deep Learning (DL), have made rapid progress and achieved significant milestones across various applications, including numerous safety-critical contexts. However, these models have recently been discovered to be susceptible to adversarial attacks, which are well-crafted input images. The primary objective of this paper is to propose a novel methodology titled "RobustFace", which is based on deep image restoration networks, that significantly improves the facial adversarial robustness of various image-classification models. Adversarial images are created using the Private Fast Gradient Sign Method (P-FGSM), StyleGAN and Fast Landmark Manipulation (FLM) methods. The adversarial images are then enhanced using deep image restoration networks to bring back them into the original space. The encoded weighted local magnitude patterns (WLMP) are extracted and provided to different types of classifiers to detect facial adversarial images from the clean images. The effectiveness of RobustFace has been demonstrated on two real-world datasets and experimental outcomes show that it significantly improves facial adversarial robustness on all evaluating classifiers. It improves the highest classification accuracy from 98.75% to 99.00% on P-FGSM attacks, from 77.94% to 85.25% on adversarial attacks generated by StyleGAN and from 65.52% to 69.50% for FLM attacks. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image reconstruction

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