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Bi-LSTM-Based Deep Stacked Sequence-to-Sequence Autoencoder for Forecasting Solar Irradiation and Wind Speed

computers, Materials & Continua 2023年第6期75卷 6375-6393页

作者： Neelam Mughees Mujtaba Hussain Jaffery Abdullah Mughees Anam Mughees Krzysztof Ejsmont Department of Electrical and Computer Engineering COMSATS University IslamabadLahore Campus45550Pakistan School of Engineering and Technology National Textile UniversityFaisalabad37610Pakistan Department of Electrical Engineering Information Technology UniversityLahore54000Pakistan Department of Electrical Engineering GC UniversityFaisalabad38000Pakistan Faculty of Mechanical and Industrial Engineering Warsaw University of Technology02-524WarsawPoland

Wind and solar energy are two popular forms of renewable energy used in microgrids and facilitating the transition towards net-zero carbon emissions by ***,they are exceedingly unpredictable since they rely highly on weather and atmospheric *** microgrids,smart energy management systems,such as integrated demand response programs,are permanently established on a step-ahead basis,which means that accu-rate forecasting of wind speed and solar irradiance intervals is becoming increasingly crucial to the optimal operation and planning of *** this in mind,a novel“bidirectional long short-term memory network”(Bi-LSTM)-based,deep stacked,sequence-to-sequence autoencoder(S2SAE)forecasting model for predicting short-term solar irradiation and wind speed was developed and evaluated in *** create a deep stacked S2SAE prediction model,a deep Bi-LSTM-based encoder and decoder are stacked on top of one another to reduce the dimension of the input sequence,extract its features,and then reconstruct it to produce the *** of the proposed deep stacked S2SAE forecasting model were optimized using the Bayesian optimization ***,the forecasting performance of the proposed Bi-LSTM-based deep stacked S2SAE model was compared to three other deep,and shallow stacked S2SAEs,i.e.,the LSTM-based deep stacked S2SAE model,gated recurrent unit-based deep stacked S2SAE model,and Bi-LSTM-based shallow stacked S2SAE *** these models were also optimized and modeled in *** results simulated based on actual data confirmed that the proposed model outperformed the alternatives by achieving an accuracy of up to 99.7%,which evidenced the high reliability of the proposed forecasting.

关键词： Deep stacked autoencoder sequence to sequence autoencoder bidirectional long short-term memory network wind speed forecasting solar irradiation forecasting

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Cross-Layer Distributed Attack Detection Model for the IoT

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International Journal of Ambient Computing and Intelligence 2022年第1期13卷 1-17页

作者： Ahmed, Hassan I. Nasr, Abdurrahman A. Abdel-Mageid, Salah M. Aslan, Heba K. Informatics Department Electronics Research Institute Cairo Egypt Computer Engineering Faculty of Engineering Al-Azhar University Cairo Egypt Computer Engineering Department College of Computer Science and Engineering Taibah University Saudi Arabia Center of Informatics Science Faculty of Information Technology and Computer Science Nile University Egypt

The security of IoT that is based on layered approaches has shortcomings such as the redundancy, inflexibility, and inefficiently of security solutions. There are many harmful attacks in IoT networks such as DoS and DDoS attacks, which can compromise the IoT architecture in all layers. Consequently, cross layer approach is proposed as an effective and practical security defending mechanism. Cross-layer distributed attack detection model (CLDAD) is proposed to enhance security solutions for IoT environments. CLDAD presents a general detection method of DDoS in sensing layer, network layer, and application layer. CLDAD is based on big data analytics techniques, which enable the detection process to be performed in a distributed way, so the model can detect DDoS attacks in any layer on-the-fly and the model support the scalability of the IoT environment. CLDAD is tested based on three datasets, namely artificial jamming attack dataset, BoT-IoT dataset, and BoT-IoT-based HTTP. The results showed that the proposed model is efficient in detecting attacks in the three layers of the IoT. Copyright © 2022, IGI Global.

关键词： Big data

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A New Approach to Road Incident Detection Leveraging Live Traffic Data: An Empirical Investigation 2

A New Approach to Road Incident Detection Leveraging Live Tr...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Kumar Gannina, Aswin Ram Jaffarullah, Aadhil Ahamed Reddy, Tiyyagura Mohit Subba Reddy, Sabbella Manoj Vikas, Ambati Sai Mathi, Senthilkumar Ramalingam, Venkadeshan Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India Faculty in Information Technology Department University of Technology and Applied Sciences-Shinas Oman

Accidents can have a significant impact on road safety and the efficiency of transport. With the ever-increasing number of vehicles on the road, it is crucial to quickly detect and respond to such events to decrease the impact. The current paper presents a road accident and incident detection solution utilizing traffic API monitoring and continuously analyzing real-time traffic data from various sources like GPS-enabled devices. Consequently, the proposed approach can identify abnormal patterns in the movement of vehicles, such as unexpected road closures or an accident. It numerically analyzes the traffic pattern and identifies incidents and accidents using live traffic data collected from the map service. When the approach identifies any abnormality, it can immediately let the user know, allowing them to act accordingly. It can facilitate authorities in identifying accident-prone areas and deploy safety measures. This can help law enforcement agencies to identify drivers at risk of causing accidents and implement education and enforcement programs targeted towards them. Hence, the proposed approach can aid in reducing the negative impact of traffic incidents, and the public can respond to these incidents more preparedly. © 2024 Elsevier B.V.. All rights reserved.

关键词： Accidents

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Improving Date Fruit Classification Using Cycle GAN-Generated Dataset

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computer Modeling in engineering & Sciences 2022年第4期131卷 331-348页

作者： Dina M.Ibrahim Nada M.Elshennawy Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Computers and Control Engineering Department Faculty of EngineeringTanta UniversityTanta37133Egypt

Dates are an important part of human *** are high in essential nutrients and provide a number of health *** fruits are also known to protect against a number of diseases,including cancer and heart *** fruits have several sizes,colors,tastes,and *** are a lot of challenges facing the date *** of the most significant challenges is the classification and sorting of *** there is no public dataset for date fruits,which is a major limitation in order to improve the performance of convolutional neural networks(CNN)models and avoid the overfitting *** this paper,an augmented date fruits dataset was developed using Deep Convolutional Generative Adversarial Networks(DCGAN)and CycleGAN approach to augment our collected date fruit *** augmentation is required to address the issue of a restricted number of images in our datasets,as well as to establish a balanced *** are three types of dates in our proposed dataset:Sukkari,Ajwa,and *** dataset augmentation,we train our created dataset using ResNet152V2 and CNN models to assess the classification process for our three categories in the *** train these two models,we start with the original ***,the models were trained using the DCGAN-generated dataset,followed by the CycleGAN-generated *** resulting results demonstrated that when using the ResNet152V2model,the CycleGAN-generated dataset had the highest classification performance with 96.8%accuracy,followed by the CNN model with 94.3%accuracy.

关键词： Dates fruits data augmentation DCGAN CycleGAN,deep learning convolution neural networks

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Sentiment Analysis of ChatGPT Healthcare Discourse: Insights from Twitter Data 15

Sentiment Analysis of ChatGPT Healthcare Discourse: Insights...

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15th International Conference on Software, Knowledge, information Management and Applications, SKIMA 2023

作者： Kumar, Roshan Ayyasamy, Ramesh Kumar Sangodiah, Anbuselvan Krishnan, Kesavan Jebna, Abdulkarim Kanaan Theam, Lim Jit Universiti Tunku Abdul Rahman Faculty of Information and Comunication Technology Kampar Malaysia Quest International University Faculty of Computing and Engineering Ipoh Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia

ISBN: (纸本)9798350316551

This paper explores the application of Chat Generative Pretrained Transformer (ChatGPT) in the healthcare domain, introducing a sentiment analysis model to evaluate ChatGPT-related tweets in healthcare contexts. The study aims to uncover predominant sentiments, thematic content, and diverse perspectives concerning ChatGPT's integration into healthcare, utilizing an extensive dataset from Twitter comprising 10,330 healthcare-related tweets. Leveraging advanced Natural Language Processing (NLP) techniques, we systematically categorized topics and emotional content within these tweets. Additionally, we conducted a comprehensive analysis of frequently occurring words in tweets expressing positive and negative sentiments. The findings reveal that the majority of healthcare-related ChatGPT tweets express either positive or negative sentiments, with a minor proportion conveying neutral viewpoints. Furthermore, to enhance our comprehension of sentiment dynamics in healthcare discussions involving ChatGPT, we applied four machine learning classifiers Support Vector Machine, K-Nearest Neighbors, Naive Bayes and Random Forest. Remarkably, the SVM classifier demonstrated the highest accuracy at 85.6%, affirming its efficacy in healthcare sentiment analysis. In summary, this research sheds light on prevailing sentiments and perspectives regarding ChatGPT in the healthcare sector, highlighting its predominantly positive and neutral reception on platforms like Twitter. Additionally, the success of SVM as a sentiment analysis tool underscores its potential for discerning sentiments in healthcare-related ChatGPT discussions, contributing to ongoing debates on AI integration in healthcare and guiding future endeavors in this evolving field. © 2023 IEEE.

关键词： Sentiment analysis

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Classification of Sleep-Related Disorders Using Machine Learning Techniques 25

Classification of Sleep-Related Disorders Using Machine Lear...

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25th International Arab Conference on information technology, ACIT 2024

作者： Nasir, Ola Alazaidah, Raed Al-Safarini, Maram Y. Alamleh, Amneh Zaid, Ali Abu Mansur, Hasan Elrashidi, Ali Alzoubi, Haneen Bani-Bakr, Alaa Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Information Technology Department of Cyber Security Zarqa Jordan American University of Madaba Faculty of Business Department of Accounting Madaba Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Private University Faculty of It Applied Science Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Several people suffer from irregular sleep patterns, a problem that negatively affects their daily lives. It presents a multitude of challenges, including daytime distress, disturbances in sleep-wake patterns, feelings of unease, and a variety of other complications. Diabetes, obesity, and psychological disorders are among the diseases that may be responsible for this sleep disorder. Therefore, the primary aim of this research is to employ the robust capabilities of machine learning in forecasting sleep disorders. This research classifies three diseases: diabetes, obesity, and psychological disorders, using thirty-two different classifiers belonging to five learning strategies. The research compares and evaluates their predictive performance on four datasets. The comparative analysis includes four evaluation metrics: Accuracy, Precision, ROC Area, and MCC. The research results indicate that the best classifier is SGD, followed by LMT, while NBM is the weakest classifier. In addition, the research results revealed that the best strategy is Functions, followed by Rules, with Bayes being the least effective strategy. While conducting the experiment and analyzing the data, it is observed that the best feature selection method that yielded the most favorable outcome is the CorrelationAttributeEval. Furthermore, it has been found that the classification algorithm known as SGD achieved the highest Accuracy and Precision in categorizing the data. This indicates that the combination of CorrelationAttributeEval as the feature selection approach and SGD as the classification algorithm proved to be the most successful and efficient strategy for this research © 2024 IEEE.

关键词： Sleep research

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An Improved Reverse Distillation Model for Unsupervised Anomaly Detection 17

An Improved Reverse Distillation Model for Unsupervised Anom...

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17th International Conference on Ubiquitous information Management and Communication, IMCOM 2023

作者： Van Duc, Nguyen Bach, Hoang Huu Trang, Le Hong Faculty of Computer Science and Engineering Ho Chi Minh City Viet Nam Faculty of Information Technology Ha Noi Hanoi Viet Nam

ISBN: (纸本)9781665453486

Using knowledge distillation for unsupervised anomaly detection problems is more efficient. Recently, a reverse distillation (RD) model has been presented a novel teacher-student (T-S) model for the problem [7]. In the model, the student network uses the one-class embedding from the teacher model as input with the goal of restoring the teacher's rep-resentations. The knowledge distillation starts with high-level abstract presentations and moves down to low-level aspects using a model called one-class bottleneck embedding (OCBE). Although its performance is expressive, it still leverages the power of transforming input images before applying this architecture. Instead of only using raw images, in this paper, we transform them using augmentation techniques. The teacher will encode raw and transformed inputs to get raw representation (encoded from raw inputs) and transformed representation (encoded from transformed inputs). The student must restore the transformed representation from the bottleneck to the raw representation. Testing results obtained on benchmarks for AD and one-class novelty detection showed that our proposed model outperforms the SOTA ones, proving the utility and applicability of the suggested strategy. © 2023 IEEE.

关键词： Deep neural networks

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Intelligent-Based Extraction of Business Capabilities from Company Descriptions 7

Intelligent-Based Extraction of Business Capabilities from C...

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7th International Conference on information technology, information Systems and Electrical engineering, ICITISEE 2023

作者： Fahmi, Faisal Hamid, Siti Hafizah Ab Yuadi, Imam Airlangga University Faculty of Political and Social Science Department of Library and Information Science Indonesia Universiti Malaya Faculty of Computer Science & Information Technology Department of Software Engineering Malaysia

ISBN: (纸本)9798350382266

In the era of Big Data, information can be generated, extracted, and utilized in diverse ways. In business, information about business capabilities can be a crucial aspect in understanding the strengths and competencies of organizations. This information can empower stakeholders to make informed decisions regarding partnerships, acquisitions, and market positioning. Additionally, by comparing capabilities across companies and industries, organizations can identify their competitive advantages, industry trends, potential disruptions, and effective resource allocations, leading to improved strategies and better market opportunities. Traditional methods for extracting business capabilities primarily rely on keyword matching or rule-based techniques, which often fail to capture the complex relationships between words in a sentence. This paper presents a comprehensive study on extracting business capabilities using dependency parsing techniques in natural language processing (NLP) to overcome limitations of the traditional methods and improve the accuracy of business capability extraction. Dependency parsing utilizes pre-trained language models to analyze the grammatical structure of sentences, identifying the dependencies between words and capturing their relationships. The feasibility and effectiveness of the proposed approach is demonstrated through experiments conducted on more than 40K real-world company descriptions crawled from Wikipedia. © 2023 IEEE.

关键词： Competition

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Attacks Detection in 6G Wireless Networks using Machine Learning 9

Attacks Detection in 6G Wireless Networks using Machine Lear...

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9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Saeed, Mamoon M. Saeed, Rashid A. Gaid, Abdulguddoos S. A. Mokhtar, Rania A. Khalifa, Othman O. Ahmed, Zeinab E. Faculty of Engineering Department of Communications and Electronics Engineering Yemen Taif University College of Computers and Information Technology Department of Computer Engineering P.O. Box 11099 Taif21944 Saudi Arabia Taiz University Faculty of Engineering & Information Technology Department of Communication & Computer Engineering Taiz Yemen International Islamic University Malaysia Department of Electrical and Computer Engineering Malaysia Libyan Center for Engineering Research and Information Technology Bani Waleed Libya International Islamic University Malaysia Electrical and Computer Engineering Dept Malaysia University of Gezira Computer Engineering Dept Sudan

ISBN: (纸本)9798350325218

Unlike the fifth generation (5G), which is well recognized for network cloudification with micro-service-oriented design, the sixth generation (6G) of networks is directly tied to intelligent network orchestration and management. The Attacks Detection in 6G (AD6Gs) wireless networks created by this research uses a Machine Learning (ML) algorithm. The pre-processing stage of the ML-AD6Gs process is the initial step. The second stage involves the feature selection approach. Correlation Feature Selection algorithm (CFS) is used to implement the suggested hybrid strategy. It selects the best subset feature and reduces dimensionality for each independent analyses of the dataset CICDDOS2019. The voting average method is used as an aggregation step, and two classifiers - Random Forest (RF) and Support Vector Machine (SVM) - are modified to be used as ML Algorithms. The proposed method shown an outperformed the existing classification method. The accuracy was 99.9%% for CICDDOS2019 dataset with a false alarm rate of 0.00102 © 2023 IEEE.

关键词： Support vector machines

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A heuristic ant colony optimization approach for allocating and packing multi-module satellite payloads 4

A heuristic ant colony optimization approach for allocating ...

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4th International Conference on computers and Artificial Intelligence technology, CAIT 2023

作者： Wang, Shiya Zhang, Tingting Zeng, Peng Nie, Jun Guangdong University of Science and Technology Faculty of Computer Guangdong China Xiantang University School of Information and Engineering Guangdong China

ISBN: (纸本)9798350326710

It is effective to pack multi-module satellite payloads for existing heuristic and evolutionary approaches. But for them, the multi-module payload allocation optimization is not considered before packing. In addition, an evolutionary approach based on a random initial scheme needs to spend more time on interference calculation. For this reason, a knowledge-based heuristic ant colony optimization approach is proposed for allocating and packing multi-module satellite payloads (MSAPHAA). Firstly, related knowledge is obtained from the principle of dynamics and the definition of moment of inertia. Then by combining it with heuristic and ant colony optimization, it will be realized to both the multi-module payload allocation optimization and packing optimization without interference computation and in the process of iteration payload migration can be accomplished by analyzing the packing position of payloads. Introducing the knowledge-based swap intelligent optimization and avoiding interference computation improves the performance of the proposed approach. Numerical experiments show that all the solution accuracy, computation efficiency, and stability of the proposed MSAPHAA approach are higher than those of existing approaches. © 2023 IEEE.

关键词： Satellites

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