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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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A Deep Learning Approach to Mesh Segmentation

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computer Modeling in engineering & Sciences 2023年第5期135卷 1745-1763页

作者： Abubakar Sulaiman Gezawa Qicong Wang Haruna Chiroma Yunqi Lei Department of Computer Science School of InformaticsXiamen UniversityXiamen361005China Shenzen Research Institute Xiamen UniversityShenzen518000China College of Computer Science and Engineering University of Hafr Al BatinHafr Albatin39524Saudi Arabia

In the shape analysis community,decomposing a 3D shape intomeaningful parts has become a topic of interest.3D model segmentation is largely used in tasks such as shape deformation,shape partial matching,skeleton extraction,shape correspondence,shape annotation and texture *** approaches have attempted to provide better segmentation solutions;however,the majority of the previous techniques used handcrafted features,which are usually focused on a particular attribute of 3Dobjects and so are difficult to *** this paper,we propose a three-stage approach for using Multi-view recurrent neural network to automatically segment a 3D shape into visually meaningful *** first stage involves normalizing and scaling a 3D model to fit within the unit sphere and rendering the object into different *** viewpoints,on the other hand,might not have been associated,and a 3D region could correlate into totally distinct outcomes depending on the *** address this,we ran each view through(shared weights)CNN and Bolster block in order to create a probability boundary *** Bolster block simulates the area relationships between different views,which helps to improve and refine the *** stage two,the feature maps generated in the previous step are correlated using a Recurrent Neural network to obtain compatible fine detail responses for each ***,a layer that is fully connected is used to return coherent edges,which are then back project to 3D objects to produce the final *** on the Princeton Segmentation Benchmark dataset show that our proposed method is effective for mesh segmentation tasks.

关键词： Deep learning mesh segmentation 3D shape shape features

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The Multi-Way Path Over the Blockchain: The Development of the Security Algorithm RB-24-1 6

The Multi-Way Path Over the Blockchain: The Development of t...

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6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Rajaprakash, S. Bagath Basha, C. Rathish Babu, T.K.S. Karthik, S. Subapriya, V. Kavitha, S. Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science & Engineering Kommuri Pratap Reddy Institute of Technology Telagana Hyderabad India Department of Computer Science & Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India Department of Computer Science & Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai India Department of Computer Science & Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

In the current world, a lot of new technologies are becoming famous. The Block Chain is one of these technologies. A very high amount of safety is offered by the technology in question;it is extremely robust. Users are not privy to a great deal of information regarding Block Chain;however, Protecting the data that is sent in multiple ways is what its security features are for. The "SalSa and RBJ25 algorithms" were still being used by this person. We are going to talk about the new "security mechanism" we have named RB-24-1. This method is comprised of these four different techniques. The procedure begins with the "Latin Alphabet code" being assigned to T. This is the first step in the process. To proceed to the next step, use Equation (1) to perform four times the multiplication of the number by itself. "Exchanging the values of the cells" is what the third step does., and it is carried out with the help of encrypted data. Putting the "SalSa" algorithm into action within the matrix constitutes the fourth step throughout the process. Finally, the plain text is converted into the encrypted text format. This has been a long time coming. There is a higher level of security offered by the method that has been suggested in compare and contrast with the method that is being used at the moment. © 2025 IEEE.

关键词： Encryption algorithms

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"SHeMeD": An Application on Secure Computation of Medical Cloud Data Based on Homomorphic Encryption 23rd

"SHeMeD": An Application on Secure Computation of Medical Cl...

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23rd International Conference on Computational Science and Its Applications, ICCSA 2023

作者： Salaga, Hara Karanikolas, Nikos Kaklamanis, Christos Department of Computer Engineering and Informatics University of Patras Rion26504 Greece Computer Technology Institute and Press "Diophantus" Rion26504 Greece

ISBN: (纸本)9783031371196

In today’s Internet-centric world, two main challenges have arisen regarding data exploration: a) the privacy of sensitive data and b) the need for users to have an efficient way to manage their data. Cloud computing offers a cost-effective way towards direct data storage and access, which can function as a catalyst to modernize and update legacy systems. Although cloud computing provides efficient dynamic storage space, data security remains a concern. To this end, we propose a novel design and implementation of an application that enables secure storage of personal data in the cloud through a complete homomorphic encryption system. The application is named "SHeMed", which stands for Secure Homomorphic encryption on Medical cloud data. We have concentrated on the secure storage of medical data for breast cancer patients to ensure patients’ data privacy and prevent unauthorized access. With the introduction of homomorphic encryption in the core of this application, we achieve four significant milestones regarding the secure "sealing" of data privacy: a) the user does not share the private key with the cloud provider, b) the user can proceed with implementing mathematical operations on encrypted data, c) cloud can be easily scaled and provide a convenient way to store data, d) after the implementation of mathematical operations, the new processed data may be shared with other users, mainly doctors, without providing the original ones. Our design implies that our users will need to retrieve their patient’s data just once. In this paper, we present a case study of our application that utilizes homomorphic encryption to eliminate the burden of preserving data confidentiality in the cloud and enable a convenient analysis of medical data through computations on encrypted data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cost effectiveness

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Enhancing Pretrained Multilingual Machine Translation Model with Code-Switching: A Study on Chinese, English and Malay Language 24

Enhancing Pretrained Multilingual Machine Translation Model ...

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Liu, Haijing Seman, Noraini Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan China College of Computing Informatics and Mathematics University Technology MARA Shah Alam Selangor Malaysia

ISBN: (纸本)9798400717482

In the field of multilingual machine translation, many pretrained language models have achieved the inspiring results. However, the results based on pretrained models are not yet very satisfactory for low-resource languages. This paper investigates how to leverage code-switching data to fine-tune pretrained multilingual machine translation model, in order to boost the performance of few-shot low resource machine translation. By utilizing a multilingual mixed corpus, the code-switching method can enhance the cross-linguistic generalization ability of the model and improve its overall understanding of the language. In this paper, on the smaller size model of FLORES-101 benchmark, we use the code-switching data augmentation method to achieve the results of benchmark's larger model on six direction pairs of three languages, Chinese, English and Malay. This paper studied various corpus mixture mechanisms to construct the data in code-switching, and the experimental findings show that the results using the code-switching fine-tuning model improve the spBLEU score by an average of 2 to 3 points over the results without code-switching. © 2024 Copyright held by the owner/author(s).

关键词： Machine translation

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A Recurrent Neural Network for Image Deblocking Detection and Quality Enhancement 5

A Recurrent Neural Network for Image Deblocking Detection an...

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5th International Conference on Smart Systems and Inventive technology, ICSSIT 2023

作者： Venkatesan, R. Pandiaraj, A. Selvakumar, M. Bannari Amman Institute of Technology-Sathyamangalam Department of Information Technology India School of Computing Kalasalingam Academic of Research and Education-Srivilliputhur Department of Computer Science and Engineering India

ISBN: (纸本)9781665474672

With the advancement of intelligent media, it is becoming much easier to control and adjust progressed video without leaving any visual snippets of data. Since video pressure is so prevalent in modern accounts, the use of deblocking systems to conceal the video adjusting may be a viable alternative. Modified JPEG (MJPEG) is a well-known video format in which each video outline or wound around the field of an electronicvideo gatheringis squeezed independently as a JPEG image. The change may use strong sight and sound deblocking techniques to cover the video changing follows by isolating the MJPEG video into JPEG picture follows. This research study proposes a novel approach to perceive deblocking, which can consequently learn portrayals that are dependent upon a critical learning structure. Initially Recurrent Neural Network (RNN) is trained based on the available datasets to become familiar with the features of deblocking techniques.. Some parts of the images are eliminated with the RNN by applying a proper assessed sliding-window to review the entire image. In order to secure the final discriminative component, a close-by pooling technique is used to set the created image representation. © 2023 IEEE.

关键词： Image segmentation

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Application of Machine Learning Models to Predict e-Learning Engagement Using EEG Data 20

Application of Machine Learning Models to Predict e-Learning...

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20th International Conference on Web Information Systems and Technologies, WEBIST 2024

作者： Dritsas, Elias Trigka, Maria Mylonas, Phivos Industrial Systems Institute Athena Research and Innovation Center Patras Greece Department of Informatics and Computer Engineering University of West Attica Greece

ISBN: (纸本)9789897587184

The rapid evolution of e-learning platforms necessitates the development of innovative methods to enhance learner engagement. This study leverages machine learning (ML) techniques and models to predict e-learning engagement with the aid of Electroencephalography (EEG). Various ML models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Networks (NN), were applied to a dataset comprising EEG signals collected during e-learning sessions. Among these models, NN demonstrated the highest accuracy (90%), with precision and F1-score of 88%, a recall of 89%, and an Area Under the Curve (AUC) of 0.92 for predicting engagement levels. The results underscore the potential of EEG-based analysis combined with advanced ML techniques to optimize e-learning environments by accurately monitoring and responding to learner engagement. Copyright © 2024 by SCITEPRESS.

关键词： Adversarial machine learning

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Advancing Color Perception: An IoT-driven Pedagogical System for Visually Impaired Children 1

Advancing Color Perception: An IoT-driven Pedagogical System...

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1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Balage, Prasandika Jayawardene, Nuwan University of Westminster School of Computer Science and Engineering London United Kingdom Informatics Institute of Technology Department of Computing Colombo Sri Lanka

ISBN: (纸本)9798350355093

Visual impairment is characterized by the loss of vision, encompassing both complete blindness and partial vision loss. Studies reveal a notable prevalence of visual impairment among school-aged children. The significance of colors in acquiring and discerning visual information is crucial, aiding children in effectively perceiving and comprehending their surroundings. This study presents an Internet of Things (IoT) system designed for teaching colors to visually impaired children. The system achieved an accuracy rate surpassing 77.8%, and it is operated effectively regardless of surrounding lighting conditions. Given that the target users of the system are visually impaired children, this research was conducted in close alignment with Human-computer Interaction (HCI) principles. © 2024 IEEE.

关键词： Color vision

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A Systematic Literature Review of XAI-based Approaches on Brain Disease Detection using Brain MRI Images 4

A Systematic Literature Review of XAI-based Approaches on Br...

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4th International Conference on Advanced research in Computing, ICARC 2024

作者： Madapatha, Savin Fernando, Pumudu University of Westminster Department of Computer Science and Engineering London United Kingdom School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (纸本)9798350384864

Brain Magnetic Resonance Imaging (MRI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MRI analysis procedure is crucial as misdiagnoses or missed findings can greatly affect a patient's treatment and long-term prediction. With the recent advancement of Artificial Intelligence (AI) in the medical field, researchers have approached various techniques to detect brain diseases using AI. Although AI models exhibit high accuracy, they suffer from a lack of transparency and interpretability, paving the way for the development of eXplainable Artificial Intelligence (XAI) methods in brain disease diagnosis. Image segmentation, machine learning, deep learning and XAI are important for assisting the diagnostic procedure. In this paper, a comprehensive overview of various existing techniques in brain disease detection using MRI is presented, starting with image segmentation techniques, followed by classification techniques, and finally, XAI techniques. In conclusion, the paper identifies a critical need for further research on XAI integration to advance brain disease detection. © 2024 IEEE.

关键词： Magnetic resonance imaging

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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