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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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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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Traffic Classification For Botnet Detection Using Deep Learning 3

Traffic Classification For Botnet Detection Using Deep Learn...

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3rd International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2023

作者： Hossen, Md. Sagar Ahmad, Tohari Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781509062805

In this modern world, we are naturally dependent on technology, which is constantly evolving. At the same time, our fear of data fraud is increasing every day. The ever-increasing number of attacks on our servers indicates that people have learned to adapt to new technologies. In addition, the number of botnet attacks is not insignificant, and botnets threaten computer networks. This can impact security systems, including malware distribution, phishing, spamming, and click fraud. Due to their harmful effects, botnets must be identified as soon as possible. Due to the dynamic character of botnets, however, detection has proven difficult. In this proposed method, deep learning is used to analyze and predict botnet data, and model performance metrics such as accuracy, precision, Recall, and F1 score are compared to prior work. In the proposed procedure, patterns are also discussed by analyzing botnet data. Various scenarios and sensors separate the NCC and NCC-2 datasets. The NCC dataset is separated into 13 scenarios, while the NCC-2 dataset is separated into 3 sensors. The dataset is preprocessed using the clean method, the null value handling method, the imbalance method, and the feature selection method to extract the essential characteristics. The dataset is then trained using LSTM, GRU, and Bi-LSTM models, and 99.93% accuracy is achieved. © 2023 IEEE.

关键词： Long short-term memory

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Cardiacnet: Cardiac Arrhythmia Detection and Classification Using Unsupervised Learning Based Optimal Feature Selection with Custom CNN Model

Informatica (Slovenia)

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Informatica (Slovenia) 2024年第2期48卷 147-158页

作者： Srinivas, Kalyanapu Ch, Vijayalakshmi Borra, Subba Reddy Raju, K. Srujan Rao, Ganga Rama Koteswara Satyanarayana, K.N.V. Kumar, Pala Mahesh Department of Artificial Intelligence and Data Science Seshadri Rao Gudlavalleru Engineering College Andhra Pradesh Gudlavalleru India Department of Electronics and Communication Engineering St. Peter’s Engineering College Maisammaguda Telangana Hyderabad India Maisammaguda Telangana Hyderabad India Department of Computer Science and Engineering CMR Technical Campus Kandlakoya Medchal Road Telangana Hyderabad India Department of Computer Science and Information Technology Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India Department of Electronics and Communication Engineering Sagi Rama Krishnam Raju Engineering College Andhra Pradesh Bhimavaram India SAK Informatics Telangana Hyderabad India

The irregularity in the heartbeats caused cardiac arrhythmia, which resulted in serious health problems. This cardiac arrhythmia is monitored by electrocardiogram (ECG) signals. As a result, an accurate and timely analysis of ECG data can prevent serious health problems. However, the conventional manual prediction systems and artificial intelligence (AI) methods failed to detect cardiac arrhythmia because they failed to extract the deep salient features from the ECG dataset. So, this research work implements a model named CardiacNet, which is used to identify and classify cardiac arrhythmias from an MIT-BIH-based dataset. Initially, the pre-processing operation is performed to remove the non-linearities from the dataset. Then, unsupervised machine learning algorithm-based principal component analysis (UML-PCA) is used to extract the features of the pre-processed dataset. Further, the optimal feature selection operation is carried out using improved Harris Hawk's optimization (IHHO), which is a naturally inspired model. Moreover, a customized convolutional neural network (CCNN) model performs the classification of various cardiac arrhythmia diseases using IHHO features. The simulation results show that the proposed CardiacNet resulted in an accuracy of 97.57%, sensitivity of 98.29%, specificity of 97.97%, F-measure of 97.40%, precision of 98.66%, Matthew's correlation coefficient (MCC) of 98.17%, dice of 98.96%, and Jaccard of 97.12%. The performance comparisons show that the proposed CardiacNet resulted in improved metrics over all existing methods. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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Creating Robust and Secure Decentralized Autonomous Organizations (DAOs) for the Metaverse Using Blockchain Technology 6th

Creating Robust and Secure Decentralized Autonomous Organiza...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Goyal, S.B. Rajawat, Anand Singh Shaari, Rosnizah Bt Verma, Chaman Singh, Pradeep Kumar City University Petaling Jaya Malaysia School of Computer Sciences and Engineering Sandip University Nashik India City University Petaling Jaya46100 Malaysia Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest1053 Hungary Department of Computer Science and Engineering Central University of Jammu Jammu India

ISBN: (纸本)9789819778614

This paper presents an all-encompassing exploration on the conception and implementation of sturdy and impregnable decentralized autonomous organizations (DAOs) custom-made for the Metaverse, capitalizing on the capabilities of blockchain technology. The Metaverse, an incorporation of virtual shared space, is evolving at a rapid pace, necessitating governance structures that are decentralized, transparent, efficient, and impervious to points of failure. DAOs, governed by smart contracts and consensus mechanisms, offer a promising resolution to this exigency. To commence by scrutinizing the distinctive prerequisites of the Metaverse milieu, encompassing scalability, interoperability, and user involvement. Subsequently, we delve into the framework of DAOs, accentuating blockchain’s essential role in ensuring security, transparency, and decentralized governance. Our approach involves a stratified method to DAO design, amalgamating blockchain’s immutable ledger, smart contract functionality, and advanced cryptographic techniques to safeguard against potential hazards and susceptibilities that are specific to the Metaverse. To propose innovative mechanisms for decentralized decision-making, asset management, and dispute resolution within DAOs, ensuring consonance with the dynamic and engrossing essence of the Metaverse. Moreover, to tackle the obstacles of incorporating DAOs with existing Metaverse platforms, with a focus on interoperability between distinct blockchain protocols and virtual domains. Our proposed model encompasses a collection of exemplary practices for DAO deployment in the Metaverse, ensuring adaptability and resilience. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Smart contract

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Adaptive Strategies for Enhancing Quality of Experience in 360° Video Streaming 58

Adaptive Strategies for Enhancing Quality of Experience in 3...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Valiandi, Ioanna Pattichis, Marios S. Kyriacou, Efthyvoulos Panayides, Andreas S. Videomics Group Cyens Centre of Excellence Nicosia Cyprus University of New Mexico Department of Electrical and Computer Engineering AlbuquerqueNM87131-0001 United States Cyprus University of Technology Department of Electrical Eng. Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798350354058

The unprecedented growth of immersive virtual and mixed reality technologies is driving the increasing demand of 360° video streaming applications. Facilitating unique opportunities, but also challenges, 360° videos require substantially higher video resolutions, and hence bandwidth demands, for a better viewing experience. This study examined four tile-based Region of Interest (ROI) encoding techniques to optimize 360° video encoding and streaming. These techniques encode the user's viewport regions with higher quality compared to the background, non-ROI areas. A dataset of ten 4K omnidirectional videos at 30 frames per second was used to evaluate the performance of four distinct tile-based ROI methods using four video encoding standards, namely hevc - nvenc, x265, h264 - nvenc, and x264. Overall, the proposed tile-based ROI encoding methods achieve substantial bitrate reductions compared to the uniform full-frame encoding while maintaining adequate subjective video quality levels, demonstrating their potential for efficient and scalable 360° video streaming. The ROI-3 method, which uses a sliding window approach to encode the user's field of view based on eye gaze movement (EM) data coordinates translated to tile regions, was found to achieve the best trade-off between bitrate demands savings and perceptual quality for all investigated video compression standards. © 2024 IEEE.

关键词： Video streaming

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Maritime radar odometry inspired by visual odometry 26

Maritime radar odometry inspired by visual odometry

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26th International Conference on Information Fusion, FUSION 2023

作者： Flemmen, Henrik D. Mester, Rudolf Stahl, Annette Bryne, Torleiv H. Brekke, Edmund Førland Norwegian University of Science and Technology Department of Engineering Cybernetics Norway Norwegian University of Science and Technology Department of Informatics and Computer Science Norway

ISBN: (纸本)9798890344854

Future autonomous ships will need several redundant positioning systems to navigate reliably. Global Navigation Satellite Systems are highly accurate but they are susceptible to disruptions and intentional jamming. Maritime radars have long range and are robust against bad weather and darkness, but the use for ownship motion estimation has received relatively little attention in the research field. In this work, we present a radar odometry estimation method inspired by advances in visual odometry and simultaneous localization and mapping. The method works on raw radar data in a coastal environment and combines the Kanade-Lucas-Tomashi tracker with a factor graph back-end. We test it on data from a large ship with a maritime radar with a range of 19 km. We find that it is robust with only a small drift and no erroneous jumps in the estimate. © 2023 International Society of Information Fusion.

关键词： Motion estimation

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SinAr Mobile Application for Learning the Javanese Letters: A Case Study

SinAr Mobile Application for Learning the Javanese Letters: ...

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2023 IEEE Symposium on Wireless Technology and Applications, ISWTA 2023

作者： Husniah, Lailatul Pratama, Dimas Agung Nuryasin, Ilyas Yuhana, Umi Laili Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Universitas Muhammadiyah Malang Department of Informatics Malang Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Department of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350310955

The Javanese language is a local subject taught in almost all elementary schools on the island of Java. Most students have difficulties learning Javanese, especially the Javanese letters. They can hardly read and write the Javanese traditional scripts as they are no longer for daily reading and writing. Therefore, this research develops attractive Javanese letters learning medium to ease the students studying the Javanese letters independently and improve their understanding and learning outcomes as an Android-based mobile application. This study involved 57 students (28 students in the experimental group and 29 students in the control group), two Javanese language teachers, and two media experts. Experiments and testing were conducted for approximately two weeks, with the experimental group using the SinAr application and the control group using textbooks during the learning process. The result is that the experimental group show a higher increase in learning outcomes than the control group, so it approached the ability of the control group, which was superior in Javanese lessons based on the pretest and post-test results. The SinAr application also supports understanding of the material, student learning independence, and interesting learning, based on the results of trials on 57 students who overall received and gave good responses to the SinAr application for each aspect of learning design, operational, and visual communication with scores of 4.29 (85%). © 2023 IEEE.

关键词： Students

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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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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas Aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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