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Deep Learning Enabled Predictive Model for P2P Energy Trading in TEM

computers, Materials & Continua 2022年第4期71卷 1473-1487页

作者： Pudi Sekhar T.J.Benedict Jose Velmurugan Subbiah Parvathy E.Laxmi Lydia Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Computer Applications Government Arts&Science CollegeKanyakumari629401India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Computer Science and Engineering Vignan’s Institute of Information Technology(Autonomous)Visakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.

关键词： Energy trading distributed systems power generation load forecasting deep learning peer-to-peer

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Multiclass Cucumber Leaf Diseases Recognition Using Best Feature Selection

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computers, Materials & Continua 2022年第2期70卷 3281-3294页

作者： Nazar Hussain Muhammad Attique Khan Usman Tariq Seifedine Kadry Muhammad Asfand E.Yar Almetwally M.Mostafa Abeer Ali Alnuaim Shafiq Ahmad Department of Computer Science HITEC University TaxilaTaxilaPakistan College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-KhrajSaudi Arabia Department of Applied Data Science Noroff University CollegeNorway Department of Computer Science Bahria UniversityIslamabadPakistan Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543KSASaudi Arabia Department of natural and engineering sciences College of Applied Studies and Community ServiceKing Saud UniversityRiyadh11421Saudi Arabia Faculty of Engineering Industrial Engineering DepartmentKing Saud UniversityRiyadh11421Saudi Arabia

Agriculture is an important research area in the field of visual recognition by *** diseases affect the quality and yields of ***-stage identification of crop disease decreases financial losses and positively impacts crop *** manual identification of crop diseases,which aremostly visible on leaves,is a very time-consuming and costly *** this work,we propose a new framework for the recognition of cucumber leaf *** proposed framework is based on deep learning and involves the fusion and selection of the best *** the feature extraction phase,VGG(Visual Geometry Group)and Inception V3 deep learning models are considered and *** fine-tuned models are trained using deep transfer *** are extracted in the later step and fused using a parallel maximum fusion *** the later step,best features are selected usingWhale Optimization *** best-selected features are classified using supervised learning algorithms for the final classification *** experimental process was conducted on a privately collected dataset that consists of five types of cucumber disease and achieved accuracy of 96.5%.A comparison with recent techniques shows the significance of the proposed method.

关键词： Cucumber diseases database preparation deep learning parallel fusion features selection

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Assessing Risk Factors with Generative Adversarial Networks for Cardiac Arrest Detection

Assessing Risk Factors with Generative Adversarial Networks ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Gaur, Sunil Kumar Preethi, D. Abrol, Monika Teetthanker Mahaveer University College of Engineering Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Applied Science Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

This paper seeks to evaluate the performance of generative adverse networks (GANs) in opposition to conventional strategies for predicting cardiac arrests. Via the usage of GANs, the paper examines the capability to assess hazard factor accuracy and generate new synthetic facts regarding the threat of cardiac arrest. The paper explores methods that GANs can be applied to generate new representations of respective cardiac arrest danger factors. Moreover, it evaluates the superiority of the GANs-based model in evaluation to traditional gadget learning techniques constructed on existing data. ultimately, the look tries to assess the accuracy of GANs in cardiac arrest prediction and its capability to assess hazard elements. This paper investigates the capability of using Generative antagonistic Networks (GANs) to assess chance factors for the early detection of cardiac arrest. First, a deep generative community consisting of two convolutional vehicle Encoder (CAE) sub-networks is employed to examine discriminative representations from clinical databases. Then, a supervised discriminative network is used to analyze the encodings and classify hazard factors that hint at the opportunity of cardiac arrest. The paper also demonstrates strategies for optimizing the GAN's training technique to further improve the device's accuracy. Subsequently, experimental consequences at the MIMIC scientific database display the effectiveness of the proposed GAN architecture in ascertaining cardiac arrest hazard elements.. © 2024 IEEE.

关键词： antagonistic capability hazard prediction strategies

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An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

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computers, Materials & Continua 2022年第1期70卷 1721-1747页

作者： Sultan Noman Qasem Mohammed Al-Sarem Faisal Saeed Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Computer Science Department Faculty of Applied ScienceTaiz UniversityTaiz6803Yemen College of Computer Science and Engineering Taibah UniversityMedinaSaudi Arabia Information System Department Saba’a Region UniversityMareebYemen

Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,especially during emergency situations and health *** huge amounts of content being posted to social media every second during these situations,it becomes very difficult to detect fake news(rumors)that poses threats to the stability and sustainability of the healthcare sector.A rumor is defined as a statement for which truthfulness has not been *** COVID 19,people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social *** methods have been applied for detecting rumors and tracking their sources for COVID 19-related ***,very few studies have been conducted for this purpose for the Arabic language,which has unique ***,this paper proposes a comprehensive approach which includes two phases:detection and *** the detection phase of the study carried out,several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset.A new detection model was used which combined two models:The Genetic Algorithm Based Support Vector Machine(that works on users’and tweets’features)and the stacking ensemble method(that works on tweets’texts).In the tracking phase,several similarity-based techniques were used to obtain the top 1%of similar tweets to a target tweet/post,which helped to find the source of the *** experiments showed interesting results in terms of accuracy,precision,recall and F1-Score for rumor detection(the accuracy reached 92.63%),and showed interesting findings in the tracking phase,in terms of ROUGE L precision,recall and F1-Score for similarity techniques.

关键词： Rumor detection rumor tracking similarity techniques COVID-19 social media analytics

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Performing Hybrid Predictions of Time Series in Head Based Aggregation Clusters

Performing Hybrid Predictions of Time Series in Head Based A...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Vishnoi, Rahul Bhuvana, J. Abrol, Monika College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Applied Science Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

This paper affords a hybrid forecasting scheme for time series prediction in head-primarily based aggregation clusters. The proposed version combines multiple individual predictors in a manner that permits each to capture heterogeneous characteristics of the time series and think of each of the temporal and spatial dimensions within the enter statistics. An aggregation step is also included to construct cluster distributions to enhance prediction accuracy. The model's performance is evaluated on actual datasets with recognition of each temporal and spatial dimension. Results display the effectiveness of the proposed technique in predicting destiny values of time collection with accuracy higher than individual predictions. Further, the model also can cope with minor adjustments within the shape of facts, allowing for excessive flexibility within the analysis and forecasting procedure. It explores the ability of hybrid predictions of time collection in head-based total aggregation clusters. It provides a complete time collection evaluation, forecasting the usage of an aggregate of records mining and device-getting-to-know techniques. The paper introduces the vital standards and techniques for an adequate time series forecasting version. A hybrid prediction model consisting of the Autoregressive included shifting standard (ARIMA) model and the Weighted Neighbourhood common (WNA) model is then explored. The efficacy of the version is statistically tested on an artificial dataset with constant natural and seasonal time series additives and on one actual-global dataset with a vast range of correlated time collection. Compared to classic forecasting fashions, the effects demonstrate that a hybrid prediction version produces drastically higher accuracy and offers an extra sensible representation of the device's behavior. The paper evaluates hybrid prediction fashions to alternative strategies and a few viable future development directions. © 2024 IEEE.

关键词： aggregation dimensions forecasting permits series

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Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier

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computers, Materials & Continua 2022年第3期70卷 5949-5965页

作者： K.S.Bhuvaneshwari K.Venkatachalam S.Hubalovsky P.Trojovsky P.Prabu Department of Computer Sceince and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Mathematics Faculty of ScienceUniversity of Hradec KrálovéHradec Králové50003Czech Republic Department of Computer Sceince CHRIST(Deemed to be University)Bangalore560074India

With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and *** on the characteristics of these intruders,many researchers attempted to aim to detect the intrusion with the help of automating ***,the large volume of data is generated and transferred through network,the security and performance are remained an ***(Intrusion Detection System)was developed to detect and prevent the intruders and secure the network *** performance and loss are still an issue because of the features space grows while detecting the *** this paper,deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and *** proposed system includes three phases such as preprocessing,feature selection and *** the first phase,KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization *** second phase,feature selection is performed by using Information Gain based Dragonfly Optimizer(IGDFO).Finally,Deep clustering based Convolutional Neural Network(CCNN)classifier optimized with Particle Swarm Optimization(PSO)identifies intrusion attacks *** clustering loss and network loss can be reduced with the optimization *** evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation *** experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy,precision,recall,f-measure and false detection rate.

关键词： Intrusion detection system binning normalization deep clustering convolutional neural network information gain dragonfly optimizer

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Don’t Train, Just Prompt: Towards a Prompt engineering Approach for a More Generative Container Orchestration Management 14

Don’t Train, Just Prompt: Towards a Prompt Engineering Appr...

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14th International Conference on Cloud Computing and Services science, CLOSER 2024

作者： Kratzke, Nane Drews, André Department of Electrical Engineering and Computer Science Lübeck University of Applied Sciences Germany Expert Group Artificial Intelligence in Applications Lübeck University of Applied Sciences Germany

ISBN: (纸本)9789897587016

Background: The intricate architecture of container orchestration systems like Kubernetes relies on the critical role of declarative manifest files that serve as the blueprints for orchestration. However, managing these manifest files often presents complex challenges requiring significant DevOps expertise. Methodology: This position paper explores using Large Language Models (LLMs) to automate the generation of Kubernetes manifest files through natural language specifications and prompt engineering, aiming to simplify Kubernetes management. The study evaluates these LLMs using Zero-Shot, Few-Shot, and Prompt-Chaining techniques against DevOps requirements and the ability to support fully automated deployment pipelines. Results show that LLMs can produce Kubernetes manifests with varying degrees of manual intervention, with GPT-4 and GPT-3.5 showing potential for fully automated deployments. Interestingly, smaller models sometimes outperform larger ones, questioning the assumption that bigger is always better. Conclusion: The study emphasizes that prompt engineering is critical to optimizing LLM outputs for Kubernetes. It suggests further research into prompt strategies and LLM comparisons and highlights a promising research direction for integrating LLMs into automatic deployment pipelines. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Automation

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Some Results on Ricci-Yamabe Soliton and Its Cosmological Models

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IAENG International Journal of applied mathematics 2025年第4期55卷 979-983页

作者： Pandey, Pankaj Yadav, Santosh Kumar Sharma, Kamakshi Chaturvedi, Braj Bhushan Department of Mathematics School of Chemical Engineering and Physical Sciences Lovely Professional University Punjab Phagwara144411 India Department of Mathematics School of Computer Science & Artificial Intelligence SR University Telangana Warangal506371 India Department of Pure & Applied Mathematics Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur495009 India

This article aims to investigate the characteristics of Ricci-Yamabe Soliton (briefly: (RYS)n). We study the cosmological models on (RYS)4 under Lorentzian para Sasakian (LPS)4 spacetime. Parallel Ricci tensor, Poisson structure and gradient property also reveal on (RYS)n. Example of (RYS)4 constructed to verify the results. © (2025), (International Association of Engineers). All rights reserved.

关键词： Solitons

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Pc Automation Using Hand Gestures

Pc Automation Using Hand Gestures

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Purushotham, P. Bennet, Ben Sujin Vijayakarthik, P. Kiran, Ajmeera Ramakrishna Pravallika, M. Shivani, Y. Chetan, Ch. MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India University of Technology and Applied Sciences Engineering Department Oman R.L.Jalappa Institute of Technology Dodabalapur Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350348217

The human computer interaction platform can be implemented in a variety of different ways because to the fact that webcams and other equipment, such as sensors, are both relatively inexpensive and readily available on the market. The use of gestures as a mode of communication between humans and machines is widely regarded as the most effective approach. The employment of hand gestures as a means of communication between humans and machines or computers is particularly helpful in achieving higher levels of information conveyance. The use of hand gestures as a form of nonverbal communication is something that can be put to use in a variety of contexts. Applications that make use of hand gestures have acquired a variety of alternative methodologies, including those that are backed by sensor technology and computer vision, according to research and survey reports. Research is expanding into new territory, particularly in the areas of pattern recognition and gesture recognition. Gestures of the hand can add a new layer of complexity to the operation of contactless computers. As a result, in this project, we produce software that shows a prototype of a system that is able to automatically recognize gestures and executes pc commands in response to certain motions. This software is shown as part of this project. Python is the language that we are utilizing to construct our solution, and we are taking advantage of integrated modules such as cv2 (OpenCV, or open source computer vision, is a collection of programming functions that is primarily geared toward real-time computer vision.) & Mediapipe (detects hand and fingers data points.) © 2023 IEEE.

关键词： Human computer interaction

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Machine Learning Assisted Interactive Multi-objectives Optimization Framework: A Proposed Formulation and Method for Overtime Planning in Software Development Projects 1

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22nd International Conference on Artificial Intelligence and Soft Computing, ICAISC 2023

作者： Mojeed, Hammed A. Szlapczynski, Rafal Department of Applied Computer Science Institute of Ocean Engineering and Ship Technology Gdansk University of Technology Gdansk Poland

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

Software development project requires proper planning to mitigate risk and uncertainty. Overtime planning within software project management has been receiving attention recently from search-based software engineering researchers. Multi-objective evolutionary algorithms are used to build automated tools that could effectively help Project Managers (PM) plan overtime on project schedules. Existing models however lack applicability by the PMs due to their disregard for expert knowledge in planning overtime. This study proposes a new interactive problem formulation for software overtime planning and presents a framework for building a machine learning-based interactive multi-objective optimization algorithm for overtime planning in software development projects. The framework is designed to train a priori a machine learning model to mimic the PM’s subjective judgment of overtime plans within the project schedule. The machine learning model is integrated with a memetic multi-objective optimization algorithm via an interactive module. Also, the memetic algorithm incorporates a preference-based w-dominance method for selecting non-dominated solutions. The proposed framework will be developed to assist software project managers to better plan overtime in order to prevent the expected risk of software development overrun. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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