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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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Digital Forensics and the Law: A computer engineering Perspective 15

Digital Forensics and the Law: A Computer Engineering Perspe...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Agarwal, Gaurav Bhatt, Rahul Pathak, Paresh Kumar, Ashwani Kumar, Anup Pandey, Sakshi Invertis University Department of Computer Science & Engineering Uttar Pradesh Bareilly243123 India Dev Bhoomi Uttarakhand University School of Engineering and Computer Uttarakhand 248007 India IIMT University School of COMPUTER Science and Applications Meerut India K. R. Mangalam University School of Engineering & Technology Department of Computer Science & Engineering Haryana Gurugram122103 India ARKA JAIN University Department of Engineering & IT Jharkhand Jamshedpur India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

Digital Forensics and the law is an interdisciplinary subject that studies the legal implications of digital evidence in the context of legal guidelines, rules governing investigations, and data-driven tactics for preventing, detecting, and remediating crimes. It explores how the use of virtual forensics techniques impacts the legal manner in crook and civil instances and the expert obligation of those chargeable for using these strategies. The scope of virtual Forensics and the regulation additionally covers the felony implications of the generation-aided discovery system utilized in pursuing investigations into online activity and the capacity to find evidence for which there's no bodily trace or era platform to get admission. Ultimately, the implications of municipal, state, and federal laws governing digital Forensics are also examined. © 2024 IEEE.

关键词： Impacts Municipal Remediating Techniques Utilized

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Mental Health Tracker Using Machine Learning Approaches 4

Mental Health Tracker Using Machine Learning Approaches

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4th IEEE Global Conference for Advancement in Technology, GCAT 2023

作者： Thotad, Puneeth N. Kallur, Shanta Mundaragi, Laxmi Kadam, Swati H. Dept. of Master of Computer Applications K.L.E. Institute of Technology Hubballi580 027 India Dept. of Computer Science and Engineering K.L.E. Institute of Technology Hubballi580 027 India

ISBN: (纸本)9798350305258

Mental illness is a common condition in the 21st century. Despite the significant number of people who experience mental illness, many continue to experience stress without comprehending its damaging effects on their brain. Python and machine learning tools are adopted to create the mental health tracker system. According to the experimentation, the work focuses on developing a psychological well-being tracker. The prediction uses various models such as logistic regression, random forest, decision tree, and k-nearest neighbor. In the proposed system, the patient must answer the questions to predict their mental illness condition. Based on the responses given by the patient, the system will predict whether they need treatment or not. The system will attempt to gain an understanding of a user's mental state, determine whether they are suffering, and then suggest steps they can take to improve their mental health situation. Mental health prediction might benefit psychological departments looking to enhance their counseling and diagnosis abilities. The model was developed by collecting the question-answer set as options related to mental health by consulting specialized doctors in the hospital. The model is trained using different ML algorithms, finding their accuracy and predicting the results. The analysis was made by considering the raw data and normalized data. Models performance was improved with normalization with the following accuracies. 71.4%, 73.5%, 80.6%, and 81.2% accuracies are obtained for Logistic regression, K-Nearest Neighbor, Decision Tree, and Random Forest, respectively. © 2023 IEEE.

关键词： Decision Tree Digital Mental Health K-Nearest Neighbor Logistic Regression Machine Learning Random Forests

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A Deep Review to Find the Most Accurate Method among KNN,RF and MLP for Predicting Alzheimer Disease 4

A Deep Review to Find the Most Accurate Method among KNN,RF ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Subathra, K. Malakar, Antara Suhasini, S. Prakash, Sneha Abed, Sarah Jassim Abdtawfeeq, Tareq Hafdhi Sri Sairam Engineering College Department of Mathematics Chennai44 India The Assam Royal Global University Department of Computer Science & Engineering Guwahati India Cvr College of Engineering Hyderabad India Adi Shankara Institute of Engineering & Technology Department of Computer Applications Kalady India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

Debilitating neurodegenerative conditions like Alzheimer's disease (AD) are linked to great pain and eventually death. For the purpose of successful treatment planning and individualized therapy, it is imperative that Alzheimer's disease (AD) and its precursor phases, such as moderate cognitive impairment (MCI), be identified as soon as possible. Single nucleotide polymorphisms, or SNPs, and neuroimaging data, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), have been found to work better together to improve the ability to distinguish between AD/MCI and normal control (NC) participants. But missing data from multimodal sources will always exist, and model learning is complicated by the variety of phenotypic and genotypic biomarkers. Furthermore, most current research uses multistage techniques to forecast illness development and focuses only on classifying brain diseases, ignoring the regression problem. Using the most samples possible inside a k-nearest neighbour, random forest and multi layer perceptron (KNN-RF-MLP) architecture, we provide a unique framework to concurrently solve classification and clinical score regression problems by combining multimodal neuroimaging and genetic data. To impute missing features in partial data, we first apply linear interpolation in our technique. We next employ KNN to extract neuroimaging features from SNPs, MRI, and PET data in order to reduce the variation in phenotypic and genotype data. © 2024 IEEE.

关键词： Positron emission tomography

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A Novel Architecture for Developing IoT Solutions Applied to Healthcare 2

A Novel Architecture for Developing IoT Solutions Applied to...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Levina Kaliyamoorthi, P. Dattu, Vinjamuri S.N.Ch. Dineshnath, G. Jaiganesh, S. Dobhal, Dinesh Chandra Robin, C. R. Rene Kns Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Madanapalle Institute of Technology & Science Department of Computer Science and Engineering Andhra Pradesh Madanapalle517325 India Aditya Engineering College Department of Mechanical Engineering Andhra Pradesh Surampalem533437 India Kl University Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India Department of Master of Computer Applications Tamil Nadu Dindigul624622 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Sri Sairam Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600044 India

ISBN: (纸本)9781665456302

The objective of this research study is to reduce the complexity and increase the flexibility to create and maintain computational solutions applied to health, where the scenarios are quite complex and dynamic. The main contributions of HealthDash are: (1) Provide dynamism in the process of creating IoT applications, both in the fog layer and in the cloud layer, through unification action of technologies adopted at both levels;(2) provide means to reduce the volume of information transmitted between the fog layer and the cloud layer, enabling the analysis and filtering of data closer to the edge of the network. This article proposes a solution for the development of IoT applications for health called HealthDash, based on the data flow-oriented programming paradigm, allowing to indicate in which layer of the architecture (fog or cloud) the data flows, which process data from the monitored patients, will be executed. © 2023 IEEE.

关键词： Internet of things

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Comparative Analysis of Decorrelation and Contrast Stretching for Multispectral Color Image Enhancement 10

Comparative Analysis of Decorrelation and Contrast Stretchin...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Ganesan, P. Leo Joseph, L.M.I. Sajiv, G. Chandrasekaran, B. Manigandan, E. Janakiraman, V. Vidya Jyothi Institute of Technology Dept. of Electronics and Communication Engineering Hyderabad India Sr University Dept. of ECE Telangana Warangal India Simats Saveetha School of Engineering Tamilnadu Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350353068

The proposed work explains the comparative detailed study of multispectral image enhancement by means of contrast and decorrelation stretching. It is challenging to extract all necessary information from multispectral image which is a huge collection of information. Multispectral images are taken using different filters (sensors) that can recognize different spectral bands, giving us important details about the scene we're looking at. Most of the data is intensified inside a narrow portion of the accessible dynamic range. So the true color composite image looks monotonous. In addition, in multispectral images the visible bands are highly correlated with each other. This is the reason they looks so dull. So it is obligatory to decorrelate the bands and enhance the contrast of the input data. In this study, the efficiency of the contrast and decorrelation stretching transformation on test images is compared. The experimental outcomes undoubtedly demonstrate that decorrelation stretching outperformed the contrast stretching in this context. © 2024 IEEE.

关键词： Image enhancement

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FedMUP: Federated learning driven malicious user prediction model for secure data distribution in cloud environments

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Applied Soft Computing 2024年 157卷

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Kumar, Jatinder Singh, Ashutosh Kumar Department of Computer Science & Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan Department of Computer Science & Engineering University of Aizu Aizuwakamatsu Fukushima Japan Department of Computer Applications SRM Institute of Science and Technology NCR Campus-Ghaziabad201204 India Department of Computer Applications National Institute of Technology Kurukshetra136119 India Department of Computer Science & Engineering Indian Institute of Information Technology Bhopal462003 India Department of Computer Science The University of Economics and Human Sciences Warsaw01043 Poland

Cloud computing is flourishing at a rapid pace. Significant consequences related to data security appear as a malicious user may get unauthorized access to sensitive data which may be misused, further. This raises an alarm-ringing situation to tackle the crucial issue related to data security and proactive malicious user prediction. This article proposes a Federated learning driven Malicious User Prediction Model for Secure Data Distribution in Cloud Environments (FedMUP). This approach firstly analyzes user behavior to acquire multiple security risk parameters. Afterward, it employs the federated learning-driven malicious user prediction approach to reveal doubtful users, proactively. FedMUP trains the local model on their local dataset and transfers computed values rather than actual raw data to obtain an updated global model based on averaging various local versions. This updated model is shared repeatedly at regular intervals with the user for retraining to acquire a better, and more efficient model capable of predicting malicious users more precisely. Extensive experimental work and comparison of the proposed model with state-of-the-art approaches demonstrate the efficiency of the proposed work. Significant improvement is observed in the key performance indicators such as malicious user prediction accuracy, precision, recall, and f1-score up to 14.32%, 17.88%, 14.32%, and 18.35%, respectively. © 2024 Elsevier B.V.

关键词： Forecasting

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Analyzing Risk Elements in Cardiovascular Diseases Prediction using Neural Networks Algorithm 10

Analyzing Risk Elements in Cardiovascular Diseases Predictio...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Yadav, Dhyan Chandra Pal, Saurabh Yadav, Rakesh Kumar Pant, Hemlata J S University Department of Computer Science Shikohabad India Vbsp University Department of Computer Applications Jaunpur India Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow India Ambalika Institute of Management and Technology Department of Information Technology Lucknow India

ISBN: (纸本)9789380544472

Globally, cardiovascular disease is the leading cause of death. In CVDs, the heart is a vital organ for supplying blood to other body organs. Complex datasets with heart problems are predicted by machine learning methods. Analysts and experts in diagnosis can easily locate reports of data on CVDs at the primary level. In the study, we investigate the performance of neural networks when used on datasets for CVD prediction. The performance of the method is assessed based on the characteristics of the dataset using Random Forest and Extra Tree feature selection strategies with the following basic analyses: accuracy score, precision, recall, f1-score, and roc. Our study highlights the critical properties of CVD datasets in order to determine which accuracy measures work best for predicting and diagnosing CVD. Our main findings were predictive Neural Network performance with an accuracy metric based on Random Forest and Extra Tree, highlighting key features for CVD diagnosis and prediction. Finally, a neural network using the Extra Tree features selection strategy achieved 97% accuracy, while a neural network using Random Forest achieved 98% accuracy. These can be effectively used to improve current CVD diagnosis efforts and help patients all around the world. Especially in places where there is a scarcity of medical professionals. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Forecasting

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A System to Track Fabricated Content in Social media using Machine Learning Approach

A System to Track Fabricated Content in Social media using M...

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2024 IEEE International Conference on Recent Advances in science and engineering Technology, ICRASET 2024

作者： Hegde, Ramakrishna Soumyasri, S.M. Shivashankara, S. Kavyashree, M.K. Pemula, Rambabu Harshitha, M. Vidyavardhaka College of Engineering Affiliated to Visvesvaraya Technological University Dept. of Computer Science and Engineering Mysuru India Vidya Vikas Institute of Engineering and Technology Affiliated to Visvesvaraya Technological University Dept. of Computer Applications Mysuru India Kr Pete Krishna Govt. Engg. College Dept. of Computer Science and Engineering Krishnarajapet Mandya India Sjce Jssstu Mysore India Vidya Jyothi Institute of Technology Dept. of Artificial Intelligence Hyderabad India Vidya Vikas Institute of Engineering and Technology Dept.of Information Science and Engg. Mysore India

ISBN: (纸本)9798350388602

In today's society, when the internet is pervasive, everyone gets their news from a variety of online sources. Information can swiftly reach millions of users in a short amount of time due to the growing usage of social media sites like Facebook, Twitter, and others. Fake news has far-reaching effects, ranging from swaying election outcomes in favor of particular politicians to fostering prejudiced viewpoints. The primary platforms for disseminating fake news are Instagram, WhatsApp, and numerous other social media platforms. By presenting a machine learning-based fake news detection algorithm, this work provides a remedy. This model needs fictitious data that was gathered from multiple information sources. Data is extracted using web analytics, which is mostly used to build datasets. Real data sets and fake data sets are the two primary categories into which the data is separated. Random forest, logistic regression, decision trees, KNN, and gradient boosting are the classifiers used to classify the data. The data is categorized as true or false data based on the version that was received. The user will be able to determine whether or not the information on the website is fraudulent based on this. © 2024 IEEE.

关键词： Adversarial machine learning

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Gaining Perspective into Cloud Computing: A Business-Oriented Approach 4

Gaining Perspective into Cloud Computing: A Business-Oriente...

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Ahmad, Javed Ahmad, Shahnawaj Arif, Mohd. Nazim, Mohd Sharda School of Engineering and Technology Sharda University Department of Computer Science and Engineering Greater Noida201110 India Bennett University Department of Computer Science and Engineering Uttar Pradesh Greater Noida201310 India School of Computing Science and Engineering Department of Computer Applications Uttar Pradesh Greater Noida203201 India Noida Institute of Engineering and Technology Department of CSE Uttar Pradesh Greater Noida201306 India

ISBN: (纸本)9798350306118

Computing may be a profoundly examined subject within the technological and financial world, and numerous huge players in the software industry are working for the improvement of computing services. With the help of a literature review, this work has identified that the classification of cloud computing (CC) does not specify how to classify CC based on industry-driven professional articles, as most of this study has been based on academic distributions. This may limit the ability of the research study to provide a consolidated view of the most current advances in this topic. CC has recently captured the attention of scholars, financial professionals, and specialists. Many of these experts have proposed a variety of applications based on various cloud-based techniques, frameworks, and essentials, which have led to different interpretations, specifications, deployment models, and service models. Therefore, a complete characterization and classification of CC are needed to address the above issue. The ultimate purpose of this research is to classify CC in the literature, including the set of security concerns, history, concepts, technologies, and challenges. © 2023 IEEE.

关键词： Cloud security

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