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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Selvarani V. Kanpur Rani Sakar Fatah Sulaiman V. Selvi Ali Ali Saber Mohammed M. Karthik Ministry of Education's Innovation Cell New Delhi India Department of EEE Hindustan Institute of Technology and Science Chennai India Department of Business Administration College of Administrative and Financial Sciences Knowledge University Erbil Iraq Department of computer science Mother Teresa Women's University Kodaikanal India Department of Computer Engineering Technologies Al Kitab University Kirkuk Iraq Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

The rapid evolution of online platforms has transformed how businesses engage with customers, making accurate user journey prediction crucial for enhancing revenue. This study investigates the application of deep learning techniques, particularly Multi-Layer Perceptron-Gated Recurrent Unit (MLP-GRU) models, to analyze and predict user behavior effectively. By leveraging a comprehensive dataset collected from e-commerce sales, we implement a systematic approach that includes data collection, preprocessing through Min-Max normalization, feature extraction using attention mechanisms, and revenue enhancement strategies. The attention mechanism allows the model to focus on significant user interactions, dynamically weighing the importance of various features to improve predictive accuracy. Our findings demonstrate that the MLP-GRU model not only captures intricate patterns in user behavior but also enables personalized recommendations that significantly boost conversion rates. By integrating deep learning with user journey analytics, businesses can optimize their marketing strategies, tailor user experiences, and ultimately drive higher revenue. The proposed method is implemented in Python and has an accuracy of about 99.11% which is higher than other existing methods like CNN, BiLSTM and LSTM. This research contributes to the growing body of knowledge in the field of artificial intelligence in e-commerce and provides actionable insights for practitioners aiming to leverage data-driven strategies to enhance customer engagement and satisfaction.

关键词： Deep learning Accuracy Attention mechanisms Systematics Predictive models Data collection Smart systems Feature extraction Electronic commerce Business

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Improving Data Encoding for Enhanced AI Performance in Complex Datasets via Quantum Feature Space Mapping: Harnessing Quantum Algorithms

Improving Data Encoding for Enhanced AI Performance in Compl...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Gurpreet Singh Walia Sathiya Priya S KV Karthikeya D. Suresh P Sudheer V. Syambabu Lead Enterprise Security and Networking Architect NetApp Inc. Durham NC Department of ECE Panimalar Engineering College Chennai AT&T Sattva Knowledge City Hyderabad Department of ECE St. Joseph's Institute of Technology Chennai Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The purpose of this study is to investigate the potential for quantum feature space mapping to be utilized in the enhancement of data encoding approaches for artificial intelligence (AI) systems that are capable of managing complicated datasets. Traditional artificial intelligence models frequently face challenges when confronted with the high-dimensionality and non-linearity of such datasets. This can hinder their performance in areas such as pattern recognition, optimization, and predictive modeling. Quantum computing, which is able to make use of quantum superposition and entanglement, provides a method that is revolutionary in nature by means of quantum feature space mapping. By encoding data into quantum states, this technology enables the construction of feature spaces that are tenfold bigger than those that would be possible in classical conditions. This makes it much simpler to differentiate between intricate patterns that would otherwise be indistinguishable. The purpose of this research is to investigate the application of quantum algorithms, such as Quantum Support Vector Machines (QSVM) and Variational Quantum Classifiers (VQC), with the intention of enhancing the capacity of artificial intelligence to learn from complex and high-dimensional data. We demonstrate that quantum feature space mapping leads to more efficient and accurate data classification, regression, and clustering tasks by comparing the performance of quantum-enhanced artificial intelligence models to that of their classical counterparts on difficult datasets. Additionally, the research addresses important difficulties, such as the constraints of the quantum hardware that is now available and the requirement for quantum circuits that are scalable. Quantum computing has the potential to improve the performance of artificial intelligence in the management of complicated, high-dimensional data, which may ultimately pave the way for advances in domains such as genetics, finance, and cryptogra

关键词： Support vector machines Quantum computing Quantum algorithm Quantum entanglement Computational modeling Space mapping Quantum state Encoding Hardware Quantum circuit

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Aggregative Dynamics and Revenue Structuring in Energy Internet User-Prosumer Ecosystems

Aggregative Dynamics and Revenue Structuring in Energy Inter...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Anil Pratap Singh Ch Veena R J Anandhi Atul Singla Ashish Parmar Haider Alabdely Department of Computer Science & Engineering IES Insititute of Technology and Management IES University Bhopal M.P India Institute of Aeronautical Engineering Dundigal Hyderabad Department of Information Science Engineering New Horizon College of Engineering Bangalore Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh The Islamic university Najaf Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This study explores income structure and group dynamics in complicated energy internet consumer-prosumer ecosystems.a DRORA (Dynamic Revenue Optimization and Aggregation) is proposed after studying energy consumers and prosumers throughout time. PDPA, EDOA, and RFO are in the system. These boost energy market efficiency, transparency, and fairness. As a way to promote fair pay and customer cost, the PDPA lets prosumers have a say in how prices are set. The EDOA makes sure that resources are used efficiently by distributing power in the best way possible to cut down on costs and improve system performance. With the RFOA’s accurate income predictions, stakeholders can make smart decisions that will help them make the most money. Simulations and real-world studies have shown that the suggested model is better than traditional methods at getting stable and fair income sharing in the Energy Internet environment, which is always changing.

关键词： Technological innovation Costs Regulators System performance Ecosystems Energy Internet Modulation

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China affiliated with the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510641 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

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Optimal Elevators Control for Buildings Based on Associative Memory

Optimal Elevators Control for Buildings Based on Associative...

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Saadaldeen Rashid Ahmed Abdulqadir Ismail Abdullah Sabah Jebur AlJanabi Zaki Saeed Tawfeek Ahmed Dheyaa Radhi Rafad Imad Kadhim Sameer Algburi Wassan Sdnsn Hashim Jungpil Shin Computer Science Department Bayan University Erbil Kurdistan Iraq Artificial Intelligence Engineering Department College of Engineering Al-Ayen University Thi-Qar Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil Kurdistan Region Iraq Electrical Power Techniques Al-Ma’moon University College Al-Washash Baghdad Iraq Engineering Computer Technology department AL Hikma University College Baghdad Iraq College of Pharmacy University of Al-Ameed Karbala Iraq Medical Devices Technology Engineering University of hilla Babylon Iraq Al-Kitab University Kirkuk Iraq School of Computer Science and Engineering The University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9798331540104

ISBN: (纸本)9798331540111

Conventional elevators encounter the issues of energy efficiency, waiting periods, and lack of adaptation under dynamic conditions of the buildings. To this purpose, this paper provides an elevator control optimization model based on associative memory in order to tackle these challenges. To minimize energy usage and waiting time and increase the responsiveness of the entire system to real-time passenger demand. This technology leverages associative memory—specifically Hopfield networks—to effectively learn and reallocate elevator assignments adjusted to past traffic flows. The observed results represent up to 31% energy consumption reductions and 33% waiting time savings that show the function of the proposed system in optimizing traffic and energy consumption patterns at different building sizes and traffic scenarios. This research introduces a new elevator control system employing associative memory, which demonstrates considerable improvement in energy efficiency and lower waiting times for passengers. These findings will lead to additional exploration, notably for real-time application, interaction with IoT and building management systems, and scaling to bigger structures.

关键词： Associative memory Energy consumption Automation Computational modeling Control systems Elevators Real-time systems Energy efficiency Internet of Things Building management systems

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Towards Target-Dependent Sentiment Classification in News Articles 16th

Towards Target-Dependent Sentiment Classification in News Ar...

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16th International Conference on Diversity, Divergence, Dialogue, iConference 2021

作者： Hamborg, Felix Donnay, Karsten Gipp, Bela Department of Computer Science University of Konstanz Konstanz Germany Heidelberg Academy of Sciences and Humanities Heidelberg Germany Department of Political Science University of Zurich Zurich Switzerland Data and Knowledge Engineering University of Wuppertal Wuppertal Germany

ISBN: (纸本)9783030713041

Extensive research on target-dependent sentiment classification (TSC) has led to strong classification performances in domains where authors tend to explicitly express sentiment about specific entities or topics, such as in reviews or on social media. We investigate TSC in news articles, a much less researched domain, despite the importance of news as an essential information source in individual and societal decision making. This article introduces NewsTSC, a manually annotated dataset to explore TSC on news articles. Investigating characteristics of sentiment in news and contrasting them to popular TSC domains, we find that sentiment in the news is expressed less explicitly, is more dependent on context and readership, and requires a greater degree of interpretation. In an extensive evaluation, we find that the current state-of-the-art in TSC performs worse on news articles than on other domains (average recall AvgRec= 69.8 on NewsTSC compared to AvgRev= [75.6, 82.2 ] on established TSC datasets). Reasons include incorrectly resolved relation of target and sentiment-bearing phrases and off-context dependence. As a major improvement over previous news TSC, we find that BERT’s natural language understanding capabilities capture the less explicit sentiment used in news articles. © 2021, Springer Nature Switzerland AG.

关键词： Decision making

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Comprehensive Approaches to Securing Multi-Cloud Architectures: Best Practices and Emerging Solutions

Comprehensive Approaches to Securing Multi-Cloud Architectur...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Raveena R Nair D. Sreevidya Chinnem Rama Mohan Joydeep Banerjee Kuldeep Chouhan Dharamvir Saveetha School of Law Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai India Department of CSE (CyS DS) and AI & DS VNRVJIET Nizampet Hyderabad India Department of Computer Science and Engineering Narayana Engineering College Nellore Andhra Pradesh India NSHM Knowledge Campus Durgapur Kolkata West Bengal India Department of Computer Science Applications Sharda School of Engineering and Technology Greater Noida Uttar Pradesh India Department of MCA The Oxford College of Engineering Bommanahalli Bengaluru Karnataka India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

With an emphasis on multi-cloud networks, this research paper offers a synopsis of cloud computing techniques. It covers the fundamentals of cloud programming, the difficulties that cloud computing faces, and the ways that multi-cloud systems, such as cloud hybridization, federation clouds, cross-clouds, and multi-clouds, might help solve these problems. The opportunity of multi-cloud structures in combination with modern technologies such as Big Data and also Machine Learning (ML) is explored as well in the study, leading to some fascinating breakthroughs. To assist researchers, grasp the difficulties and generate enthusiasm within the community for the upcoming pairing of multi-cloud services with additional cutting-edge technologies, the aim is to present a contemporary perspective on multi-cloud technology and solve outstanding concerns.

关键词： Cloud computing Machine learning computer architecture Companies Programming Maintenance engineering Big Data Safety Informatics Faces

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Privacy Protection of Healthcare Data over Social Networks Using Machine Learning Algorithms

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Computational Intelligence and Neuroscience 2022年第0期2022卷 9985933页

作者： Khan, Shakir Saravanan, V. Gnanaprakasam, C.N. Lakshmi, T. Jaya Deb, Nabamita Othman, Nashwan Adnan Riyadh Saudi Arabia Department of Computer Science College of Engineering and Technology Dambi Dollo University Oromia Region Dambi Dollo Ethiopia Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering Tamilnadu Chennai600119 India Department of Computer Science and Engineering Srm University AP Amaravati India Department of Information Technology Gauhati University Gawahati Assam781014 India Department of Computer Science College of Science Knowledge University Erbil44001 Iraq

With the rapid development of mobile medical care, medical institutions also have the hidden danger of privacy leakage while sharing personal medical data. Based on the k-Anonymity and l-diversity supervised models, it is proposed to use the classified personalized entropy l-diversity privacy protection model to protect user privacy in a fine-grained manner. By distinguishing solid and weak sensitive attribute values, the constraints on sensitive attributes are improved, and the sensitive information is reduced for the leakage probability of vital information to achieve the safety of medical data sharing. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. Data analysis and experimental results show that this method can minimize execution time while improving data accuracy and service quality, which is more effective than existing solutions. The limits of solid and weak on sensitive qualities are enhanced, sensitive data are reduced, and the chance of crucial data leakage is lowered, all of which contribute to the security of healthcare data exchange. This research offers a customized information entropy l-diversity model and performs experiments to tackle the issues that the information entropy l-diversity model does not discriminate between strong and weak sensitive features. The scope of this research is that this paper enhances data accuracy while minimizing the algorithm's execution time. © 2022 Shakir Khan et al.

关键词： Electronic data interchange

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Optimization of Building Construction Control System Based on Nonlinear Image Enhancement

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computer-Aided Design and Applications 2023年 20卷 213-224页

作者： Liu, Congru Lin, Mingsen Ghafoor, Kayhan Zrar Al-Dabagh, Mustafa Zuhaer Nayef Karras, Dimitrios A. School of Urban Construction Wuhan University of Science and Technology Wuhan China Changjiang Polytechnic Wuhan China Department of Computer Science Knowledge University Erbil44001 Iraq Department of Software & Informatics Engineering Salahaddin University-Erbil Erbil44001 Iraq School of Science Dept. General Athens Greece

To reflect the nonlinear characteristics of the building structural adjustment system, an active vibration control strategy based on the nonlinear is proposed. In this method, the size of the structural control force is determined by comparing the allowable energy with the actual energy, and its direction is determined by taking the derivative of the energy. Because there is no need to solve the linear matrix inequality, the calculation is simple and the operation is convenient. Moreover, when multiple actuators with the same capacity limit are used, the control output force is consistent, thus avoiding the design of multiple control systems and simplifying the controller design. The simulation results show that the maximum controlled displacements of floor 6 to floor 8 are 1.16cm, 1.12cm, and 0.97cm, respectively. The proposed nonlinear energy control method has the advantages of simple control process and avoiding actuator saturation. © 2023 CAD Solutions, LLC.

关键词： Floors

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A Comparative Analysis of IoT Data Using Machine Learning Algorithms 15th

A Comparative Analysis of IoT Data Using Machine Learning Al...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Nagashree, B.A. Satheesh Kumar, S. Muthukumaran, V. Munrathnam, Meram Angeline Kavitha, M. Department of Computer Science and Applications School of CSA REVA University Bangalore India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.K. Valley Kadapa District Andhra Pradesh Idupulapaya516330 India Department of Mathematics Voorhees College Vellore632001 India

ISBN: (纸本)9783031889912

IoT (Internet of Things) is revolutionizing the commercial environment by acquiring vast amounts of data from various sources. However, processing and analyzing this data is challenging due to the large volume of IoT devices. AI-powered IoT creates intelligent machines that mimic human intelligence, aiding decision-making with minimal human input. Both individuals and professionals’ benefit from this combination, as IoT is concerned with communicating online, but AI allows machines to learn from data and experience. This study demonstrates the analysis of IoT data using machine learning algorithms and compares the accuracy of various machine learning algorithms used. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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