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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Ibrahim Mahmood Rashid Al-Bakri Muhammad Imran Ahmad Mohd Nazrin Md Isa Mustafa Zuhaer Nayef Al-Dabagh Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Perlis Malaysia Collage of Agriculture Telafer University Telafer Iraq Department of Computer Science Knowledge University Erbil Iraq

The study of computer vision and pattern recognition is growing because of the various commercial and practical applications of these disciplines. Identification of individuals in a multitude, access control, forensics, and human-computer interactions are only among the topics studied by these areas. However, analyzing unconstrained face recognition poses ethical issues and privacy concerns. Many recent proposals employ Holistic Methods, Geometric Approach and Local-Texture Approach, methods and databases like ORL, FERET and AR Dataset to study constrained face recognition. At least some understanding of 2D perspective was achieved. This occurred in highly controlled environments where parameters such as camera angles, lighting and distance were strictly regulated. However, significant degradation in recognition performance occurred if the environment changed or the subject smiled or frowned. This critique discusses the technology utilized in face recognition, as well as the databases of methods that utilize this technology. To help guide future research, this article discusses current knowledge and suggests future directions for study in the field of facial recognition.

关键词： Privacy Three-dimensional displays Image recognition Databases Face recognition Lighting Cameras

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Analyzing The Employee Turnover by Using Decision Tree Algorithm

Analyzing The Employee Turnover by Using Decision Tree Algor...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Saadaldeen Rashid Ahmed Aso Kurdo Ahmed Swran Jawamir Jwmaa Department Computer Science College Of Engineering And Science Bayan University Erbil Iraq Department of Business Administration College of Administration and Economics Bayan University Erbil Iraq Department of business administration Swran Jawamir jwmaa Knowledge University Erbil Iraq

In knowledge-based organizations, employee turnover is a significant challenge. Frequently, a company's competitive advantage can be traced back to the tacit knowledge of its employees, which is lost when those employees depart. If a company is sincere about remaining ahead of the competition, it must do everything possible to prevent its employees from leaving in droves. To comprehend the factors contributing to employee turnover in enterprises, this article examines employee turnover. The worker was assigned to one of several predetermined attrition categories primarily based on employee demographic and employment history information. Python was used to develop decision tree models and rule sets. A predictive model was constructed using the results of the developed rule sets and decision tree models to predict future employee turnover cases. Also proposed was a software application framework to implement the study's recommendations.

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Evaluating Natural Language Descriptions Generated in a Workspace-Based Architecture 12

Evaluating Natural Language Descriptions Generated in a Work...

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12th International Conference on Computational Creativity, ICCC 2021

作者： Wright, George A. Purver, Matthew Cognitive Science Research Group School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Knowledge Technologies Jožef Stefan Institute Ljubljana Slovenia

ISBN: (纸本)9789895416035

This paper concerns the evaluation of a workspace architecture for generating natural language descriptions, including methods for evaluating both its output and its own self-evaluation. Herein are details of preliminary results from evaluation of an early iteration of the architecture operating in the domain of weather. The domain is not typically seen as creative, but provides a simple testbed for the architecture and evaluation methodology. The program does not yet match humans in terms of fluency of language, factual correctness, and how completely the input is described, but human judges did find the program’s output easier to read than human generated texts. Planned improvements to the program also described in the paper will incorporate self-monitoring and better self-evaluation with the aim of producing descriptions that are more fluently written and more accurate. © ICCC *** rights reserved.

关键词： Architecture

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Privacy-Preserving Multi-Task Online Matching in Mobile Crowdsensing: A Snapshot-Based Approach

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IEEE Internet of Things Journal 2025年

作者： Wei, Kaimin Zhao, Shiting Chen, Jinpeng Pei, Tingrui Sun, Dezhi Jinan University College of Information Science and Technology China Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Department of Information Science China

With the growing popularity of Mobile Crowdsensing (MCS), online matching has recently attracted considerable attention. However, most previous schemes focused on single-task matching, which limits their practicality in new MCS applications that require multi-task matching. Moreover, most MCS tasks require workers to share locations with the platform, which poses serious privacy concerns. To address this issue, we propose a privacy-preserving multi-task online matching algorithm in a snapshot-based mode (PMS). Specifically, the entire time period is divided into snapshots to reduce the waiting time for newly arrived tasks to be matched. In each snapshot, the planar Laplace-based privacy mechanism is applied to protect worker locations and ensure ε-geo-indistinguishability. Meanwhile, the Minimum-Cost Maximum-Flow (MCMF)-based multi-task matching mechanism is presented to maximize the task completion rate while minimizing the total travel cost. Experiments on real-world datasets demonstrate that PMS achieves superior task completion, reduced travel costs, and improved privacy preservation compared to existing algorithms. © 2014 IEEE.

关键词： Mobile Crowdsensing Multi-task Matching Privacy-Preserving Snapshot

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Leveraging Deep Learning for Accurate User Journey Prediction and Revenue Enhancement on Online Platforms

Leveraging Deep Learning for Accurate User Journey Predictio...

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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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