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International Conference on computer and Information Science (ACIS)

作者： Isidoros Perikos Chara Pylarinou Computer Engineering and Informatics Department University of Patras Computer Technology Institute and Press “Diophantus” Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece

ISBN: (数字)9798350376647

ISBN: (纸本)9798350376654

Emotion recognition in text has become an essential research area within artificial intelligence and natural language processing due to its applications in sentiment analysis, human-computer interaction, and social media analysis. This paper introduces an approach for emotion recognition in social networks using transformer models enhanced with explainability techniques. By leveraging the advanced capabilities of transformers to analyze and classify emotions in written content, our finetuned models achieves high accuracy in identifying emotions according to the Ekman emotional model. The integration of LIME and SHAP provides transparency and interpretability to our model, making it more trustworthy for practical applications. Our findings indicate that models like XLNET report an exceptional performance in emotion recognition tasks. The explainability techniques illustrate the transformer model decision-making processes and the way that they utilize words and their continual information in sentences.

关键词： Human computer interaction Emotion recognition Analytical models Sentiment analysis Social networking (online) Computational modeling Predictive models Transformers Data models Artificial intelligence

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A Structured Analysis on IPL 2022 matches by approaching various Data Visualization and Analytics

A Structured Analysis on IPL 2022 matches by approaching var...

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2023 International Conference on computer Communication and informatics, ICCCI 2023

作者： Selva Birunda, S. Nagaraj, P. Jency A Jebamani, B. Revathi, B. Muneeswaran, V. Ramco Institute of Technology Department of Artificial Intelligence and Data Science Virudhunagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350348217

The main purpose of making this report is to give the information to the franchise to make their decision on buying the players by bidding in the auction based on their performances. There are many models of reports which describe the IPL seasons. This report's purpose is to give a brief analysis of the IPL 202215th league. In this report, much of the data from the datasets of matches is utilized and analysis is made by using Python packages specifically, Pandas, NumPy, and matplotlib. By making graphs and tables a brief analysis with complete knowledge of an IPL season is given. This analysis describes every good performance and gives good chance to the best players. © 2023 IEEE.

关键词： Data visualization

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Opposition-based multi-objective ant colony optimization framework for sustainable retrofitting: time–cost–energy–risk trade-offs

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Asian Journal of Civil engineering 2025年第5期26卷 2223-2239页

作者： Pokkuluri, Kiran Sree Chauhan, Trupti Ravindra Macedo, Víctor Daniel Jiménez Manjunath, T.C. Bharadwaj, Manish Sethi, Krushna Chandra Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Design Manipal School of Architecture and Planning Manipal Academic of Higher Education Manipal Karnataka Udupi India Mechanical Engineering Faculty Michoacan University of Saint Nicholas of Hidalgo Morelia Mexico Department of Computer Science and Engineering Rajarajeswari College of Engineering Bengaluru India Department of Management Studies Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior India Civil Engineering Department Centurion University of Technology and Management Gajapati India

The increasing emphasis on sustainable retrofitting of buildings has brought attention to the need for optimizing trade-offs among critical objectives, such as time, cost, energy consumption, and risk. This study proposes an opposition-based multi-objective ant colony optimization (OB-MOACO) framework to address these challenges effectively. The framework incorporates opposition-based learning to enhance solution exploration and convergence in multi-objective optimization problems, specifically tailored for sustainable retrofitting projects. The study develops a time–cost–energy–risk trade-off (TCERT) model with four objectives: minimizing retrofitting time, cost, energy consumption, and risks. The model integrates constraints related to budget, project timelines, and energy efficiency to ensure feasibility and sustainability. A detailed case study of retrofitting a mixed-use building is presented, encompassing 11 aspects such as structural reinforcement, energy efficiency enhancement, and accessibility improvements. Results demonstrate the superiority of OB-MOACO over conventional methods like NSGA-III, MODE, and MOPSO in achieving Pareto-optimal solutions. Key performance metrics, including hypervolume (HV) and inverted generational distance (IGD), highlight the model's efficiency in balancing competing objectives. The study contributes to advancing sustainable retrofitting practices by providing an innovative, cost-effective, and energy-efficient decision-making framework. Implications for practitioners and policymakers are discussed, alongside recommendations for future research. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Multi-objective optimization Opposition-based ant colony optimization Pareto optimization Sustainable retrofitting Time–cost–energy–risk trade-off

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Federated Feature Concatenate Method for Heterogeneous Computing in Federated Learning

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computers, Materials & Continua 2023年第4期75卷 351-371页

作者： Wu-Chun Chung Yung-Chin Chang Ching-Hsien Hsu Chih-Hung Chang Che-Lun Hung Department of Information and Computer Engineering Chung Yuan Christian UniversityTaoyuanTaiwan Department of Computer Science and Information Engineering Asia UniversityTaichungTaiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichungTaiwan Department of Computer Science&Communication Engineering Providence UniversityTaichungTaiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung UniversityTaipeiTaiwan

Federated learning is an emerging machine learning techniquethat enables clients to collaboratively train a deep learning model withoutuploading raw data to the aggregation server. Each client may be equippedwith different computing resources for model training. The client equippedwith a lower computing capability requires more time for model training,resulting in a prolonged training time in federated learning. Moreover, it mayfail to train the entire model because of the out-of-memory issue. This studyaims to tackle these problems and propose the federated feature concatenate(FedFC) method for federated learning considering heterogeneous *** leverages the model splitting and feature concatenate for offloadinga portion of the training loads from clients to the aggregation server. Eachclient in FedFC can collaboratively train a model with different cutting ***, the specific features learned in the deeper layer of the serversidemodel are more identical for the data class classification. Accordingly,FedFC can reduce the computation loading for the resource-constrainedclient and accelerate the convergence time. The performance effectiveness isverified by considering different dataset scenarios, such as data and classimbalance for the participant clients in the experiments. The performanceimpacts of different cutting layers are evaluated during the model *** experimental results show that the co-adapted features have a criticalimpact on the adequate classification of the deep learning model. Overall,FedFC not only shortens the convergence time, but also improves the bestaccuracy by up to 5.9% and 14.5% when compared to conventional federatedlearning and splitfed, respectively. In conclusion, the proposed approach isfeasible and effective for heterogeneous clients in federated learning.

关键词： Federated learning deep learning artificial intelligence heterogeneous computing

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The Novel DeepBoost Multi Output Regressor for Household Finance Intelligence

The Novel DeepBoost Multi Output Regressor for Household Fin...

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Advanced research in Computing (ICARC), International Conference on

作者： S. H. S. V. Suwandaratna G. O. Priyankara Department of Computer Science and Engineering Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331530983

ISBN: (纸本)9798331530990

This paper forecasts the microeconomic level household expenditures using a novel hybrid deep learning approach. In terms of research significance, household finance control has a major influence on the finance system within the economy. Accurate forecasting of household finances assists in maintaining positive financial behavior among individuals and the economy. The DeepBoost multi-output regressor proposed in this paper is based on the 1D CNN-ANN and the XGBoost. The proposed model in this paper is compared with the R 2 , MSE, and MAE since it’s a regression problem. The experimental results reveal that the proposed DeepBoost multi-output regressor has the best application in forecasting the multiple expenditures of households by outperforming the ANN, 1D CNN-ANN, and Random Forest Regressor models. The proposed DeepBoost multi-output regressor evaluated the housing, food, transportation, healthcare, other necessities, childcare, and tax expenditures that had 0.94, 0.98, 0.83, 0.94, 0.97, 0.97, and 0.99 values for the R 2 , 9037.71, 2692.12, 9788, 15077.33, 1373.93, 13629.36, and 1904.52 values for the MSE, and 66.07, 34.05, 73.17, 87.05, 26.25, 78.74, and 29.47 MAE values than the ANN, RFR, and 1D CNN-ANN models.

关键词： Deep learning Pediatrics Computational modeling Finance Transportation Medical services Predictive models Microeconomics Forecasting Random forests

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Private Computation of Boolean Functions Using Single Qubits 15th

Private Computation of Boolean Functions Using Single Qubit...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Rahmani, Zeinab Pinto, Armando N. Barbosa, Luis S. Department of Electronics Telecommunications and Informatics University of Aveiro Aveiro Portugal Instituto de Telecomunicações Aveiro Portugal International Iberian Nanotechnology Laboratory Braga Portugal Department of Computer Science University of Minho Braga Portugal Institute of Systems and Computer Engineering Technology and Science Braga Portugal

ISBN: (纸本)9783031856990

Secure Multiparty Computation (SMC) facilitates secure collaboration among multiple parties while safeguarding the privacy of their confidential data. This paper introduces a two-party quantum SMC protocol designed for evaluating binary Boolean functions using single qubits. Complexity analyses demonstrate a reduction of 66.7% in required quantum resources, achieved by utilizing single qubits instead of multi-particle entangled states. However, the quantum communication cost has increased by 40% due to the amplified exchange of qubits among participants. Furthermore, we bolster security by performing additional quantum operations along the y-axis of the Bloch sphere, effectively hiding the output from potential adversaries. We design the corresponding quantum circuit and implement the proposed protocol on the IBM Qiskit platform, yielding reliable outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Qubits

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An Attention-Based Deep Network for Plant Disease Classification

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Machine Graphics and Vision 2024年第1期33卷 47-67页

作者： Bera, Asish Bhattacharjee, Debotosh Krejcar, Ondrej Department of Computer Science and Information Systems Birla Institute of Technology and Science Rajasthan Pilani India Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Králové Czech Republic Škoda Auto University Mladá Boleslav Czech Republic Universiti Teknologi Malaysia Kuala Lumpur Malaysia

Plant disease classification using machine learning in a real agricultural field environment is a difficult task. Often, an automated plant disease diagnosis method might fail to capture and interpret discriminatory information due to small variations among leaf sub-categories. Yet, modern Convolutional Neural Networks (CNNs) have achieved decent success in discriminating various plant diseases using leave images. A few existing methods have applied additional pre-processing modules or sub-networks to tackle this challenge. Sometimes, the feature maps ignore partial information for holistic description by part-mining. A deep CNN that emphasizes integration of partial descriptiveness of leaf regions is proposed in this work. The efficacious attention mechanism is integrated with high-level feature map of a base CNN for enhancing feature representation. The proposed method focuses on important diseased areas in leaves, and employs an attention weighting scheme for utilizing useful neighborhood information. The proposed Attention-based network for Plant Disease Classification (APDC) method has achieved state-of-the-art performances on four public plant datasets containing visual/thermal images. The best top-1 accuracies attained by the proposed APDC are: PlantPathology 97.74%, PaddyCrop 99.62%, PaddyDoctor 99.65%, and PlantVillage 99.97%. These results justify the suitability of proposed method. © 2024 institute of Information technology, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Convolutional neural networks

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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Bilingual Chatbot Powered by Artificial Intelligence for academic Advice

Bilingual Chatbot Powered by Artificial Intelligence for Aca...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Torres-Cruz, Fred Coyla-Idme, Leonel Juarez-Vargas, Juan C. Singh, Rajesh Upender, Maloth Kapila, Dhiraj Universidad Nacional Del Altiplano de Puno Academic Department of Statistics and Computer Engineering Puno Peru Uttaranchal University Uttaranchal Institute of Technology India Hyderabad India Lovely Professional University Department of Computer Science & Engineering Punjab Phagwara India

ISBN: (纸本)9798350308167

The role of academic advising in advancing institutions' educational goals is becoming more widely recognised. Utilizing technology tools like Canvas and Degree Works, universities have made considerable investments in assisting instructional advisers and student success measures. The demand for innovation that can support academic advisors in their duty to direct students toward academic achievement is growing, and AI tools that encourage the effective use of human capital are well-placed to fill that requirement. Functions for chatbots can be used as technology-mediated assistance to give knowledge and engaging content to help students succeed. This project set out to create, build, and assess an automated, AI-enabled advising resource (the AVA chatbot) for use in an honour's college setting to provide academic counselling and university-related information. The researchers came to the conclusion that chatbots could assist educational universities in efficiently supporting their student populations by giving 24/7 advice, following and evaluating total assistance, and identifying data perceptions in real time. The consequences of this research can be helpful for chat application design and manufacturing as well as technology adoption in higher education. © 2023 IEEE.

关键词： academic advice AI Chatbot Higher education

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Did State-Sponsored Trolls Shape the 2016 US Presidential Election Discourse? Quantifying Influence on Twitter 9th

Did State-Sponsored Trolls Shape the 2016 US Presidential E...

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Security and Privacy in Social Networks and Big Data - 9th International Symposium, SocialSec 2023, Proceedings

作者： Salamanos, Nikos Jensen, Michael J. Iordanou, Costas Sirivianos, Michael Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol3036 Cyprus Institute for Governance and Policy Analysis University of Canberra Canberra2601 Australia

ISBN: (纸本)9789819951765

It is a widely accepted fact that state-sponsored Twitter accounts operated during the 2016 US presidential election, spreading millions of tweets with misinformation and inflammatory political content. Whether these social media campaigns of the so-called "troll" accounts were able to manipulate public opinion is still in question. Here, we quantify the influence of troll accounts on Twitter by analyzing 152.5 million tweets (by 9.9 million users) from that period. The data contain original tweets from 822 troll accounts identified as such by Twitter. We construct and analyze a very large interaction graph of 9.3 million nodes and 169.9 million edges using graph analysis techniques and a game-theoretic centrality measure. Then, we quantify the influence of all Twitter accounts on the overall information exchange as defined by the retweet cascades. We provide a global influence ranking of all Twitter accounts, and we find that one troll account appears in the top-100 and four in the top-1000. This, combined with other findings presented in this paper, constitute evidence that the driving force of virality and influence in the network came from regular users - users who have not been classified as trolls by Twitter. On the other hand, we find that, on average, troll accounts were tens of times more influential than regular users were. Moreover, 23% and 22% of regular accounts in the top-100 and top-1000, respectively, have now been suspended by Twitter. This raises questions about their authenticity and practices during the 2016 US presidential election. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Social aspects

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