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Enhancing Classification Algorithm Recommendation in Automated Machine Learning: A Meta-Learning Approach Using Multivariate Sparse Group Lasso

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computer Modeling in engineering & Sciences 2025年第2期142卷 1611-1636页

作者： Irfan Khan Xianchao Zhang Ramesh Kumar Ayyasamy Saadat M.Alhashmi Azizur Rahim School of Software Dalian University of TechnologyDalian116620China Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia College of Computing and Informatics University of SharjahSharjah27272United Arab Emirates Department of Computer Systems Engineering University of Engineering and Applied SciencesSwat19200Pakistan

The rapid growth of machine learning(ML)across fields has intensified the challenge of selecting the right algorithm for specific tasks,known as the Algorithm Selection Problem(ASP).Traditional trial-and-error methods have become impractical due to their resource *** Machine Learning(AutoML)systems automate this process,but often neglect the group structures and sparsity in meta-features,leading to inefficiencies in algorithm recommendations for classification *** paper proposes a meta-learning approach using Multivariate Sparse Group Lasso(MSGL)to address these *** method models both within-group and across-group sparsity among meta-features to manage high-dimensional data and reduce multicollinearity across eight meta-feature *** Fast Iterative Shrinkage-Thresholding Algorithm(FISTA)with adaptive restart efficiently solves the non-smooth optimization *** validation on 145 classification datasets with 17 classification algorithms shows that our meta-learning method outperforms four state-of-the-art approaches,achieving 77.18%classification accuracy,86.07%recommendation accuracy and 88.83%normalized discounted cumulative gain.

关键词： Meta-learning machine learning automated machine learning classification meta-features

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Intelligent Farm Irrigation and Safety System 25

Intelligent Farm Irrigation and Safety System

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25th International Arab Conference on information technology, ACIT 2024

作者： Al-Smadi, Adnan Al-Nusairat, Murar Erjoub, Ammar Freitekh, Laith Khouj, Mohammed Zarqa Univeristy Faculty of Information Technology Department of Computer Science Zarqa Jordan Yarmouk University Engineering Faculty Depatrment of Electronics Engineering Irbid Jordan University of Business and Technology Department of Electrical Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

In recent years, there has been growing interest in utilizing technology to improve agricultural practices and optimize crop yield. One area of focus is the development of intelligent farm irrigation systems that integrate automation, sensing, and control mechanisms to enhance the efficiency and effectiveness of farming operations. An important aspect of automated agriculture is the management of irrigation, as well as ensuring the safety of the farming environment. Plastic houses have become a cornerstone in modern agriculture, enabling controlled cultivation environments and increased yields. However, managing these environments efficiently requires a harmonious integration of various factors. This paper proposes an intelligent for irrigation, temperature, humidity, flame, gas detection, and fire alarm functions in a plastic house. The system presented here uses Arduino Microcontroller based system that integrates various sensors technology to monitor soil conditions accompanied by audible and visual alerts. Fire safety is ensured through flame and gas sensors, triggering immediate alerts. Python language was used to write the code for the Arduino microcontroller. © 2024 IEEE.

关键词： Smart agriculture

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COVID-19 Prediction Model Empowered with Fused Computational Intelligence Technique

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IEIE Transactions on Smart Processing and Computing 2025年第1期14卷 109-117页

作者： Khan, Muhammad Adnan Naseer, Iftikhar Ali, Muhammad Nadeem Kim, Byung-Seo Department of Software Faculty of Artificial Intelligence & Software Gachon University Seongnam13120 Korea Republic of Faculty of Computer Science & Information Technology The Superior University Lahore54600 Pakistan Department of Software and Communications Engineering Hongik University Sejong Campus Korea Republic of

The novel Coronavirus (COVID-19) spread rapidly around the world and caused overwhelming effects on the health and economy of the world. It first appeared in Wuhan city of China and was declared a pandemic by the World Health Organization (WHO). Many researchers, as well as experts in clinical and artificial intelligence experts, are working together to control the rapid spread of COVID-19 with early detection. This study focused on intelligent prediction for coronavirus using computational intelligence approaches (IPC-FCIA) like convolutional neural networks, support vector machines, and fuzzy logic techniques. The proposed IPC-FCIA model is based on two sections namely the training section and the validation section. Features fusion and decision-level fusion are used in this study to enhance the performance of the recommended IPC-FCIA model. The proposed model predicts the early detection of COVID-19 in two types COVID-negative and COVID-positive. The benchmark results of the model show an accuracy of 97.66 % on decision-level fusion in the detection of COVID-19. The proposed model can be helpful for medical experts as well as COVID-19-affected patients. © 2025 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： COVID-19

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Towards distinguishing trust based attacks in an IoV network

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Journal of King Saud University - computer and information Sciences 2025年第4期37卷 1-15页

作者： Wang, Yingxun Mahmood, Adnan Mohd Sabri, Mohamad Faizrizwan Zen, Hushairi Faculty of Engineering Universiti Malaysia Sarawak Sarawak Kota Samarahan 94300 Malaysia Faculty of Computer and Information Engineering Qilu Institute of Technology Jinan 250200 China School of Computing Macquarie University Sydney 2109 NSW Australia Faculty of Engineering and Technology i-CATS University College Sarawak Kuching 93350 Malaysia

The paradigm of the Internet of Vehicles (IoV) promises significant innovative advancements and is receiving an increased attention of the researchers from both academia and industry. The IoV creates an intelligent network between different road entities and their corresponding roadside infrastructure via state-of-the-art sensing and communication technologies and, therefore, facilitates in addressing a range of safety-critical and non-safety vehicular applications. Accordingly, the security of an IoV network is of importance with internal attacks being of considerable concern. Although the conventional cryptography-based schemes are extremely intelligent in tackling the external attacks, trust has been recently employed to handle the internal attacks. Nevertheless, trust-based attacks also present a formidable challenge. Therefore, in this paper, an IoV-based trust management heuristic has been envisaged that takes into account both direct trust and indirect trust to ascertain the behaviors of the vehicles vis-à-vis time in a bid to detect various trust-based attacks, i.e., zig-zag attacks, self-promoting attacks, on-off attacks, and opportunistic attacks, along with the attackers’ multiple attacking strategies. The experimental findings further demonstrate that the envisaged IoV-based trust management heuristic exhibits prompt and accurate detection of the trust-based attacks in contrast to the state-of-the-art trust management mechanisms. © The Author(s) 2025.

关键词： Internet of vehicles Non-safety vehicular applications Safety-critical vehicular applications Trust-related attacks Vehicles’ behaviors

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Hybrid Deep Learning (CNN-BiLSTM-LSTM) Model for Prediction of Short Power Consumption 8

Hybrid Deep Learning (CNN-BiLSTM-LSTM) Model for Prediction ...

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8th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2024

作者： Amirkhanloo, Danial Chitsaz, Hamid Mohajerzadeh, Amirhossein Ferdowsi University of Mashhad Department of Computer Engineering Mashhad Iran Sohar University Faculty of Computing and Information Technology Sohar Oman

ISBN: (纸本)9798350374674

The increase in electricity-based equipment caused by technological advances results in inordinate energy consumption and request for day-to-day power. Furthermore, energy forecasting for individual residential customers acts increasingly as an important factor in operating and designing future grids. A variety of methodologies has been used in order to project energy usage at distinct layers of distribution and transferring systems. In the current study, a new hybrid deep learning approach is presented to estimate power usage in smart homes. First, our model processes the data, excludes missing values, scales the data, and transforms them to assure the model performance. The pre-processed data is subsequently introduced to the proposed joint model as input, which uses CNN, LSTM, Bi-LSTM to choose hybrid discriminative features. Eventually, the fully connected layers are employed. The experiments were carried out to assess and compare the forecasting performance of the proposed method with recent models using the IHEPC dataset, including several variants. The outcomes show that the suggested network has the lowest mean MSE, RMSE and MAPE, compared to other cutting-edge models. © 2024 IEEE.

关键词： Forecasting

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Detection of Customer Satisfaction in In-Person Telecommunication Services Using Automated Facial Image Analysis with Artificial Intelligence 11

Detection of Customer Satisfaction in In-Person Telecommunic...

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11th International Symposium on Telecommunication, IST 2024

作者： Gangan, Hadiseh Arab Rohani, Mehrdad Hosseini, Seyed Ali Mansouri, Alireza Birjand University Department of Electrical and Computer Engineering Birjand Iran Information Technology Faculty & ICT Research Institute Tehran Iran

ISBN: (纸本)9798350356250

In the present era, improving customer experience and enhancing services in the telecommunications industry are of paramount importance. One of the fundamental criteria for evaluating customer experience is their level of satisfaction with services at physical sales points. This paper focuses on the automated analysis of facial images using artificial intelligence to determine the satisfaction level of inperson visitors to telecommunications service booths. Advanced technologies such as transfer learning and the use of the EfficientNet network architecture have been employed to provide a more accurate and efficient method for facial emotion analysis. The results of this study demonstrate that the proposed method significantly improves the accuracy and efficiency of emotion detection and, consequently, the satisfaction or dissatisfaction of visitors in both implementation stages. This research not only contributes to the improvement of service processes and the enhancement of customer experience but also confirms the superiority and reliability of this method by comparing the results with previous approaches. These findings can assist telecommunications businesses in improving services and increasing customer satisfaction, guiding them towards optimized performance and success. This study is introduced as a solution for improving the customer experience process during in-person visits to telecommunications service offices, which has extensive applicability in enhancing communication systems, increasing employee productivity, improving service delivery supervision and management, and enhancing responsiveness and service quality at telecommunications service counters. Additionally, it provides for increased customer satisfaction and loyalty, optimization of internal company processes, improved marketing strategies, and more accurate assessment of customer experience status. © 2024 IEEE.

关键词： Emotion Recognition

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IoT Based Systems and Services: Recent Security Concerns and Feasible Solutions 1

IoT Based Systems and Services: Recent Security Concerns and...

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1st IEEE Karachi Section Humanitarian technology Conference, Khi-HTC 2024

作者： Attaullah, Hafiz Muhammad Memon, Shahzad Erkan, Ozlem Feyza Khawar, Ruba Mohammad Ali Jinnah University Department of Computer Science Karachi Pakistan Beykoz University Faculty of Engineering and Architecture Department of Computer Engineering Istanbul Turkey NED University of Engineering and Technology Faculty of Information Sciences and Humanities Karachi Pakistan

ISBN: (纸本)9798350373042

The term Internet of Things (IoT) is used to refer as embedded devices or objects with internet access, allowing them to communicate globally, interacting with people and networks. IoT security issues are directly related to the widespread use of its systems. This paper examines the security technologies employed in IoT and their respective features and applications. Additionally, it addresses the various security concerns associated with IoT-based systems and services, highlighting their occurrence within the three-layer framework of IoT. These days, as IoT introduces advanced application architectures that minimizes human intervention by integrating businesses in some way to address digital computer processes by communicating within a given network. Moreover, it will be discussed the key technologies associated with Advanced Encryption Standards (AES) and the changes brought in market value and usage of services that has been taken place b since the IoT incorporated. © 2024 IEEE.

关键词： Network security

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques 5

Novel Approach for Enhancing Mental Well-Being Through Machi...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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Multi-Agent System Based on Stacking Technique for Rice Yield Prediction 3

Multi-Agent System Based on Stacking Technique for Rice Yiel...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Seireg, Hayam R. Omar, Yasser M. Elmahalawy, Ahmed Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Menouf Egypt Aastmt Faculty of Computing and Information Technology Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350323511

Artificial intelligence (AI) has expanded its influence across various sectors including education, healthcare, and agriculture. In the agricultural setting, the multiagent system (MAS) is recognized as a powerful tool for optimizing resource allocation, enhancing decision-making processes, and improving overall farm productivity. Accurate prediction of rice yield levels is paramount importance in the agricultural sector. It allows farmers, policymakers, and stakeholders to make informed decisions regarding crop management. Individual machine-learning algorithms (MLA) have been used to predict rice yield levels, but they may not fully exploit the available information. Therefore, the novel system implemented based on stacking techniques to solve complex problems. The objective of this paper is to present an efficient system that leverages MAS based on a novel proposed stacking technique (Extra Trees Classifier (ETC), Random Forest Classifier (RFC), Linear Discriminant Analysis (LDA) and Gaussian Naive Bayes (GNB)) for improving the rice yield level prediction within the agricultural environment. Each algorithm brings its unique approach to handling complex relationships, and modeling class separability. The findings from this study provided valuable insights for decision-making in interconnected sectors and facilitating optimal business planning. The dataset incorporated climate variables such as monthly maximum and minimum temperatures and rainfall. The final result of the dataset consists of 1266 rows and 18 features. The results showed that the novel proposed stacking technique achieved the highest prediction accuracy 87% and the best individual Decision tree classifier obtained 77.5%. The novel proposed stacking technique increases the accuracy by 9.5% compared to the best individual MLA. © 2023 IEEE.

关键词： Multi agent systems

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Impact of Distributed Generation Integration on Distribution System Reliability

Impact of Distributed Generation Integration on Distribution...

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2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Vukušić, Mario Šljivac, Damir Marić, Predrag Žnidarec, Matej J.J. Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek Croatia

ISBN: (纸本)9789531842976

Ensuring the reliability of electrical power systems is essential for meeting the growing energy demands of modern society. In this paper, we investigate the technical implications of distributed generation deployment, focusing on its influence on system reliability indices such as frequency and duration of distribution grid components failures and resulting expected energy not delivered to the consumers. In order to assess this impact a reliability analysis is performed for case study distribution network in DIgSILENT PowerFactory software for integration scenarios of variable photovoltaic generation and constant biogas power plants generation focusing on possibility of islanded operation of distributed generation on reliability indices. © 2024 IEEE.

关键词： Power distribution reliability

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