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Investigating long short-term memory approach for extremist messages detection in Kazakh language Expert Syst. (IF 3.3) Pub Date : 2024-03-27 Mussiraliyeva Shynar Zhenisbekovna, Bolatbek Milana Aslanbekkyzy, Baispay Gulshat Bolatkyzy
In recent years, there has been a noticeable increase in both individuals and organizations utilizing social networks for illicit purposes. This trend can be viewed as a potential threat to the national security of the country. In this article, the authors pay attention to how various extremist organizations use social networks in their activities, and offer LSTM-based models for classifying extremist
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Extracting the explore-exploit intelligence of Physarum to manage the sustainability of an enterprise network Expert Syst. (IF 3.3) Pub Date : 2024-03-27 Sami J. Habib, Paulvanna Nayaki Marimuthu
In this work, we enhance the sustainability of an enterprise network (EN) by complementing it with an expert system that apprehends the explore-exploit behavioural intelligence of Physarum to survive against the attractive-adversarial nutritional environment. EN sustainability is dynamic since it depends on how well EN can react to an adversarial environment. We capture a reverse analogy to characterize
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Data‐driven allocation of renewables quota among regional power industries under the policy of renewable electricity standard Expert Syst. (IF 3.3) Pub Date : 2024-03-26 Xiaohong Liu, Chengzhen Xu, Yinghao Pan, Xingchen Li, Qingyuan Zhu
China is struggling to facilitate the application of renewable portfolio standards to realize sustainable economic growth. As such, improving the current distribution mechanism is crucial. In this paper, the context‐dependent data envelopment analysis and multi‐objective linear programming are combined to allocate the renewables quota for each province. This integrated approach can maximize total electricity
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Effects of feature reduction on emotion recognition using EEG signals and machine learning Expert Syst. (IF 3.3) Pub Date : 2024-03-25 Leonardo Trujillo, Daniel E. Hernandez, Adrian Rodriguez, Omar Monroy, Omar Villanueva
Electroencephalography is a core technology of brain computer interfaces. Even a few number of electrodes can produce complex signals that are difficult to interpret. This is particularly true when trying to detect complex mental states, such as the identification of human emotions. This work analyzes the impact of feature reduction using the SJTU Emotion EEG data set, considering inter‐subject and
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Meta heuristic approaches for sentiment analysis Expert Syst. (IF 3.3) Pub Date : 2024-03-25 Meesala Shobha Rani, Sumathy Subramanian
On the Internet, online microblogging and social networks have experienced tremendous growth. Millions of individuals share opinions on social networking platforms including Twitter, Facebook, YouTube, and Microblogging sites. Millions of individuals express their thoughts on social platforms namely Twitter, Facebook, YouTube, and microblogging sites. As user‐generated content is growing to a huge
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The imbalance problem: A comparison of sampling approaches using different parameters and feature selection methods in the context of classification Expert Syst. (IF 3.3) Pub Date : 2024-03-23 Jose L. Morillo‐Salas, Verónica Bolón‐Canedo, Amparo Alonso‐Betanzos
A common situation in classification tasks is to deal with unbalanced datasets, an issue that appears when the majority class(es) has a large number of samples compared to the minority class(es). This problem is even more significant when the datasets have a large number of features but only a few samples, as is the case with microarray datasets. Traditionally, an approach to alleviate this problem
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An analysis to investigate plant disease identification based on machine learning techniques Expert Syst. (IF 3.3) Pub Date : 2024-03-23 Sangeeta Duhan, Preeti Gulia, Nasib Singh Gill, Mohammad Yahya, Sangeeta Yadav, Mohamed M. Hassan, Hassan Alsberi, Piyush Kumar Shukla
In agriculture, crops are severely affected by illnesses, which reduce their production every year. The detection of plant diseases during their initial stages is critical and thus needs to be addressed. Researchers have been making significant progress in the development of automatic plant disease recognition techniques through the utilization of machine learning (ML), image processing, and deep learning
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Integrating singular value decomposition with deep learning for enhanced travel time estimation in multimodal freight transportation networks Expert Syst. (IF 3.3) Pub Date : 2024-03-22 Mohanad R. Aljanabi, Keivan Borna, Shamsollah Ghanbari, Ahmed J. Obaid
Multimodal freight transport allows switching among various modes of transportation to efficiently utilize transport facilities. A multimodal transport system incorporates geographical scales from global to local. Travel time estimation in a multi‐modal cargo transportation network is essential for enhancing supply chain (SC) and logistics operations. Accurate travel time prediction is of great importance
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SAFE: Unsupervised image feature extraction using self‐attention based feature extraction network Expert Syst. (IF 3.3) Pub Date : 2024-03-22 Yeoung Je Choi, Gyeong Taek Lee, Chang Ouk Kim
The ability to extract high‐quality features from data is critical for machine learning applications. With the development of deep learning, various methods have been developed for image feature extraction, and unsupervised techniques have gained popularity due to their ability to operate without response variables. Autoencoders with encoder–decoder architectures are a common example of such techniques
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Probabilistic interval prediction method based on shape‐adaptive quantile regression Expert Syst. (IF 3.3) Pub Date : 2024-03-21 Lin Li, Hua Wang, Yepeng Liu, Fan Zhang
This article introduces customized screening ensemble with shape‐adaptive quantile regression (CseAQR), a novel probabilistic interval forecasting method built upon the quantile regression model. CseAQR utilizes ensemble learning to perform adaptive quantile regression prediction, which can handle the heteroscedasticity feature in time series data by using a weighted adaptive allocation loss function
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Advancing democratic processes in Ecuador: A case study on neural network‐driven OCR for election report verification Expert Syst. (IF 3.3) Pub Date : 2024-03-21 José Alejandro Mosquera Asimbaya, Gabriel M. Ramírez, Jaime Díaz‐Arancibia
BackgroundIn Ecuador, scepticism surrounding electoral outcomes underscores the need for a reliable system to ensure transparent election results. Manual verification demands a more efficient approach due to the vast volume of election reports. This research introduces an automated system leveraging Artificial Intelligence to process results from Ecuador's three recent national elections.MethodsThe
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An efficient multi‐scale transformer for satellite image dehazing Expert Syst. (IF 3.3) Pub Date : 2024-03-20 Lei Yang, Jianzhong Cao, Weining Chen, Hao Wang, Lang He
Given the impressive achievement of convolutional neural networks (CNNs) in grasping image priors from extensive datasets, they have been widely utilized for tasks related to image restoration. Recently, there is been significant progress in another category of neural architectures—Transformers. These models have demonstrated remarkable performance in natural language tasks and higher‐level vision
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AI‐driven IoT‐fog analytics interactive smart system with data protection Expert Syst. (IF 3.3) Pub Date : 2024-03-19 Khalid Haseeb, Tanzila Saba, Amjad Rehman, Naveed Abbas, Pyoung Won Kim
In recent decades, fog computing has contributed significantly to the expansion of smart cities. It generated numerous real‐time data and coped with time‐constraint applications. They use sensors, physical objects, and network standards to monitor health imaging, traffic surveillance, industrial management, and so forth. Interactive applications have been proposed for the Internet of Things (IoT) to
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Artificial intelligence-enabled smart city management using multi-objective optimization strategies Expert Syst. (IF 3.3) Pub Date : 2024-03-15 Pinki, Rakesh Kumar, S. Vimal, Norah Saleh Alghamdi, Gaurav Dhiman, Subbulakshmi Pasupathi, Aarna Sood, Wattana Viriyasitavat, Assadaporn Sapsomboon, Amandeep Kaur
This article outlines an integrated strategy that combines fuzzy multi-objective programming and a multi-criteria decision-making framework to achieve a number of transportation system management-related objectives. To rank fleet cars using various criteria enhancement, the Fuzzy technique for order of preference by resemblance to optimum solution are initially integrated. We then offer a novel Multi-Objective
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Security and privacy protection protocol based on edge computing in smart campus Expert Syst. (IF 3.3) Pub Date : 2024-03-16 Jing Liang, Yan Gong
With the continuous emergence of modern Internet of Things, mobile Internet and other new generation of campus information technology and its extensive application in professional campus education, the development of college professional campus education information construction has entered a new stage of development. In view of higher requirements on data storage and network transmission delay of
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An improved particle swarm optimization algorithm for scheduling tasks in cloud environment Expert Syst. (IF 3.3) Pub Date : 2024-03-12 Zi‐Ren Wang, Xiao‐Xiang Hu, Peng Wei, Bo Yuan
Cloud computing provide services dynamically according to the contract between service providers and users. However, Inappropriateness of scheduling task on VMs can lead huge resource waste and load unbalance, which becomes a seriously challenging problem. Current Swarm intelligence algorithms like genetic algorithm (GA), particle swarm optimization (PSO) are combination of random initialization and
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AI unveiled personalities: Profiling optimistic and pessimistic attitudes in Hindi dataset using transformer‐based models Expert Syst. (IF 3.3) Pub Date : 2024-03-12 Dipika Jain, Akshi Kumar
Both optimism and pessimism are intricately intertwined with an individual's inherent personality traits and people of all personality types can exhibit a wide range of attitudes and behaviours, including levels of optimism and pessimism. This paper undertakes a comprehensive analysis of optimistic and pessimistic tendencies present within Hindi textual data, employing transformer‐based models. The
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Enhancing COVID-19 misinformation detection through novel attention mechanisms in NLP Expert Syst. (IF 3.3) Pub Date : 2024-02-26 Anbar Hussain, Wajid Ali, Awais Ahmad, Muhammad Shahid Iqbal, Syed Atif Moqurrab, Anand Paul, Sohail Jabbar, Sheeraz Akram
The rapid evolution of electronic media in recent decades has exponentially amplified the propagation of fake news, resulting in widespread confusion and misunderstanding among the masses, especially concerning critical topics like the COVID-19 pandemic. Consequently, detecting fake news on social media has emerged as a prominent area of research, attracting significant attention. This article introduces
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Hate speech detection: A comprehensive review of recent works Expert Syst. (IF 3.3) Pub Date : 2024-02-25 Ankita Gandhi, Param Ahir, Kinjal Adhvaryu, Pooja Shah, Ritika Lohiya, Erik Cambria, Soujanya Poria, Amir Hussain
There has been surge in the usage of Internet as well as social media platforms which has led to rise in online hate speech targeted on individual or group. In the recent years, hate speech has resulted in one of the challenging problems that can unfurl at a fast pace on digital platforms leading to various issues such as prejudice, violence and even genocide. Considering the acceptance of Artificial
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Fuzzy logic‐based trusted routing protocol using vehicular cloud networks for smart cities Expert Syst. (IF 3.3) Pub Date : 2024-02-27 Ramesh Kait, Sarbjit Kaur, Purushottam Sharma, Chhikara Ankita, Tajinder Kumar, Xiaochun Cheng
Due to the characteristics of vehicular ad hoc networks, the increased mobility of nodes and the inconsistency of wireless communication connections pose significant challenges for routing. As a result, researchers find it to be a fascinating topic to study. Furthermore, since these networks are vulnerable to various assaults, providing an authentication method between the source and destination nodes
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An enhanced method for surface defect detection in workpieces based on improved MobileNetV2‐SSD Expert Syst. (IF 3.3) Pub Date : 2024-02-27 Junlin Qiu, Yongshan Shen, Jianchu Lin, Yuxin Qin, Jian Yang, Hengdan Lei, Minghui Li
In the process of workpieces production, surface defects are prone to occur, and these defects come in a wide variety and are often intermixed, making defect detection and classification exceptionally challenging. With the development of artificial intelligence and deep learning, to tackle this problem, this paper introduces an enhanced single shot multibox detector algorithm based on MobileNetV2 for
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Leveraging quantum‐inspired chimp optimization and deep neural networks for enhanced profit forecasting in financial accounting systems Expert Syst. (IF 3.3) Pub Date : 2024-02-27 Lin Zhang, Shtwai Alsubai, Abdullah Alqahtani, Abed Alanazi, Laith Abualigah
Deep learning and metaheuristic algorithms have recently increased in various sciences, including financial accounting information systems (FAISs). However, the existence of large datasets has dramatically increased the complexity of these hybrid networks, so to address this shortcoming, this paper aims to develop a quantum‐behaved chimp optimization algorithm (QCHOA) and deep neural network (DNN)
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A review of deep learning-based approaches for deepfake content detection Expert Syst. (IF 3.3) Pub Date : 2024-02-22 Leandro A. Passos, Danilo Jodas, Kelton A. P. Costa, Luis A. Souza Júnior, Douglas Rodrigues, Javier Del Ser, David Camacho, João Paulo Papa
Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's integrity and can lead to social instability. To address this issue, there is a pressing need to develop new computational models that can efficiently detect forged content and alert users to potential image and video manipulations
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Backtranslate what you are saying and I will tell who you are Expert Syst. (IF 3.3) Pub Date : 2024-02-21 Marco Siino, Francesco Lomonaco, Paolo Rosso
With this work, we hypothesize that semantically enriching a user's text corpus using backtranslation and expansion modules can improve performance for author profiling tasks. To perform this textual enrichment, we translate an author's representative text. Translations are made from one language—the source language—into another—the target language—and then back to the original one. Finally, we expand
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A novel generative adversarial network‐based super‐resolution approach for face recognition Expert Syst. (IF 3.3) Pub Date : 2024-02-21 Amit Chougule, Shreyas Kolte, Vinay Chamola, Amir Hussain
Face recognition is an essential feature required for a range of computer vision applications such as security, attendance systems, emotion detection, airport check‐in, and many others. The super‐resolution of subject images is an important and challenging element in numerous scenarios. At times the images are low resolution and need to be processed through super‐resolution techniques to gain more
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Airport take‐off and landing optimization through genetic algorithms Expert Syst. (IF 3.3) Pub Date : 2024-02-20 Fernando Guedan‐Pecker, Cristian Ramirez‐Atencia
This research addresses the crucial issue of pollution from aircraft operations, focusing on optimizing both gate allocation and runway scheduling simultaneously, a novel approach not previously explored. The study presents an innovative genetic algorithm‐based method for minimizing pollution from fuel combustion during aircraft take‐off and landing at airports. This algorithm uniquely integrates the
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Special issue on computing, intelligence and data analytics for wisdom (CIDA4Wisdom) Expert Syst. (IF 3.3) Pub Date : 2024-02-19 Süleyman Eken, Serdar Solak, M. Hikmet Bilgehan Uçar, Zeynep Hilal Kilimci, Akhtar Jamil, Alaa Ali Hameed, Fausto Pedro Garcia Marquez
Data wisdom is the ability to think critically about data and create data-based judgments by combining domain, mathematical, and methodological expertise with experience, comprehension, common sense, insight, and sound judgment. This special issue focuses on data wisdom which is a mix of mathematical, scientific, and humanistic abilities and combines science with art. In this special issue, we invited
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A novel hybrid CNN methodology for automated leaf disease detection and classification Expert Syst. (IF 3.3) Pub Date : 2024-02-11 Prabhjot Kaur, Anand Muni Mishra, Nitin Goyal, Sachin Kumar Gupta, Achyut Shankar, Wattana Viriyasitavat
Plant leaf diseases are challenging to categorize due to the complexity of the pattern variations and the high degrees of inter-class similarity. Plant ailments harm food quality and production. To ensure the quality and quantity of harvests, it is essential to protect plants from disease. Detection of diseases at an early stage is the main and the most complex task for farmers due to common morphological
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Classification of intrusion cyber-attacks in smart power grids using deep ensemble learning with metaheuristic-based optimization Expert Syst. (IF 3.3) Pub Date : 2024-02-12 Hamad Naeem, Farhan Ullah, Gautam Srivastava
The most advanced power grid design, known as a ‘smart power grid’, integrates information and communication technology (ICT) with a conventional grid system to enable remote management of electricity distribution. The intelligent cyber-physical architecture enables bidirectional, real-time data sharing between electricity suppliers and consumers through smart meters and advanced metering infrastructure
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Preventing and mitigating risks of rumours during major pandemics in the era of artificial intelligence: A perspective on vulnerability Expert Syst. (IF 3.3) Pub Date : 2024-02-07 Yuhuan Kong
The sudden outbreak of a major pandemic often leads to the widespread dissemination of rumours related to the event. The public serves as both disseminators and regulators of rumours. Enhancing the public's capability to defend against rumours and strengthening their resilience are crucial for turning the tide of the pandemic. This study focuses on the rumours surrounding the COVID-19 event and explores
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Stakeholder perception towards a machine-learning-based digital platform for detection and management of autism spectrum disorder Expert Syst. (IF 3.3) Pub Date : 2024-02-07 Manu Kohli, Arpan Kumar Kar, Shuchi Sinha, Swati Kohli
Autism spectrum disorder (ASD) affects approximately 1% of the population, presenting a significant healthcare challenge due to limited resources, particularly a shortage of clinicians, which impedes timely ASD detection and management in children. This study investigates stakeholder viewpoints regarding the effectiveness of integrating machine learning (ML) into the information and communications
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A new frontier in dashboard design: Evaluating an innovative meta-modelling approach through expert insights Expert Syst. (IF 3.3) Pub Date : 2024-02-05 Andrea Vázquez-Ingelmo, Alicia García-Holgado, Francisco José García-Peñalvo, Roberto Therón, Ricardo Colomo-Palacios
Data visualizations and dashboards are essential in disseminating information to broad audiences. However, designing these instruments is complex due to the multitude of involved factors. Theoretical frameworks are therefore critical to guide the design and implementation of data visualizations.
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Enhanced slime mould optimization with convolutional BLSTM autoencoder based malware classification in intelligent systems Expert Syst. (IF 3.3) Pub Date : 2024-02-04 Shtwai Alsubai, Ashit Kumar Dutta, Abdul Rahaman Wahab Sait, Yasser Adnan Abu Jaish, Bader Hussain Alamer, Hussam Eldin Hussein Saad, Rashid Ayub
Autonomous intelligent systems are artificial intelligence (AI) tools that act autonomously without direct human supervision. Cloud computing (CC) and Internet of Things (IoT) technologies find it challenging to deploy sufficient security defences because of the different structures, storage, and limited computing capabilities that make them more vulnerable to attacks. Security threats against IoT
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O-SCDR: Optimal cluster with attention based shared-account cross-domain sequential recommendation using deep reinforcement learning technique Expert Syst. (IF 3.3) Pub Date : 2024-02-03 M. Nanthini, K. Pradeep Mohan Kumar
Sequential recommendation involves suggesting subsequent items in a series of user activities. When recommending relevant items to users within the same account, the challenge lies in discerning diverse user behaviours to provide tailored recommendations based on individual preferences and timing. Cross-domain sequential recommendation (CDSR) focuses on accurately extracting cross-domain user preferences
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Evolution of confrontation and cooperation in simple organisms as a function of environmental resources and cost of a conflict Expert Syst. (IF 3.3) Pub Date : 2024-02-02 Philippe Chassy, Jon Cole, Chloe Brennan
The root cause of human conflict needs to be understood but it is currently unknown whether the decision to engage in conflict is an inherited or acquired trait. This article reports two experimental simulations which demonstrate that the level of confrontation in a population of simple organisms can be explained by the evolution of a simulated gene pool. Game theory and evolutionary algorithms were
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A multi-label social short text classification method based on contrastive learning and improved ml-KNN Expert Syst. (IF 3.3) Pub Date : 2024-02-02 Gang Tian, Jiachang Wang, Rui Wang, Guangxin Zhao, Cheng He
Short texts on social platforms often have the problems of diverse categories and semantic sparsity, making it challenging to identify the diverse intentions of users. To address this issue, this article proposes a multi-label social short text classification method (IML-CL) based on contrastive learning and improved ml-KNN. First, a contrastive learning approach is employed to train a multi-label
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Artificial intelligence for the practical assessment of nutritional status in emergencies Expert Syst. (IF 3.3) Pub Date : 2024-02-01 Ben Watkins, Lameck Odallo, Jenny Yu
This paper describes a novel method for detecting child malnutrition based on artificial intelligence and facial photography. Estimates of severe and moderate acute malnutrition in children are critical for rapid emergency responses. However, the two traditional measurement methods, mid-upper arm circumference (MUAC) and weight-for-height (WFH), are impractical in conflict and catastrophic disaster
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Spatial coordination and industrial pollution of urban agglomerations: Evidence from the Yellow River Basin in China Expert Syst. (IF 3.3) Pub Date : 2024-02-01 Chao Hua, Zhenhua Zhang, Jianjun Miao, Jingwei Han, Zhiyuan Zhu
Population, economy, and other social factors of urban agglomerations in river basins bear the inescapable responsibility for water environmental pollution. This article utilizes data from 53 cities in three regional-level urban agglomerations in the Yellow River Basin (YRB) from 2006 to 2015 as research samples to analyse the impact of the coupling and coordination of physical space expansion (PSE)
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Toxic language detection: A systematic review of Arabic datasets Expert Syst. (IF 3.3) Pub Date : 2024-01-30 Imene Bensalem, Paolo Rosso, Hanane Zitouni
The detection of toxic language in the Arabic language has emerged as an active area of research in recent years, and reviewing the existing datasets employed for training the developed solutions has become a pressing need. This paper offers a comprehensive survey of Arabic datasets focused on online toxic language. We systematically gathered a total of 54 available datasets and their corresponding
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Improved cooperative Ant Colony Optimization for the solution of binary combinatorial optimization applications Expert Syst. (IF 3.3) Pub Date : 2024-01-29 Roberto Prado-Rodríguez, Patricia González, Julio R. Banga, Ramón Doallo
Binary combinatorial optimization plays a crucial role in various scientific and engineering fields. While deterministic approaches have traditionally been used to solve these problems, stochastic methods, particularly metaheuristics, have gained popularity in recent years for efficiently handling large problem instances. Ant Colony Optimization (ACO) is among the most successful metaheuristics and
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Enhanced text classification through an improved discrete laying chicken algorithm Expert Syst. (IF 3.3) Pub Date : 2024-01-25 Fatemeh Daneshfar, Mohammad Javad Aghajani
The exponential growth of digital text documents presents a significant challenge for text classification algorithms, as the vast number of words in each document can hinder their efficiency. Feature selection (FS) is a crucial technique that aims to eliminate irrelevant features and enhance classification accuracy. In this study, we propose an improved version of the discrete laying chicken algorithm
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Cloud-based deep learning architecture for DDoS cyber attack prediction Expert Syst. (IF 3.3) Pub Date : 2024-01-23 Jeferson Arango-López, Gustavo Isaza, Fabian Ramirez, Nestor Duque, Jose Montes
Conventional methodologies employed in detecting distributed denial-of-service attacks have frequently struggled to adapt to the dynamic and multi-faceted evolution of such threats. Furthermore, many of the contemporary detection and prevention solutions, while innovative, remain anchored to dedicated workstations, lacking the flexibility and scalability required in today's digital landscape. To bridge
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IETAFusion: An illumination enhancement and target-aware infrared and visible image fusion network for security system of smart city Expert Syst. (IF 3.3) Pub Date : 2024-01-19 Shuang Guo, Kun Wu, Seunggil Jeon, Xiaomin Yang
In the environmental security monitoring of smart cities, the infrared and visible image fusion method deployed on intelligent systems based on cloud and fog computing plays a vital role in providing enhanced images for target detection systems. However, the fusion quality can be significantly influenced by the illumination of the monitoring scenario in visible images. Therefore, conventional methods
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A smart decentralized identifiable distributed ledger technology-based blockchain (DIDLT-BC) model for cloud-IoT security Expert Syst. (IF 3.3) Pub Date : 2024-01-19 Shitharth Selvarajan, Achyut Shankar, Mueen Uddin, Abdullah Saleh Alqahtani, Taher Al-Shehari, Wattana Viriyasitavat
The most important and difficult challenge the digital society has recently faced is ensuring data privacy and security in cloud-based Internet of Things (IoT) technologies. As a result, many researchers believe that the blockchain's Distributed Ledger Technology (DLT) is a good choice for various clever applications. Nevertheless, it encountered constraints and difficulties with elevated computing
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An efficient hybrid extreme learning machine and evolutionary framework with applications for medical diagnosis Expert Syst. (IF 3.3) Pub Date : 2024-01-22 Ali Al Bataineh, Seyed Mohammad Jafar Jalali, Seyed Jalaleddin Mousavirad, Amirmehdi Yazdani, Syed Mohammed Shamsul Islam, Abbas Khosravi
Integrating machine learning techniques into medical diagnostic systems holds great promise for enhancing disease identification and treatment. Among the various options for training such systems, the extreme learning machine (ELM) stands out due to its rapid learning capability and computational efficiency. However, the random selection of input weights and hidden neuron biases in the ELM can lead
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Enhancing diversity for logical table-to-text generation with mixture of experts Expert Syst. (IF 3.3) Pub Date : 2024-01-18 Jie Wu, Mengshu Hou
Logical table-to-text generation is a task within the realm of natural language generation (NLG) that aims to generate coherent and logically faithful sentences based on tables. Unlike conventional NLG tasks, this task demands not only surface-level fluency but also a high degree of logic-level fidelity in the generated outputs. Current table-to-text systems grapple with various quality issues, such
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An augmented fuzzy decision support system to analyse compatible cosmetic face masks for various complexions Expert Syst. (IF 3.3) Pub Date : 2024-01-16 Joseph Raj Vikilal Joice Brainy, Samayan Narayanamoorthy, Samayan Kalaiselvan, Ranganathan Saraswathy, Ali Ahmadian, Norazak Senu, Jeonghwan Jeon
Beauty face masks (BFM) are becoming increasingly popular among both men and women since they provide quick refreshment and nurture the skin. Given the wide range of skin types and the chemicals used in their formulation, it can be difficult to find a product that not only complements the skin type but is also free of potentially harmful ingredients that could endanger the consumer's health. When dealing
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An AI knowledge-based system for police assistance in crime investigation Expert Syst. (IF 3.3) Pub Date : 2024-01-09 Carlos Fernandez-Basso, Karel Gutiérrez-Batista, Juan Gómez-Romero, M. Dolores Ruiz, Maria J. Martin-Bautista
The fight against crime is often an arduous task overall when huge amounts of data have to be inspected, as is currently the case when it comes for example in the detection of criminal activity on the dark web. This work presents and describes an artificial intelligence (AI) based system that combines various tools to assist police or law enforcement agencies during their investigations, or at least
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Detecting cardiovascular diseases from radiographic images using deep learning techniques Expert Syst. (IF 3.3) Pub Date : 2024-01-12 Majed Alsanea, Ashit Kumar Dutta
Cardiovascular disease (CD) is one of the leading causes of death and disability across the globe. Chest x-rays (CXR) are crucial in detecting chest and CD. The CXR images present helpful information to the radiologist to identify a disease at an earlier stage. Several convolutional neural network (CNN) models for classifying the CXR images have been established. However, there is a demand for significant
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Sarcasm-based tweet-level stress detection Expert Syst. (IF 3.3) Pub Date : 2024-01-10 KVTKN Prashanth, Tene Ramakrishnudu
Psychological stress has evolved as an important health concern across the globe. The vulnerability to stress and the ramifications of it have only worsened during the time of the COVID-19 pandemic. This necessitates a timely diagnosis of stress before the condition progresses to chronicity. In this context, the popularity of social media like Twitter, where large numbers of users share opinions without
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Deep convolutional neural networks information fusion and improved whale optimization algorithm based smart oral squamous cell carcinoma classification framework using histopathological images Expert Syst. (IF 3.3) Pub Date : 2024-01-09 Momina Meer, Muhammad Attique Khan, Kiran Jabeen, Ahmed Ibrahim Alzahrani, Nasser Alalwan, Mohammad Shabaz, Faheem Khan
The most prevalent type of cancer worldwide is mouth cancer. Around 2.5% of deaths are reported annually due to oral cancer in 2023. Early diagnosis of oral squamous cell carcinoma (OSCC), a prevalent oral cavity cancer, is essential for treating and recovering patients. A few computerized techniques exist but are focused on traditional machine learning methods, such as handcrafted features. In this
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Reduction of financial tick big data for intraday trading Expert Syst. (IF 3.3) Pub Date : 2024-01-09 Vitaliy Milke, Cristina Luca, George B. Wilson
Various neural network architectures are often used to forecast movements in financial markets. Most research in quantitative analytics in finance uses interval financial data as this reduces the raw tick big data, but the averaging can lose key behaviour patterns. This work presents an alternative novel method to reduce raw tick data whilst retaining important information for training, as demonstrated
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Empowering EEG motor imagery classification with deep transfer learning approach Expert Syst. (IF 3.3) Pub Date : 2024-01-09 Awanish Kumar Mishra, Indresh Kumar Gupta, Swati Srivastava, Sultan Alfarhood, Mejdl Safran
The brain-computer interface (BCI) enables individuals with impairments to interact with the real world without relying on the neuromuscular pathway. BCI leverages artificial intelligence (AI) models for control. It can capture brain activity patterns associated with mental processes and convert them into commands for actuators. One promising application of BCI is in rehabilitation centres. When compared
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Artificial intelligence for heart sound classification: A review Expert Syst. (IF 3.3) Pub Date : 2024-01-08 Junxin Chen, Zhihuan Guo, Xu Xu, Gwanggil Jeon, David Camacho
Heart sound signal analysis is very important for the early identification and treatment of cardiovascular illness. With rapid advancements in science and technology, artificial intelligence technologies are providing tremendous opportunities to enhance diagnosis and clinical decision-making. Instruments can now perform clinical diagnoses that previously could only be handled by human experts more
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Hourly load prediction based feature selection scheme and hybrid CNN-LSTM method for building's smart solar microgrid Expert Syst. (IF 3.3) Pub Date : 2024-01-04 Thao Nguyen Da, Ming-Yuan Cho, Phuong Nguyen Thanh
The short-term load prediction is the critical operation in the peak demand administration and power generation scheduling of buildings that integrated the smart solar microgrid (SSM). Many research studies have proved that hybrid deep learning strategies achieve more accuracy and feasibility in practical applications than individual algorithms. Moreover, many buildings have integrated the SSM on the
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Judicial intelligent assistant system: Extracting events from Chinese divorce cases to detect disputes for the judge Expert Syst. (IF 3.3) Pub Date : 2024-01-04 Yuan Zhang, Chuanyi Li, Yu Sheng, Jidong Ge, Bin Luo
In the formal procedure of Chinese civil cases, the textual materials provided by different parties describe the development process of the cases. It is a difficult but necessary task to extract the key information for the cases from these textual materials and to clarify the dispute focus of related parties. Currently, officers read the materials manually and use methods, such as keyword searching
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A new computer-aided diagnostic method for classifying anaemia disease: Hybrid use of Tree Bagger and metaheuristics Expert Syst. (IF 3.3) Pub Date : 2023-12-28 Nagihan Yagmur, Idiris Dag, Hasan Temurtas
Anaemia occurs when the haemoglobin (Hgb) value falls below a certain reference range. It requires many blood tests, radiological images, and tests for diagnosis and treatment. By processing medical data from patients with artificial intelligence and machine learning methods, disease predictions can be made for newly ill individuals and decision-support mechanisms can be created for physicians with
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Expert systems supporting strategic decisions Expert Syst. (IF 3.3) Pub Date : 2023-12-27 Maria José Sousa, Álvaro Rocha
Extensive study in fields like expert systems and unforeseen breakthroughs that have an impact on our regular activities are changing the world as we know. Researchers continuously develop novel, cutting-edge concepts, and paradigms as well as outstanding technologies that speedily mature and are released onto the market with applications using machine learning, pervasive social gaming, forecasting
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Identifying professional photographers through image quality and aesthetics in Flickr Expert Syst. (IF 3.3) Pub Date : 2023-12-27 Sofia Strukova, Rubén Gaspar Marco, Félix Gómez Mármol, José A. Ruipérez-Valiente
In our generation, there is an undoubted rise in the use of social media and specifically photo and video sharing platforms. These sites have proved their ability to yield rich data sets through the users’ interaction which can be used to perform a data-driven evaluation of capabilities. Nevertheless, this study reveals the lack of suitable data sets in photo and video sharing platforms and evaluation