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A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

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Archives of Computational Methods in engineering 2025年第1期32卷 269-310页

作者： Hosseinzadeh, Mehdi Rahmani, Amir Masoud Husari, Fatimatelbatoul Mahmoud Alsalami, Omar Mutab Marzougui, Mehrez Nguyen, Gia Nhu Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia School of Computer Science Duy Tan University Da Nang Viet Nam Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2024.

关键词： Optimization

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Unveiling cognitive processes in digital reading through behavioural cues: A hybrid intelligence (HI) approach

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British Journal of Educational Technology 2025年第2期56卷 678-711页

作者： Lee, Yoon Migut, Gosia Specht, Marcus Industrial Design Graduate School of Design University of Seoul Seoul Korea Republic of Research Lab Faculty of Education and Psychology University of Oulu Oulu Finland Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

Learner behaviours often provide critical clues about learners' cognitive processes. However, the capacity of human intelligence to comprehend and intervene in learners' cognitive processes is often constrained by the subjective nature of human evaluation and the challenges of maintaining consistency and scalability. The recent widespread AI technology has been applied to learning analytics (LA), aiming at a more accurate, consistent and scalable understanding of learning to compensate for challenges that human intelligence faces. However, machine intelligence has been criticized for lacking contextual understanding and difficulties dealing with complex human emotions and social cues. In this work, we aim to understand learners' internal cognitive processes based on the external behavioural cues of learners in a digital reading context, using a hybrid intelligence (HI) approach, bridging human and machine intelligence. Based on the behavioural frameworks and the insights from human experts, we scope specific behavioural cues that are known to be relevant to learners' attention regulation, which is highly relevant for learners' cognitive processes. We utilize the public WEDAR dataset with 30 subjects' video data, behaviour annotation and pre–post tests on multiple choice and summarization tasks. We apply the explainable AI (XAI) approach to train the machine learning model so that human evaluators can also understand which behavioural features were essential for predicting the usage of the cognitive processes (ie, higher-order thinking skills [HOTS] and lower-order thinking skills [LOTS]) of learners, providing insights for the next-round feature engineering and intervention design. The result indicates that the dominant use of attention regulation behaviours is a reliable indicator of low use of LOTS with 79.33% prediction accuracy, while reading speed is a valuable indicator for predicting the overall usage of HOTS and LOTS, ranging from 60.66% to 78.66% accuracy,

关键词： Contrastive Learning

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Scalability Assessment of EVM-Compatible Blockchains for E-Voting 6th

Scalability Assessment of EVM-Compatible Blockchains for E...

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6th International Congress on Blockchain and Applications, BLOCKCHAIN 2024

作者： Hajian Berenjestanaki, Mohammad Barzegar, Hamid R El Ioini, Nabil Pahl, Claus Faculty of Engineering Free University of Bozen-Bolzano Bolzano Italy School of Computer Science University of Nottingham Malaysia Selangor Malaysia

ISBN: (纸本)9783031819278

The emergence of blockchain technology is transforming various aspects, including electronic voting (e-voting) systems, which have become increasingly important due to the need for transparency, accessibility, and security in voting processes. This study explores the suitability of four Ethereum Virtual Machine (EVM) compatible blockchain platforms—Fantom, Arbitrum One, Avalanche, and Polygon zkEVM—for implementing e-voting systems by evaluating their scalability metrics, including cost, throughput, efficiency, and delay. Fantom performs well in transaction cost and efficiency, similar to Arbitrum One in terms of scalability, offering low delay and high throughput. Avalanche consensus and architecture promise high throughput. Polygon zkEVM zk-Rollups offer scalability with growing infrastructure. The optimal choice of blockchain platform for e-voting systems relies on carefully evaluating the specific scalability requirements of the election scenario. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Blockchain

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Towards a Green Digital Currency for Smart Communities: An AI-Powered Ecosystem for Citizen Green Stewardship 14

Towards a Green Digital Currency for Smart Communities: An A...

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14th International Conference on Smart Cities and Green ICT Systems, SMARTGREENS 2025

作者： Kerkad, Amira Gouri, Rabah LSI-Computer Science Faculty USTHB Algiers Algeria IECPS Lab Department of Industrial Engineering & Maintenance ENSTA Algiers Algeria

ISBN: (纸本)9789897587511

This paper proposes a new ecosystem vision designed to measure and incentivize citizen and corporate engagement in environmental stewardship through circular economy (CE). The ecosystem uses a gamified platform where participants earn digital points for performing two types of operations: (1) recycling matters and (2) reusing items, within smart communities. We propose to consider these points as a new global digital currency redeemable for rewards and services offered by participating businesses worldwide. The businesses are mainly the polluters that aim to have green advertisement and engagement in the "polluter pays" principle. Consequently, these businesses will benefit simultaneously from green advertising and tax reductions. A suite of digital services and AI-powered tools are used to optimize operations and generate valuable data for improving both urban and rural sustainability initiatives. By making this new currency, we can assess environmental engagement, motivate citizens to adhere into CE, and impose hierarchical participation in sustainable practices. This paper outlines the ecosystem's components, discusses the feasibility of our proposal, and explores its potential benefits and challenges for various stakeholders. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Competitive intelligence

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Specification of Complex Analytics Workflows: A Formal Language Model of Decision Options 20th

Specification of Complex Analytics Workflows: A Formal Lang...

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20th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Miri, Pouriya Kochovski, Petar Tuler de Oliveira, Marcela Stankovski, Vlado Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Department of Engineering Systems Delft University of Technology Delft Netherlands

ISBN: (纸本)9783031812255

The specification of experiments expressed as Complex Analytics Workflows is a complex task that involves many decision-making steps with various degrees of complexity. The use of the context, the expert knowledge, and the potential for its sharing and reuse in the context of experiment specification have not been addressed sufficiently until now. Moreover, to make such knowledge instrumental, it should be coupled with specific probabilistic measures, such as particular assurances, ranking, and verification of various options. The paper aims to present a novel semantic model for probabilistic reasoning in any experimentation context coupled with a functional system for knowledge generation, reuse, and sharing. The result of our work can be used within existing experimentation engines. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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Utilizing faculty computers for Cryptocurrency Validation and Mining During Idle Time 33rd

Utilizing Faculty Computers for Cryptocurrency Validation an...

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33rd International Conference on Organization and Technology of Maintenance, OTO 2024

作者： Švarcmajer, Miljenko Köhler, Mirko Marinčić, Juraj Perić, Juraj Faculty of Electrical Engineering Josip Juraj Strossmayer University of Osijek Computer Science and Information Technology Osijek Osijek31000 Croatia

ISBN: (纸本)9783031805967

In the world of cryptocurrencies, there is the possibility of participating in numerous processes in which some kind of reward is obtained, most often in the form of cryptocurrency. This paper investigates the possibilities of using laboratory computers at the faculty of Electrical engineering, Computing and Information Technologies in these processes when they are not used for teaching purposes. The hardware specifications of the computer were collected and an insight into the ways of cryptocurrency earnings mining and validation was provided. Mining is the most well-known earning process, and it was introduced with the Bitcoin cryptocurrency, and based on this cryptocurrency, this process is described in detail. Due to the hardware configuration of the computer, in this paper we focus on the profitability of CPU mining of cryptocurrencies that can be mined this way, namely Monero, Zephyr, Raptoreum, Skytere and Subi Network. This type of mining has been unprofitable for Bitcoin for years. Computing power does not have to be used only for the mining process. For example, Solana uses CPUs to validate transactions. This paper combines technical and economic analyses, taking into account computer specifications to assess the economic viability of cryptocurrency mining and validation on college computers. We highlight the importance of organization, maintenance, and economic factors in managing these processes, providing practical guidelines for optimal utilization of faculty resources for the purpose of cryptocurrency earnings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Earnings

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Performance Enhancement in Terms of BER and Data Rate of Next Generation Optical Networking System using Raman Amplifier 3

Performance Enhancement in Terms of BER and Data Rate of Nex...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Khant, Shailesh Makwana, Mayur Patel, Atul Patel, Dip Faculty of Computer Science and Applications Charusat Changa Anand India Faculty of Electronics and Communication Engineering Charusat Changa Anand India

ISBN: (纸本)9798331508456

This study explores the integration of Raman optical amplifiers in a Wavelength Division Multiplexing Passive Optical Network (WDM-PON) system to enhance high-speed data transmission. Traditional amplifiers like Erbium doped fiber amplifier (EDFA) and Semiconductor amplifier (SOA) face limitations in noise performance and gain efficiency over long distances. To address these challenges, a four-channel WDM-PON system was designed and simulated over a 50 km fiber link. Performance metrics, including Bit Error Rate (BER), Quality Factor (Q-Factor), and eye diagram characteristics, were analyzed. The results demonstrate that Raman amplification significantly reduces BER, improves Q-Factor, and enhances signal integrity, making it a robust solution for long-reach optical networks. This study contributes to the advancement of optical communication by showcasing the effectiveness of Raman amplifiers in supporting high-capacity, high-speed transmission. The main findings are Raman amplification is used for improving signal quality and network efficiency in next-generation optical systems and is better compared to other optical amplifiers. © 2025 IEEE.

关键词： Erbium doped fiber amplifiers

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Enhancing Data Aggregation in Cluster-Based Wireless Sensor Networks with LCSS: Longest Common Subsequence Empowerment 8th

Enhancing Data Aggregation in Cluster-Based Wireless Sensor ...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Jamil, Muhammad Farooq, Umer Tariq, Noshina Alwakid, Ghadah Humayun, Mamoona Khan, Nadia Department of Computer Science Abdul Wali Khan University Mardan Pakistan Department of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan Department of Avionics Engineering Air University Islamabad Pakistan Department of Computer Science College of Computer and Information Sciences Jouf University Sakaka Al-Jouf Saudi Arabia Department of Information Systems College of Computer and Information Sciences Jouf University Sakaka Al-Jouf Saudi Arabia Institute of Computer Sciences and Information Technology Faculty of Management and Computer Sciences The University of Agriculture Peshawar Pakistan

ISBN: (纸本)9789819793266

A wireless sensor network (WSN) is a system of interconnected sensors that can gather environmental information. On the other hand, data redundancy is a common source of problems with WSNs. The literature presents a plethora of approaches. Despite these mechanisms’ best efforts, most current systems still need help with difficulties like noise, redundancy, or application specificity. Thus, we proposed an algorithm to overcome all the problems with redundant and highly correlated data. The longest common subsequence (LCSS)-enabled sliding window algorithm can resolve issues in constrained networks related to energy conservation and congestion control. The proposed method exhibits remarkable performance on multiple benchmark datasets. According to the result analysis, the suggested technique can outperform its equivalents for a range of data sets by about 32%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Congestion control (communication)

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Diabetes Prediction Using Hybrid 1D CNN and PeeHole Long Short-Term Memory Algorithm 5

Diabetes Prediction Using Hybrid 1D CNN and PeeHole Long Sho...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Behera, Mandakini Mishra, Debahuti Sarangi, Archana Sarangi, Shubhendu Deemed to be University School of Computer Engineering Bhubaneswar India University Department of Computer Science & Engineering Bhubaneswar India University Department of Electronics and Telecommunications Bhubaneswar India University Faculty of Engineering Bhubaneswar India

ISBN: (纸本)9798331522100

According to the survey of International Diabetes Federation (IDF) the number of persons with diabetes is continuously increasing. There are currently 3820 million diabetics worldwide, and during the next 15 years, this figure might double. Diabetes can create several life-threatening diseases, including heart attack, renal failure, and stroke. To sustain a healthy life, early diabetes identification is crucial. machine learning (ML) is a approach which automatically learns from experience to improve performance and produce more accurate predictions, that can be used to forecast diabetes. Deep learning is a branch of machine learning, and the human brain inspires both its architecture and the way it *** manuscript presents a hybrid method that combines peephole LSTM and 1D CNN to improve the accuracy of diabetes prediction. Performance is evaluated based on F1 score, accuracy, specificity, sensitivity, and epoch-wise accuracy and *** Pima Indian diabetes dataset is utilized for experimental validation. Experimental results demonstrate high prediction accuracy of 96.74% on training and 75.32% on testing for diabetes prediction. © 2025 IEEE.

关键词： Deep learning

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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

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Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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