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RedTops: real-time energy-aware dynamic task offloading via federated mountain gazelle optimisation in SDN-enhanced edge computing

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Neural Computing and Applications 2025年 1-39页

作者： Al Aghbari, Zaher Khedr, Ahmed M. Ahmed, Naveed Girija, Shini Baker, Thar Department of Computer Science University of Sharjah Sharjah United Arab Emirates School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Offloading computational tasks is vital for real-time applications on mobile devices with limited resources. Mobile edge computing (MEC) is deemed a solution that puts computational resources closer to users. Nevertheless, there are many associated concerns during the offloading procedure (i.e., privacy, delay, and high energy consumption). Federated learning (FL) has been considered a solution to address MEC’s data privacy issues;however, it comes with its own resource consumption issues. To address these issues, this paper proposes a distributed learning paradigm inspired by FL. We propose an optimisation technique for offloading computational tasks that aims to reduce both total delay and energy consumption by using the mountain gazelle optimisation algorithm, which shows it can reduce both delay and energy consumption in dynamic situations. Additionally, an improved variant known as the improved mountain gazelle optimiser is integrated into a distributed SDN controller architecture to create an offloading policy model for optimal edge node selection. We also present a new SDN-enabled edge computing architecture that achieves the best task distribution through task offloading using federated mountain gazelle optimisation (RedTops). Energy usage, delay, and bandwidth are considered by RedTops, which successfully addresses high training costs, dependability issues, and privacy concerns in MEC. Based on the outcomes of five extensive simulations, RedTops is more energy-efficient and faster at completing tasks than four state-of-the-art offloading methods (DDLO, DROO, DRL without TL and SDN, and DTRL). © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Federated learning

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Zero-day Android botnet detection using neural networks

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Neural Computing and Applications 2025年第17期37卷 10795-10805页

作者： Seraj, Saeed Pimenidis, Elias Trovati, Marcello Polatidis, Nikolaos Department of Computer Science Royal Holloway University EghamTW20 0EX United Kingdom School of Computing and Creative Technologies University of the West of England BristolBS16 1QY United Kingdom Department of Computer Science Edge Hill University OrmskirkL39 4QP United Kingdom School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom

Android devices have evolved to offer a diverse array of services, spanning applications related to banking, business, health, and entertainment. The widespread adoption of Android devices, coupled with the open-source architecture of the Android operating system, has rendered them a prime target for malicious actors. Among the most perilous threats are Android botnets, which enable malicious actors, often referred to as botmasters, to exert remote control for the execution of destructive attacks. Android botnets have huge potential to be an emerging threat to mobile device security. In this paper, we focus on detecting evolving Android botnets and introduce a new dataset of 3458 apps, represented by 455 permission-based features. We propose an improved multilayer perceptron neural network for zero-day botnet detection. Our methodology, in this way, achieves an accuracy of 98.5%, thus outperforming traditional classifiers. It has a lot of functionality and is based on the neural network approach, making it able to identify slight botnet behaviours in order to improve Android security. © The Author(s) 2024.

关键词： Bot (Internet)

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Enhancing LLM Code Generation Using Natural Language Processing in the Context of Machine Learning 28th

Enhancing LLM Code Generation Using Natural Language Process...

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28th International Symposium on Database Engineered Applications, IDEAS 2024

作者： Nelson, Jordan Pavlidis, Michalis Fish, Andrew Polatidis, Nikolaos School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9783031834714

The rapid rise in popularity of Generative AI and Large Language Models (LLMs) has brought both innovation and controversy, particularly regarding plagiarism and IP law infringements. However, one underexplored concern is the generation of code by these models, which, despite their potential, often includes errors and promotes poor programming practices. This paper explores new methods to address these issues by integrating LLMs with Automated Machine Learning (AutoML). By leveraging AutoML’s capabilities in hyperparameter tuning and model selection, we propose a novel approach for generating robust machine learning algorithms. This integration aims to enhance the accuracy and reliability of code generation while mitigating legal risks. Our findings include the application of Natural Language Processing (NLP) and Natural Language Understanding (NLU) techniques to interpret chatbot prompts, thereby improving the generation and customization of machine learning models. The proposed methodology demonstrates practical implementation and high prediction accuracy, offering a promising solution to the current challenges faced by LLM-based code generation. In summary the findings of the paper are as follows: A new implementation of natural language processing for natural language understanding in the context of chatbot prompts aims to serve as an initial step for feature extraction, which will be utilised by an AutoML system to generate machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Ambient intelligence for self-care and self-management

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Journal of Ambient Intelligence and Humanized Computing 2023年第1期14卷 1-3页

作者： Hervás, Ramón Baños, Oresti Villalonga, Claudia Department of Technologies and Information Systems Modelling Ambient Intelligence Research Group University of Castilla-La Mancha Paseo de La Universidad 4 Ciudad Real Spain Department of Computer Architecture and Computer Technology Research Center for Information and Communication Technologies University of Granada Calle Periodista Rafael Gomez Montero 2 Granada Spain

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Leveraging Machine Learning for Crime Intent Detection in Social Media Posts 1st

Leveraging Machine Learning for Crime Intent Detection in ...

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1st International Conference on AI-generated Content, AIGC 2023

作者： Bokolo, Biodoumoye George Onyehanere, Praise Ogegbene-Ise, Ebikela Olufemi, Itunu Tettey, Josiah Nii Armah Department of Computer Science Sam Houston State University HuntsvilleTX77340 United States Department of Architecture Technology and Engineering University of Brighton BrightonBN2 4AT United Kingdom Department of Computer and Software Engineering Kennesaw State University KennesawGA United States Department of Computer Science and Quantitative Methods Austin Peay State University ClarksvilleTN37044 United States Department of Computer Science and Engineering Wright State University DaytonOH45435 United States

ISBN: (纸本)9789819975860

Detecting crime intent from user-generated content on social media platforms has become increasingly important for law enforcement and crime prevention. This paper presents a comprehensive approach for crime intent detection from user tweets using machine learning techniques. The study utilizes a dataset of about 400,000 tweets and applies data preprocessing, feature selection, and model training with logistic regression, ridge regression classifier, Stochastic Gradient Descent (SGD) classifier, Random Forests, and support vector machine models. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess the models’ performance. The results reveal that the logistic regression model achieves the highest accuracy ratio of 0.981 in detecting crime intent from tweets. This research showcases the effectiveness of machine learning and advanced transformer-based models in leveraging social media data for crime analysis. The findings provide valuable insights into the potential for early detection and monitoring of crime intent using online platforms, contributing to the field of crime prevention and law enforcement. The utilization of machine learning techniques offers new avenues for understanding and analyzing crime-related sentiments expressed by social media users. By accurately detecting crime intent from user-generated content, law enforcement agencies can enhance their proactive measures, monitor public sentiment towards crime, and shape policies and interventions to address public concerns effectively. The research highlights the significance of leveraging social media data for crime detection and emphasizes the potential impact of advanced machine learning models in improving public safety and crime prevention efforts. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Global scale solar energy harnessing: An advanced intra-hourly diffuse solar irradiance predicting framework for solar energy projects

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Neural Computing and Applications 2024年第18期36卷 10585-10598页

作者： El-kenawy, El-Sayed M. Bailek, Nadjem Bouchouicha, Kada Zerouali, Bilel Hassan, Muhammed A. Kuriqi, Alban Jamil, Basharat Colak, Ilhami Khalil, Adel Ibrahim, Abdelhameed Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura35111 Egypt Laboratory of Mathematics Modeling and Applications Department of Mathematics and Computer Science Faculty of Sciences and Technology Ahmed Draia University of Adrar Adrar01000 Algeria Energies and Materials Research Laboratory Faculty of Sciences and Technology University of Tamanghasset Tamanrasset10034 Algeria MEU Research Unit Middle East University Amman Jordan Adrar01000 Algeria Vegetal Chemistry-Water-Energy Laboratory Faculty of Civil Engineering and Architecture Department of Hydraulic Hassiba Benbouali University of Chlef Ouled Fares Chlef02180 Algeria Mechanical Power Engineering Department Faculty of Engineering Cairo University Giza12613 Egypt School of Architecture University of Waterloo 7 Melville St S CambridgeONN1S 2H4 Canada CERIS Instituto Superior Tecnico Universidade de Lisboa Lisbon Portugal Civil Engineering Department University for Business and Technology PristinaKosovo10000 High Temperature Processes Unit IMDEA Energy Institute Av. Ramón de La Sagra 3 Móstoles Madrid28935 Spain Faculty of Engineering and Architectures Nisantasi University Istanbul Turkey Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

Diffuse horizontal irradiance (DHI) forecasts are critical for adopting solar photovoltaic technology. Yet, they can lack reliability given the limited and uncertain meteorological data available for desert areas. This research develops a new sine cosine dipper throated optimization (SCDTO) technique, targeting DHI prediction even given such data constraints. SCDTO uniquely hybridizes sine cosine metaheuristics, adept at exploration, with dipper throat optimization, providing focused exploitation. This hybridization aims to create a robust ensemble model capable of delivering reliable DHI predictions despite climatic uncertainty. The ensemble model, employing various input combinations, was rigorously evaluated across multiple meteorological stations. The SCDTO algorithm exhibited remarkable performance improvements, yielding substantial reductions exceeding 93% in root mean squared error and 98% in mean absolute error at key stations in Morocco, including Tan-Tan, Zagora, Erfoud, and Oujda. Comparative analyses against established optimization algorithms consistently underscored the superior predictive capabilities of SCDTO. Visualizations, including box plots and histograms, demonstrated SCDTO’s efficacy in minimizing prediction errors, particularly for Tan-Tan, Zagora, and Erfoud stations. Also, statistical validation through one-way analysis of variance (ANOVA) further affirmed the significance of the proposed SCDTO method. Therefore, this hybrid metaheuristic optimization enables more accurate DHI predictions from limited meteorological data. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Solar energy

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Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach

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Environmental Science and Pollution Research 2024年第22期31卷 32449-32463页

作者： Rajak, Upendra Chaurasiya, Prem Kumar Verma, Tikendra Nath Dasore, Abhishek Ağbulut, Ümit Meshram, Kundan Saleel, CAhamed Saboor, Shaik Cuce, Erdem Mian, Zhibao Department of Mechanical Engineering RGM College of Engineering and Technology Nandyal518501 India Department of Mechanical Engineering National Institute of Technology Raipur492010 India Department of Mechanical Engineering Maulana Azad National Institute of Technology Vellore462003 India Department of Biological and Agricultural Engineering Faculty of Engineering Universiti Putra Malaysia Selangor UPM Serdang43400 Malaysia Department of Mechanical Engineering Faculty of Mechanical Engineering Yildiz Technical University Istanbul34349 Turkey Department of Civil Engineering Guru Ghasidas Vishwavidyalaya Bilaspur India Department of Mechanical Engineering College of Engineering King Khalid University Abha61421 Saudi Arabia School of Mechanical Engineering Vellore Institute of Technology Vellore Tamil Nadu Vellore632014 India Department of Mechanical Engineering Faculty of Engineering and Architecture Recep Tayyip Erdogan University Zihni Derin Campus Rize53100 Turkey School of Engineering and the Built Environment Birmingham City University BirminghamB4 7XG United Kingdom Faculty of Science and Engineering School of Computer Science University of Hull Cottingham Road HullHU6 7RX United Kingdom Center for Research Impact & amp Outcome Chitkara University Punjab Rajpura140401 India

This article presents the outcomes of a research study focused on optimizing the performance of soybean biofuel blends derived from soybean seeds specifically for urban medium-duty commercial vehicles. The study took into consideration elements such as production capacity, economics and assumed engine characteristics. For the purpose of predicting performance, combustion and emission characteristics, an artificial intelligence approach that has been trained using experimental data is used. At full load, the brake thermal efficiency (BTE) dropped as engine speed increased for biofuel and diesel fuel mixes, but brake-specific fuel consumption (BSFC) increased. The BSFC increased by 11.9% when diesel compared to using biofuel with diesel blends. The mixes cut both maximum cylinder pressure and NOx emissions. The biofuel-diesel fuel proved more successful, with maximum reduction of 9.8% and 22.2 at rpm, respectively. The biofuel and diesel blend significantly improved carbon dioxide (CO2) and smoke emissions. The biofuel blends offer significant advantages by decreeing exhaust pollutants and enhancing engine performance. Graphical Abstract: (Figure presented.) © The Author(s) 2024.

关键词： Diesel engines

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Designing demand responsive transport services in small-sized cities using an agent-based model

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Transportation Research Procedia 2023年 69卷 759-766页

作者： Giovanni Calabrò Michela Le Pira Nadia Giuffrida Giuseppe Inturri Matteo Ignaccolo Gonçalo H. de A. Correia Department of Civil Engineering and Architecture University of Catania Catania 95123 Italy DICATECH Polytechnic University of Bari Via E. Orabona 4 Bari 70126 Italy Department of Electric Electronic and Computer Engineering University of Catania Catania 95123 Italy Department of Transport & Planning Delft University of Technology Delft Netherlands

This paper presents an agent-based model (ABM) to simulate and compare two different operation strategies of a public transport service in small-sized cities, namely a fixed-route transit (FRT) and a demand-responsive transport (DRT) service, under varying demand rates and supply configurations. The ABM builds upon a previous work by the Authors, where flexible and feeder services of a Mass Rapid Transit system were simulated. In this paper, instead of a many-to-one pattern typical of a feeder service, we considered a many-to-many one. The objective is to investigate the conditions that make a DRT more attractive than a FRT in small-sized cities and guide its design considering the demand fluctuation, land-use pattern, service constraints and passenger preferences. A dispatching algorithm for the DRT allows to assign each new trip request to a vehicle, and a couple of origin and destination stops, updating the vehicle schedule in real time. The service includes fixed and virtual stops, allowing request consolidation and balancing operator-related (cost of the service) and user-related (quality of service) needs. The model is applied to Vittoria (Italy), a small city with 60,000 residents in Southern Italy where most trips are made by car, also due to the absence of an urban public transport service. First results highlight the benefits of providing a flexible service compared to a fixed one to minimize detours, waiting times and walking distances experienced by passengers while allowing for a higher shareability and efficiency of the service.

关键词： Demand Responsive Transport (DRT) Mobility on demand Agent-based simulation Mass rapid transit Low demand areas

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Implementation of an Intelligent Animal Monitoring System Using Wireless Sensor Network and IoT Platform

Implementation of an Intelligent Animal Monitoring System Us...

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2022 International Conference on Cyber Resilience, ICCR 2022

作者： Ur Rehman, Ateeq Arshad, Jehangir Sadiq, Muhammad Tariq Rehman, Abdul Ahmad, Munir Hasan, Mohammad Kamrul Al Hamadi, Hussam Faiz, Tauqeer Gc University Department of Electrical Engineering Lahore Pakistan Comsats University Islamabad Lahore Campus Department of Electrical and Computer Engineering Lahore54000 Pakistan School of Architecture Technology and Engineering University of Brighton BrightonBN2 4AT United Kingdom School of Computer Science Ncbae Lahore54000 Pakistan Faculty of Information Science and Technology Selangor Bangi43600 Malaysia College of Engineering and It University of Dubai United Arab Emirates School of It Skyline University College Sharjah United Arab Emirates - Swinburne Australia

ISBN: (纸本)9781665461221

Meat and dairy products are negatively impacted by a lack of technology in the livestock industry in developing countries. To cater for this challenge, the Internet of Things (IoT), Node-MCU, and intelligent wireless sensor nodes are deployed to create a new smart dairy monitoring system. A cow collar with a temperature sensor, a GPS module, and an environmental parameter regularization system are included. Data from modules is stored in a separate database using an innovative IoT -based front end. All the deployed WS nodes can determine whether the environment is stable or not at any given time. Sensors built inside cow collars can assess vital indications like temperature and pulse rate, as well as the animal's exact location. The design of the Cow collar also has a feature that automatically alerts the owner. The plug-and-play technology offered is designed to be easy to adapt. When a farm has many animals, automation decreases the need for human involvement and so lowers labor expenses. The exploitation of remote monitoring systems improves the health of the animals and hence yields a better amount of dairy. This study also includes a detailed comparison of the proposed implementation with current systems to demonstrate its originality. Other applications, such as smart monitoring of zoo animals and poultry, may be derived from the proposed technology. © 2022 IEEE.

关键词： Microcontrollers

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Higher education assessment practice in the era of generative AI tools

arXiv

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arXiv 2024年

作者： Ogunleye, Bayode Zakariyyah, Kudirat Ibilola Ajao, Oluwaseun Sharma, Hemlata Olayinka, Olakunle School of Architecture Technology & Engineering University of Brighton BrightonBN2 4GJ United Kingdom Department of Computing & Mathematics Manchester Metropolitan University ManchesterM1 5GD United Kingdom Department of Computer Science University of Sheffield SheffieldS1 4DP United Kingdom Department of Computing Sheffield Hallam University SheffieldS1 2NU United Kingdom

The higher education (HE) sector benefits every nation’s economy and society at large. However, their contributions are challenged by advanced technologies like generative artificial intelligence (GenAI) tools. In this paper, we provide a comprehensive assessment of GenAI tools towards assessment and pedagogic practice and, subsequently, discuss the potential impacts. This study experimented using three assessment instruments from data science, data analytics, and construction management disciplines. Our findings are two-fold: first, the findings revealed that GenAI tools exhibit subject knowledge, problem-solving, analytical, critical thinking, and presentation skills and thus can limit learning when used unethically. Secondly, the design of the assessment of certain disciplines revealed the limitations of the GenAI tools. Based on our findings, we made recommendations on how AI tools can be utilised for teaching and learning in HE. © 2024, CC BY.

关键词： Project management

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