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IAENG International Journal of computer science 2024年第4期51卷 409-421页

作者： Lokesh, Bommareddy Rajagopalan, Narendran School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Department of Computer Science and Engineering National Institute of Technology Puducherry Karaikal609609 India

Unlike traditional networks, Software-defined networks (SDNs) provide an overall view and centralized control of all the devices in the network. SDNs enable the network administrator to implement the network policy by programming applications on top of the SDN controller using generic APIs. One or more controller instances can be deployed to administer the data flow by maintaining unified control of the entire network. The controller is expected to respond quickly to queries from forwarding devices. Presuming quick responses from the controller while enforcing a complex security mechanism is unreasonable. In this paper, the authors propose a unique, adaptive, lightweight, yet efficient technique called ISOLATOR to mitigate the effect of insider attacks and malfunctioning of distributed applications in an SDN-enabled cloud. The proposed security application upon detecting any suspicious activity by a virtual machine, isolates it by removing the interface to its respective shared network and reconnects it through a restricted network operating in a highly selective mode. By subjecting the data traffic to deep packet inspection, the restricted network searches for a match with a known worm pattern. The application is programmed for the OpenDayLight controller and the results show a significant improvement in identifying malicious activities with minimal latency and computational cost. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Controllers

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Distributed Population-Based Simultaneous Perturbation Stochastic Approximation for Fine-Tuning Large Language Models 21st

Distributed Population-Based Simultaneous Perturbation Stoch...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Tan, Yajing Huang, Yuwei Duan, Qiqi Yang, Yijun Shi, Yuhui Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9789819601219

Very recently, a memory-efficient version (called MeZO) of simultaneous perturbation stochastic approximation (SPSA), one well-established zeroth-order optimizer from the automatic control community, has shown competitive fine-tuning performance on large language models (LLMs) with billions of weights under the limited memory of a single GPU, which often hinders the direct usage of the standard backpropagation (BP) update. Furthermore, population-based evolutionary methods (such as evolution strategies from OpenAI and population-based training from DeepMind) have shown a promising direction to improving and scaling up various large-scale AI systems via efficiently utilizing increasingly available distributed computing resources, owing to their relatively simple yet efficient data and control flows. In this paper, we propose a population-based SPSA for fine-tuning LLMs with non-differentiable objective functions. Specifically, our proposed method combines the desirable exploration ability of distributed population-based evolutionary methods with the powerful exploitation ability of SPSA in the modern distributed computing environment wherein the efficient implementation of BP is still challenging because of its complex dependencies in forward-pass and backward-pass information flows. Simulation experiments on a set of challenging (non-differentiable) natural language process tasks empirically validate the potential of our method on fine-tuning even in a relatively small-scale distributed computing scenario. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stochastic systems

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A Novel Lightweight Image Encryption Scheme

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computers, Materials & Continua 2023年第4期75卷 2137-2153页

作者： Rawia Abdulla Mohammed Maisa’a Abid Ali Khodher Ashwak Alabaichi Department of Computer Science University of TechnologyBaghdadIraq Department of Biomedical Engineering University of KerbalaKerbalaIraq

Encryption algorithms are one of the methods to protect dataduring its transmission through an unsafe transmission medium. But encryptionmethods need a lot of time during encryption and decryption, so itis necessary to find encryption algorithms that consume little time whilepreserving the security of the data. In this paper, more than one algorithmwas combined to obtain high security with a short implementation time. Achaotic system, DNA computing, and Salsa20 were combined. A proposed5D chaos system was used to generate more robust keys in a Salsa algorithmand DNA computing. Also, the confusion is performed using a new *** proposed chaos system achieves three positive Lyapunov *** results demonstrate of the proposed scheme has a sufficient peak signalto-noise ratio, a low correlation, and a large key space. These factors makeit more efficient than its classical counterpart and can resist statistical anddifferential attacks. The number of changing pixel rates (NPCR) and theunified averaged changed intensity (UACI) values were 0.99710 and UACI33.68. The entropy oscillates from 7.9965 to 7.9982 for the tested encryptedimages. The suggested approach is resistant to heavy attacks and takes lesstime to execute than previously discussed methods, making it an efficient,lightweight image encryption scheme. The method provides lower correlationcoefficients than other methods, another indicator of an efficient imageencryption system. Even though the proposed scheme has useful applicationsin image transmission, it still requires profound improvement in implementingthe high-intelligence scheme and verifying its feasibility on devices with theInternet of Things (IoT) enabled.

关键词： Chaotic system lightweight confusion diffusion encryption Salsa20

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Scheduling Bi-Objective Lot-Streaming Hybrid Flow Shops with Consistent Sublots via an Enhanced Artificial Bee Colony Algorithm

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Complex System Modeling and Simulation 2025年第1期5卷 46-67页

作者： Benxue Lu Kaizhou Gao Peiyong Duan Adam Slowik School of Computer Science Liaocheng UniversityLiaocheng 252000China Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Information and Automation Engineering Qilu University of TechnologyJinan 250353China Department of Electronics and Computer Science Koszalin University of TechnologyKoszalin 75-453Poland

This work addresses bi-objective hybrid flow shop scheduling problems considering consistent sublots(Bi-HFSP_CS).The objectives are to minimize the makespan and total energy ***,the Bi-HFSP_CS is formalized,followed by the establishment of a mathematical ***,enhanced version of the artificial bee colony(ABC)algorithms is proposed for tackling the Bi-HFSP_***,fourteen local search operators are employed to search for better *** different Q-learning tactics are developed to embed into the ABC algorithm to guide the selection of operators throughout the iteration ***,the proposed tactics are assessed for their efficacy through a comparison of the ABC algorithm,its three variants,and three effective algorithms in resolving 95 instances of 35 different *** experimental results and analysis showcase that the enhanced ABC algorithm combined with Q-learning(QABC1)demonstrates as the top performer for solving concerned *** study introduces a novel approach to solve the Bi-HFSP_CS and illustrates its efficacy and superior competitive strength,offering beneficial perspectives for exploration and research in relevant domains.

关键词： hybrid flow shop scheduling Q-learning lot streaming local search

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Deep Learning Approaches for Multi-target Weather Patterns Forecasting in Bangladesh 5th

Deep Learning Approaches for Multi-target Weather Patterns F...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Mumu, Sumaiya Siddiqua Sultana, Syeda Samia Adnan, Imranul Islam Waughfa, Mohammed Z. Department of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (纸本)9789819791118

Weather forecasting in countries like Bangladesh poses unique challenges, given its diverse geographical features. The region experiences varying weather patterns influenced by factors such as monsoons, river systems, and coastal influences. The intricacies of high temperatures, sudden atmospheric changes, and complex circulations demand advanced forecasting methods. The research aimed to address the challenges in weather prediction for 8 cities in Bangladesh. The research used 3 deep learning models: CNN(Conv1D), LSTM, and GRU. Comparing these models to get the best results for temperature, rainfall, and humidity prediction. The performance of the models was evaluated using standard techniques, including the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). In individual forecasts for Maximum Temperature and Rainfall, GRU shows the lowest RMSE for Rainfall, while LSTM excels in Maximum Temperature prediction. Conv1D proves highly effective in Humidity forecasting. Our investigation emphasizes GRU’s superior performance across eight cities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mean square error

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Multi-Task Learning Model with Data Augmentation for Arabic Aspect-Based Sentiment Analysis

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computers, Materials & Continua 2023年第5期75卷 4419-4444页

作者： Arwa Saif Fadel Osama Ahmed Abulnaja Mostafa Elsayed Saleh Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computer Sciences and EngineeringHodeidah UniversityYemen

Aspect-based sentiment analysis(ABSA)is a fine-grained *** fundamental subtasks are aspect termextraction(ATE)and aspect polarity classification(APC),and these subtasks are dependent and closely ***,most existing works on Arabic ABSA content separately address them,assume that aspect terms are preidentified,or use a pipeline *** solutions design different models for each task,and the output from the ATE model is used as the input to the APC model,which may result in error propagation among different steps because APC is affected by ATE *** methods are impractical for real-world scenarios where the ATE task is the base task for APC,and its result impacts the accuracy of ***,in this study,we focused on a multi-task learning model for Arabic ATE and APC in which the model is jointly trained on two subtasks simultaneously in a *** paper integrates themulti-task model,namely Local Cotext Foucse-Aspect Term Extraction and Polarity classification(LCF-ATEPC)and Arabic Bidirectional Encoder Representation from Transformers(AraBERT)as a shred layer for Arabic contextual text *** LCF-ATEPC model is based on a multi-head selfattention and local context focus mechanism(LCF)to capture the interactive information between an aspect and its ***,data augmentation techniques are proposed based on state-of-the-art augmentation techniques(word embedding substitution with constraints and contextual embedding(AraBERT))to increase the diversity of the training *** paper examined the effect of data augmentation on the multi-task model for Arabic *** experiments were conducted on the original and combined datasets(merging the original and augmented datasets).Experimental results demonstrate that the proposed Multi-task model outperformed existing APC *** results were obtained by AraBERT and LCF-ATEPC with fusion layer(AR-LCF-ATEPC-Fusion)and the proposed data augmentation

关键词： Arabic aspect extraction arabic sentiment classification AraBERT multi-task learning data augmentation

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Energy efficient quantum-informed ant colony optimization algorithms for industrial internet of things

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1077-1086页

作者： Jannu, Srikanth Dara, Suresh Thuppari, Chaitanya Vidyarthi, Ankit Ghosh, Debjani Tiwari, Prayag Muhammad, Ghulam Department of Computer Science and Engineering Vaagdevi Engineering College Singaram506005 India School of Technology Woxsen University HyderabadTS 502 345 India Department of Computer Science and Engineering and Information Technology Jaypee Institute of Information Technology Noida201309 India Department of Computer Science and Engineering Bennett University Greater Noida201309 India School of Information Technology Halmstad University Halmstad301 18 Sweden Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11451 Saudi Arabia

One of themost prominent research areas in information technology is the Internet of Things (IoT) as its applications are widely used, such as structural monitoring, health care management systems, agriculture and battlefield management, and so on. Due to its self-organizing network and simple installation of the network, the researchers have been attracted to pursue research in the various fields of IoTs. However, a huge amount of work has been addressed on various problems confronted by IoT. The nodes densely deploy over critical environments and those are operated on tiny batteries. Moreover, the replacement of dead batteries in the nodes is almost impractical. Therefore, the problem of energy preservation and maximization of IoT networks has become the most prominent research area. However, numerous state-of-The-Art algorithms have addressed this issue. Thus, it has become necessary to gather the information and send it to the base station in an optimized method to maximize the network. Therefore, in this article, we propose a novel quantum-informed ant colony optimization (ACO) routing algorithm with the efficient encoding scheme of cluster head selection and derivation of information heuristic factors. The algorithm has been tested by simulation for various network scenarios. The simulation results of the proposed algorithm show its efficacy over a few existing evolutionary algorithms using various performance metrics, such as residual energy of the network, network lifetime, and the number of live IoT nodes. Impact Statement-Toward IoT-based applications, here we presented the Quantum-inspired ACO clustering algorithm for network lifetime. IoT nodes in the clustering phase choose theirCH through the distance between cluster member IoT nodes and the residual energy. Thus, CH selection reduces the energy consumption of member IoT nodes. Therefore, our significant contributions are summarized as follows. i. Developing Quantum-informed ACO clustered routing algor

关键词： Energy efficiency

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Ancient Cultural Models from Informal Learning Structures That Can Transform Developments in Africa: This Ndịgbo Discuss

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Journal of Entrepreneurship and Innovation in Emerging Economies 2025年第1期11卷 49-67页

作者： Ikpo, C.V. Akowuah, E.K. Boateng, K.O. Kponyo, J.J. Nunoo-Mensah, H. Appiah, Y.S. Department of Computer Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Telecommunication Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana

The innovation for entrepreneurial systems and the advocacy to enact policies that institutionalise it had recently flooded the literature. Every system has fundamental principles responsible for their state, progress and change;such is the impact of the Igbo apprenticeship scheme on the entrepreneurial system. This has recently resulted in many scholars calling for government involvement through enacting policies that will make the scheme all-inclusive for sustainable economic growth in developing economies. Amongst the huge body of literature in its advocacy, none has addressed the question of the underlying principles for reproducing the relations in terms of the mathematical phenomena that the system embodies. This work shows that the success of the Ndịgbo apprenticeship hinges on certain defined underlying metaphorical mathematical functional patterns responsible for the strategic informal management behavioural model. The symbolic characteristics derived from the informal discipleship learning mode are activated for commerce, by young males and their hands-on in trading, with persistent cooperation, integrity, communication and collective purposeful attempts to succeed. The evidence of the model described is extracted from the explorative and exploitative trajectories that emerged from their goal-pursuing dedications to satisfy customer needs, with optimal methods for managing resources and building economies of scale, emerge. This is known as Ịgba-ọsọ-ahịa. This work reveals that Ịgba-ọsọ-ahịa embodies attributes of models that have shaped world-class productive systems such as (a) the geometrical Mandelbrot model, (b) Markov Chain variant with a backoff process, (c) stochastic nonlinear models, (d) network spatial model and (e) system computational model. Finally, a simplistic novel stochastic model of the socio-economic processes is developed to solve complex engineering decisions and hyperparameter optimisation problems in this work. © 2024 SAGE Publicatio

关键词： behaviour discipleship Entrepreneurial minimisation socio-economic and cultural Ịgba-ọsọ-ahịa

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Influence of rare earth oxides on microstructure and mechanical properties of nickel-based superalloys fabricated by high energy beam processing:A review

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Journal of Rare Earths 2024年第9期42卷 1629-1645,I0001页

作者： Lianjie Bi Hua Yan Peilei Zhang Haichuan Shi Zhiyuan Li Ruidi Li School of Materials Science and Engineering Shanghai University of Engineering ScienceShanghai201620China College of Agricultural Science and Engineering Hohai UniversityNanjing211100China Department of Mechanics and Maritime Science Chalmers University of TechnologyGothenburSE41296Sweden State Key Laboratory of Powder Metallurgy Central South UniversityChangsha410083China

In this paper,the effects of rare earth oxides on the micro structure and mechanical properties of nickelbased superalloys prepared by high-energy beam processing technology were critically *** focus is on the optimal amount of rare earth oxides that can produce ideal *** attention was paid to their main strengthening mechanisms,including solid solution strengthening mainly in the form of solid solution dissolved in the nickel-based alloy and improving the microstructure of the alloy by grain refinement or fine grain strengthening produced by homogenizing the distribution phase.Y_(2)O_(3),La_(2)O_(3) and CeO_(2) rare earth oxides can also improve the fluidity of the alloy molten pool and reduce the segregation of alloying *** advantages can significantly improve the mechanical properties of the ***,this paper outlines the future research directions of rare earth oxides,aiming to expand their application potential.

关键词： Rare earths High energy beam Nickel-based superalloys Microstructure Mechanical properties

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Advanced Hydro-Informatic Modeling through Feedforward Neural Network, Federated Learning, and Explainable AI for Enhancing Flood Prediction

IEEE Open Journal of the Computer Society

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IEEE Open Journal of the computer Society 2025年第1期6卷 726-738页

作者： Mahir, Shahariar Hossain Tashrif, Md Tanjum An Karim, Md Ahsan Kundu, Dipanjali Rahman, Anichur Hamza, Md. Amir Farid, Fahim Al Miah, Abu Saleh Musa Mansor, Sarina Dhaka Bangladesh Mawlana Bhashani Science and Technology University Department of Computer Science and Engineering Tangail1902 Bangladesh Multimedia University Faculty of Engineering Cyberjaya63100 Malaysia Department of CSE Nilphamari Bangladesh

Flood prediction is one of the most critical challenges facing today's world. Predicting the probable time of a flood and the area that might get affected is the main goal of it, and more so for a region like Sylhet, Bangladesh where transboundary water flows and climate change have increased the risk of disasters. Accurate flood detection plays a vital role in mitigating these impacts by allowing timely early warnings and strategic planning. Recent advancements in flood prediction research include the development of robust, accurate, and low-cost flood models designed for urban deployment. By applying and utilizing powerful deep learning models show promise in improving the accuracy of prediction and prevention. But those models faced significant issues related to scalability, data privacy concerns and limitations of cross-border data sharing including the inaccuracies in prediction models due to changing climate patterns. To address this, our research adopts the Federated Learning (FL) framework in an effort to train state-of-the-art deep learning models like Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), Feed-Forward Neural Network (FNN) and Temporal Fusion Transformer-Convolutional Neural Network (TFT -CNN) on a 78-year dataset of rainfall, river flow, and meteorological variables from Sylhet and its upstream regions in Meghalaya and Assam, India. This approach promotes data privacy and allows collaborative learning while working under cross-border data-sharing constraints, therefore improving the accuracy of prediction. The results showed that the best-performing FNN model achieved an R-squared value of 0.96, a Mean Absolute Error (MAE) value of 0.02, Percent bias (PBIAS) value of 0.4185 and lower Root Mean Square Error (RMSE) in the FL environment. Explainable AI techniques, such as SHAP, sheds light on the most significant role played by upstream rainfall and river dynamics, particularly from Cherrapunji and the Surma-Kushiyara river system, in d

关键词： Convolutional neural networks

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