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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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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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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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Transaction transmission model for blockchain channels based on non-cooperative games

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science China(Information sciences) 2023年第1期66卷 97-114页

作者： Peiyun ZHANG Chenxi LI Mengchu ZHOU Wenjun HUANG Abdullah ABUSORRAH Omaimah O.BAMASAG Engineering Research Center of Digital Forensics of Ministry of Education School of Computer and SoftwareNanjing University of Information Science and Technology School of Computer and Information Anhui Normal University Department of Electrical and Computer Engineering New Jersey Institute of Technology Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering and K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University Department of Computer Science Faculty of Computing and Information Technology Center of Excellence in Smart Environment Research King Abdulaziz University

In blockchain networks, transactions can be transmitted through channels. The existing transmission methods depend on their routing information. If a node randomly chooses a channel to transmit a transaction, the transmission may be aborted due to insufficient funds(also called balance) or a low transmission rate. To increase the success rate and reduce transmission delay across all transactions, this work proposes a transaction transmission model for blockchain channels based on non-cooperative game *** balance, channel states, and transmission probability are fully considered. This work then presents an optimized channel transaction transmission algorithm. First, channel balances are analyzed and suitable channels are selected if their balance is sufficient. Second, a Nash equilibrium point is found by using an iterative sub-gradient method and its related channels are then used to transmit transactions. The proposed method is compared with two state-of-the-art approaches: Silent Whispers and Speedy Murmurs. Experimental results show that the proposed method improves transmission success rate, reduces transmission delay,and effectively decreases transmission overhead in comparison with its two competitive peers.

关键词： Blockchain channel transaction transmission channel balance channel state channel transmission probability non-cooperative game

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Design and simulation of an accelerometer based on NV center spin–strain coupling

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Chinese Physics B 2024年第1期33卷 192-198页

作者：季鲁敏赵立业王裕海 Key Laboratory of Micro-Inertial Instrument and Advanced Navigation Technology Ministry of EducationDepartment of Instrument Science and EngineeringSoutheast UniversityNanjing 210096China

The nitrogen-vacancy (NV) center quantum systems have emerged as versatile tools in the field of precision measurement because of their high sensitivity in spin state detection and miniaturization potential as solid-state *** this paper,an acceleration sensing scheme based on NV spin–strain coupling is proposed,which can effectively eliminate the influence of the stray noise field introduced by traditional mechanical *** the finite element simulation,it is found that the measurement bandwidth of this ensemble NV spin system ranges from 3 kHz to hundreds of kHz with structure√*** required power is at the sub-μW level,corresponding to a noise-limited sensitivity of 6.7×10^(-5) /√*** with other types of accelerometers,this micro-sized diamond sensor proposed here has low power consumption,exquisite sensitivity,and integration *** research opens a fresh perspective to realize an accelerometer with appealing comprehensive performance applied in biomechanics and inertial measurement fields.

关键词： nitrogen-vacancy(NV)accelerometer spin–strain diamond

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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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Corrosion behavior of single-and poly-crystalline dual-phase TiAl–Ti3Al alloy in NaCl solution

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International Journal of Minerals,Metallurgy and Materials 2023年第4期30卷 689-696页

作者： Dongpeng Wang Guang Chen Anding Wang Yuxin Wang Yanxin Qiao Zhenguang Liu Zhixiang Qi Chain Tsuan Liu School of Materials Science and Engineering Jiangsu University of Science and TechnologyZhenjiang 212100China Centre for Advanced Structural Materials Department of Mechanical and Biomechanical EngineeringCity University of Hong KongHong Kong 999077China Engineering Research Center of Materials Behavior and Design Ministry of EducationNanjing University of Science and TechnologyNanjing 210094China School of Materials Science and Engineering Dongguan University of TechnologyDongguan 523808China

To clarify the correlation of single-crystalline structure with corrosion performance in high-strength TiAl alloys, electrochemical and surface characterization was performed by comparing Ti–45Al–8Nb dual-phase single crystals with their polycrystalline counterparts in NaCl solution. Polarization curves show a lower corrosion rate and a higher pitting potential of ~280 mV for the dual-phase single crystals. Electrochemical impedance spectroscopy and potentiostatic polarization plots revealed a higher impedance of the charge transfer through the compact passive film. Surface composition analysis indicated a compact film with more content of Nb, as twice as that in the film on the *** results reflect that the dual-phase Ti–45Al–8Nb single crystals possess a higher corrosion resistance in NaCl solution, compared with their polycrystalline counterpart, arising from a more homogeneous microstructure and composition distribution.

关键词： titanium alloy single crystal corrosion X-ray photoelectron spectroscopy

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Boosting the Prediction of Brain Tumor Using Two Stage BiGait Architecture 25

Boosting the Prediction of Brain Tumor Using Two Stage BiGai...

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25th International Conference on Digital Image Computing: Techniques and Applications, DICTA 2024

作者： Khan, Saif-Ur-Rehman Khan, Zia Hossain, Md Zakir Mayumu, Nicanor Yasmin, Farhana Aziz, Younas School of Computer Science and Engineering Central South University Changsha China Curtin University Perth Australia Nanjing University of information Science and Technology Department of Computer and Software Nanjing China

ISBN: (纸本)9798350379037

Deep learning (DL) techniques hold immense promise for revolutionizing medical diagnostics, including brain tumor detection. Detecting malignancies in the brain is fraught with challenges that carry critical implications for accurate and timely diagnosis. Early detection is paramount, and our paper presents a novel approach to address this issue. We introduce the BiGait ensemble transfer learning (TL) architecture tailored for brain tumor classification tasks. Our proposed methodology is a two-stage process that harnesses the power of TL. In the initial phase, we employ two established models, ResNet50 and EfficientNetB1, and fine-tune them using the Kaggle multiclass dataset. This includes the incorporation of supplementary feature layers. We also leverage a stack ensemble solution to combine these feature layers, enhancing the model's overall predictive capabilities. Our approach is designed to shift the input distribution positively, effectively mitigating overfitting and reducing hardware overhead. To validate the effectiveness of our method, we conducted extensive experiments and evaluation. The dataset utilized in this work, publicly available on Kaggle contains 3,264 brain MRIs categorized into four groups: normal (500 images), meningioma (937 images), glioma (926 images), and pituitary tumor (901 images). The experimental results demonstrate that our model surpasses traditional single-model approaches, achieving an impressive accuracy rate of 96% and exhibiting superior generalization capabilities. This advancement in brain tumor classification holds great promise for early diagnosis and improved patient outcomes. © 2024 IEEE.

关键词： Magnetic resonance imaging

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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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Dual-Cloud Multi-Secret Sharing Architecture for Privacy Preserving Persistent Computation

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IEEE Transactions on Information Forensics and Security 2024年 19卷 7523-7535页

作者： Chen, Yu-Chi Yang, Jhe-Kai Yen, Hsin-Chan Lin, Pei-Wen National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan Yuan Ze University Department of Computer Science and Engineering Taoyuan32003 Taiwan Genie Networks Taipei114064 Taiwan

With the prevalence of artificial intelligence, people collect data through numerous sensors and use machine learning to create models for intelligent services. However, data privacy and massive data issues are raised with the proliferation of devices. Although secret sharing can be the solution to providing privacy and performing efficient computations (than homomorphic encryption-based) in the privacy domain, sharing large amounts of data may impose a considerable storage burden on resource-restricted devices. Therefore, we aim to reduce the shares that participants need to hold and construct secure computation protocols that form privacy-preserving data utilization with multi-secret sharing. In this paper, we study the mechanism of persistent computation with multi-secret sharing for privacy protection, which avoids the requirements for participants to store extra information during secure computation. We present the mask techniques to convert secrets into protected data and upload them separately to non-colluding dual-cloud servers through multi-secret sharing. Then, we can locally evaluate the operation result shares by revealing a part of the protected data, where the true secret consistently remains secure as the other part stays in a shared state. Eventually, we outsource a dataset to the cloud, build a privacy-preserving multi-party kNN classification based on our scheme, and provide some experiments to demonstrate the feasibility and usability of storage size. © 2005-2012 IEEE.

关键词： computer architecture

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