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Annealing Harmony Search Algorithm to Solve the Nurse Rostering Problem

computers, Materials & Continua 2022年第6期71卷 5545-5559页

作者： Mohammed Hadwan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Science College of Applied SciencesTaiz UniversityTaizYemen Intelligent Analytics Group(IAG) College of ComputerQassim UniversityBuraydahSaudi Arabia

A real-life problem is the rostering of nurses at *** is a famous nondeterministic,polynomial time(NP)-hard combinatorial optimization *** the real-world nurse rostering problem(NRP)constraints in distributing workload equally between available nurses is still a difficult task to *** international shortage of nurses,in addition to the spread of COVID-19,has made it more difficult to provide convenient rosters for *** on the literature,heuristic-based methods are the most commonly used methods to solve the NRP due to its computational complexity,especially for large ***-based algorithms in general have problems striking the balance between diversification and ***,this paper aims to introduce a novel metaheuristic hybridization that combines the enhanced harmony search algorithm(EHSA)with the simulated annealing(SA)algorithm called the annealing harmony search algorithm(AHSA).The AHSA is used to solve NRP from a Malaysian *** AHSA performance is compared to the EHSA,climbing harmony search algorithm(CHSA),deluge harmony search algorithm(DHSA),and harmony annealing search algorithm(HAS).The results show that the AHSA performs better than the other compared algorithms for all the tested instances where the best ever results reported for the UKMMC dataset.

关键词： Harmony search algorithm simulated annealing combinatorial optimization problems timetabling metaheuristic algorithms nurse rostering problems

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The Future Era of Computing: Automatic Computing

The Future Era of Computing: Automatic Computing

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Singhal, Swasti Pathak, Nilotpal Kiet Group of Institutions Department of Computer Science and Information Technology Ghaziabad India Gl Bajaj Institute of Technology and Management Department of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350322309

The paper aim to describe the futuristic perspective of technology that will govern the aspects of our lives. The computers of today would soon start to become decapacitated as the needs, requirements and our dependency on technology continues to evolve. The study makes an attempt to look for the possible futuristic ideas that would aid this massive technological dependence. The ever-growing technology has proved beneficial to mankind however, we should have a glimpse of the next big thing. With the fall in Moore's law and the rise of quantum computing it has now become a necessity that the new options and opportunities must be examined. The term "quantum computing"refers to a sort of computing whose processes may exploit the processes of quantum physics, such as superposition, interference, and entanglement. Quantum computing is a relatively new field of research. Moore's Law is a model of technical economics that has enabled the information technology sector to roughly double the functionality and performance of digital devices every two years while retaining the same cost, power, and space. This paper is merely a small cog in this long chain of futuristic possibilities. © 2023 IEEE.

关键词： Digital devices

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Utilizing the Prophet Model for Time Series Based Stock Price Forecasting 5

Utilizing the Prophet Model for Time Series Based Stock Pric...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Pant, Janmejay Pant, Hitesh Kumar Kotlia, Priyanshi Singh, Devendra Sharma, Vaibhav Kumar Bhatt, Jaishankar Graphic Era Hill University Department of Computer Science and Engineering Bhimtal Campus India Kumaun University Department of Management Studies Uttarakhand Nainital India Amrapali Group of Institutes Department of Computer Applications Uttarakhand Haldwani India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350379945

Time series analysis and forecasting play a crucial role due to their extensive application across various practical domains. Time series data represents a sequential order or collection of data points depicting the values of a variable at regular time intervals. The stock market is widely regarded as one of the most intricate financial systems, comprising numerous components or stocks, whose prices fluctuate significantly over time. Stock market forecasting aims to reveal the trends within the market as they evolve. All investors in the stock market strive to maximize returns on their investments while minimizing associated risks Due to the high sensitivity and rapid changes in stock markets, the primary objective of stock-trend prediction is to devise innovative approaches that can anticipate stocks leading to substantial profits. A statistical model capable of predicting Indian stock prices will be constructed using time series data from the Indian stock market. In our study, we utilize daily stock price data from a prominent company listed on the National Stock Exchange (NSE) of India. © 2024 IEEE.

关键词： Marketplaces

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Editorial

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Journal of Reliable Intelligent Environments 2023年第4期9卷 369-370页

作者： Augusto, Juan Carlos Coronato, Antonio Research Group on Development of Intelligent Environments Department of Computer Science Middlesex University London United Kingdom Università Giustino Fortunato Benevento Italy

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Particle Swarm Optimizer Guided Active Contour Model for Lung Image Segmentation 1

Particle Swarm Optimizer Guided Active Contour Model for Lun...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Biradar, Ashwini Vishwanathrao Pancholi, Priti Vijaykumar Bhosale, Jyoti Dinkar Pawar, Bharat Madhavrao Suryawanshi, Ujwala V M.S.Bidve Engineering College Computer Engineering Department Maharashtra Latur India Vilasrao Deshmukh Foundations Group of Institutions Computer Engineering Department Maharashtra Latur India Rajarshi Shahu Junior Science College Computer Department Maharashtra Latur India

ISBN: (纸本)9798331518943

Snakes, also known as active contour models, are widely used in a variety of applications, including Detecting object boundaries, segmenting images, tracking objects, and classification based on energy minimization. While standard optimization techniques can be used to achieve energy minimization, more recently, techniques based on evolutionary algorithms inspired by nature have been created. Particle swarm optimization (PSO) is one such evolutionary method that has been widely applied in active contours. Conventional PSO provides accurate curve recognition in the object border because it converges quickly and is readily took in a local minimum. The suggested PSOGACM has each control point in the snake and also has a swarm to assign in it. Subsequently, the swarms exchange information with one another to jointly seek for their optimal spots. The active contour model considerably improves the PSO-based search process's performance. The outcomes of the experiments show that the suggested strategy is more effective than traditional methods. The proposed system has accuracy of 91.15%, sensitivity of 89.20%, and precision of 88.23% is more effective and more accurate. © 2024 IEEE.

关键词： Lung cancer

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Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

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computers, Materials & Continua 2024年第5期79卷 3139-3173页

作者： Norah Abdullah Al-Johany Sanaa Abdullah Sharaf Fathy Elbouraey Eassa Reem Abdulaziz Alnanih Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Software Engineering and Distributed System Research Group King Abdulaziz UniversityJeddah21589Saudi Arabia

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed ***, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

关键词： High-performance computing parallel computing software engineering software defect message passing interface deadlock

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Pre-trained SAM as data augmentation for image segmentation

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 268-282页

作者： Junjun Wu Yunbo Rao Shaoning Zeng Bob Zhang Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhouChina School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengduChina Pattern Analysis and Machine Intelligence Research Group Department of Computer and Information ScienceUniversity of MacaoMacaoChina

Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the ***,data augmentation mainly involved some simple transformations of ***,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative ***,these methods required a mass of computation of training or *** this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for *** the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved *** this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation *** comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and *** this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,***,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.

关键词： data augmentation image segmentation large model segment anything model

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An Adaptive Federated Relevance Framework for Spatial-Temporal Graph Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2227-2240页

作者： Zhang, Tiehua Liu, Yuze Shen, Zhishu Xu, Rui Chen, Xin Huang, Xiaowei Zheng, Xi Ant Group Shanghai200120 China Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430062 China Macquarie University Department of Computing SydneyNSW2109 Australia

Spatial-temporal data contains rich information and has been widely studied recently due to the rapid development of relevant applications. For instance, medical institutions often use electrodes attached to different parts of a patient to analyse the electroencephalogram data with rich spatial and temporal features for health assessment and disease diagnosis. Existing research has mainly used deep learning techniques such as convolutional neural network (CNN) or recurrent neural network (RNN) to extract spatial-temporal features. Yet, it is challenging to incorporate both inter-dependencies spatial information and dynamic temporal changes simultaneously. In reality, for a model that leverages these spatial-temporal features to fulfil complex prediction tasks, it often requires considerable training data to obtain satisfactory model performance. To address the challenges at hand, we propose an adaptive federated relevance framework called FedRel for spatial-temporal graph learning. After transforming the raw spatial-temporal data into high-quality features, the core Dynamic Inter-Intra Graph (DIIG) module in the framework leverages these features to generate the spatial-temporal graphs capable of capturing both the hidden topological and long-term temporal correlation information. To further enhance the model's generalization ability and performance, while still prioritizing local data privacy, we have incorporated a relevance-driven federated learning module into our framework. This module leverages the diverse data distributions from different participants and applies attentive aggregations of respective models. We then conducted extensive experiments on two real-world spatial-temporal datasets. The results demonstrate the framework's efficacy in spatial-temporal interpretation, collaborative model training, and handling divergent data distributions across various comparison settings. © 2023 IEEE.

关键词： Recurrent neural networks

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An effective algorithm for predicting load and dynamic task scheduling in cloud fog architecture for smart homes

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International Journal of Cloud Computing 2025年第1期14卷 54-85页

作者： Agrawal, Krishna Kant Kumar, Sujeet Seth, Jitendra Kumar Gupta, Abhishek Kumar Lamba, Sonia School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida India GL Bajaj Institute of Management Uttar Pradesh Greater Noida India Department of Information Technology KIET Group of Institutions Uttar Pradesh Ghaziabad India Department of Information Technology Jagannath International Management School New Delhi India Department of Computer Science ABES Engineering College Uttar Pradesh Ghaziabad India

The need for smart homes with many devices and services continues to rise quickly. With this surge, smart homes need task scheduling and load-prediction algorithms to provide the proper services for the residents. A deep learning-based dynamic job scheduling and load prediction technique for cloud-fog smart homes is proposed in this paper. This algorithm forecasts task arrival rates at each fog node and assigns them to available fog nodes. It dynamically schedules tasks based on fog node workload. Another option is to send non-real-time jobs to the cloud and real-time tasks to the fog layer. This optimises load distribution for performance. Using these task assignee models and features, the program optimises prioritised tasks, scores, network latency, and device resource characteristics. We simulate the algorithm’s performance in various workloads in this part. The proposed algorithms achieved in higher percentile for 93.79% latency, 95.00% throughput, 95.34% response time, 96.28% scalability, 94.20% fault-tolerance, 97.41% scheduling capacity, 91.41% load balancing capacity, and 95.22% priority management. The results indicate that such an algorithm significantly surpasses the conventional task scheduling methods in load balancing and shortens the average task response time. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Scheduling algorithms

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