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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Samarasekara, Iromie K. Mendis, Oshan K. Ahangama, Sapumal Atukorale, Ajantha S. University of Colombo University of Colombo School of Computing Sri Lanka University of Moratuwa Department of Computer Science & Engineering Sri Lanka

ISBN: (纸本)9781665480451

Stop-Loss strategies are often used by investors to combat negative returns by predetermining thresholds at which they should exit trades. Though existing traditional Stop-Loss mechanisms such as Fixed Stop-Loss and Trailing Stop-Loss are empirically proven to have the ability to minimize risks associated with trades, they still face serious challenges when it comes to achieving a balance between risk and return. In this study, we develop a Deep Learning model that combines the concept of Stop-Loss with the capabilities offered by Deep Neural Networks. The architecture is composed of three components where trend detection and price prediction components provide inputs to the stop price prediction component which predicts the variation of stop price. The study focuses on short term trades of minute frequency which involves analysing massive chunks of data that fluctuates rapidly within extremely short time intervals. Despite the advancements that has taken place in the context of Big Data analytics, intraday financial time series analysis has not received much academic attention. The model utilizes convolutional layers to capture spatial features along with Long Short Term Memory networks to capture temporal dependencies in price sequences. The proposed solution gives outstanding results for diverse market conditions and it works specifically well for trades that are downtrending. We evaluate our model for a portfolio that consists of five stock symbols from NASDAQ that are adequately liquid and two cryptocurrencies which are highly circulated. The model delivers a stable outcome across all symbols indicating its ability to be generalized over a range of symbols and its tolerance to diverse market conditions. Results also indicate that Stop-Loss mechanisms possess the potential to work well even in speculative markets such as the cryptocurrency market. The ability to reduce losses without compromising on opportunities to realize profits in dynamic and unstable market

关键词： Time series analysis

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Improved Fireworks Optimization Algorithm for Multi-product Human-robot Collaborative Hybrid Disassembly Line Balancing 4

Improved Fireworks Optimization Algorithm for Multi-product ...

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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： Guo, Feng Guo, Xiwang Wang, Jiacun Wang, Weitian Cao, Jinrui Bigaume, Imane Liaoning Petrochemical University Information And Control Engineering Fushun China Monmouth University Computer Science And Software Eng. Wes Long BranchNJ United States Montclair State University School Of Computing Montclair United States New Jersey City University Computer Science Department Jersey City United States

ISBN: (纸本)9798350315790

In the remanufacturing process, the efficient disassembly of end-of-life (EOL) products assumes paramount significance for recycling endeavors. This work introduces a novel disassembly model aiming at enhancing disassembly efficiency within a hybrid disassembly line through the strengths of humanrobot collaboration. In order to optimize the generation of disassembly sequences, this work proposes an improved fireworks optimization algorithm (IFW) that combines uniform mutation and two-point crossover methods. Afterwards, the model's validity and algorithmic accuracy are substantiated using the CPLEX solver (a commercial solver). Furthermore, a comparative analysis and evaluation against alternative algorithms, such as salp swarm algorithm (SSA) and whale optimization algorithm (WOA), demonstrate a better stability of the proposed algorithm in the human-robot collaborative hybrid disassembly line balancing problem. © 2024 IEEE.

关键词： Optimization

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Enhancing Building Safety Through Machine Learning-Based Smoke Detection 5

Enhancing Building Safety Through Machine Learning-Based Smo...

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5th IEEE International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Mohamed, Sulaiman Syed Judeson Antony Kovilpillai, J. Pragya Mir, Mahmood Hussain Golda Brunet, R. Mir, Tawseef Ahmad Alliance School of Advanced Computing Alliance University Bangalore India Government College of Engineering Department of Computer Science & Engineering Thanjavur India

ISBN: (纸本)9798350356236

Smoke detection in surveillance systems plays a crucial role in ensuring the safety and security of various environments, including buildings, forests, and industrial sites. In this paper, a comprehensive analysis of machine learning algorithms applied to smoke detection is presented. The proposed technique is validated using the Smoke Detection Dataset from Kaggle. The dataset comprises a diverse collection of images captured in different scenarios, including both smoke and non-smoke instances. The performance of various machine learning classifiers was evaluated. This included ensemble methods Random Forest (RF), Gradient Boosting (GB), AdaBoost (AB), linear models Logistic Regression (LR), kernel methods Support Vector Machine (SVM), decision-based methods Decision Tree (DT), and nearest neighbor methods K-Nearest Neighbors (KNN). Their precision, recall, AUC-ROC score, and Intersection over Union (IoU) are measured to assess their effectiveness in smoke detection. The results highlight that classifiers such as RF, DT, KNN, GB and AdaBoost achieve outstanding performance, with perfect scores in multiple metrics. © 2025 IEEE.

关键词： Random forests

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SAM struggles in concealed scenes—empirical study on “Segment Anything”

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science China(Information sciences) 2023年第12期66卷 278-280页

作者： Ge-Peng JI Deng-Ping FAN Peng XU Bowen ZHOU Ming-Ming CHENG Luc VAN GOOL School of Computing Australian National University Computer Vision Lab(CVL) ETH Zurich Department of Electronic Engineering Tsinghua University College of Computer Science Nankai University

Large models open up new opportunities for artificial intelligence. In the past few months, there has been a boom in training foundation models on the vast linguistic corpus to produce amazing applications, e.g., Chat GPT, *** natural language processing and multimodal learning communities have been revolutionized. Large models' capacity for generalization and emergent makes it easy for users to believe that large models can solve anything.

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Automated Classification of Mouth Cancers for Early Cancer Detection Using a VGG-16 Inspired CNN

Automated Classification of Mouth Cancers for Early Cancer D...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Sasmita Padhy Preetam Suman Amrit Suman Sachikant Das Masood H Siddiqui School of Computing Science and Engineering VIT Bhopal University Bhopal MP Computer Science and Engineering Sharda School of Engineering & Technology Sharda University Noida U.P. Computer Science and Engineering GIET University Gunupur Odisha Dept of Statistics University of Lucknow UP

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

Mouth cancers, a risky sort of cancer, are typically caused by tobacco use and cigarette smoking. Early detection of oral cancer is essential for green remedy and affected person survival. This study pursues to broaden an automatic approach for diagnosing sicknesses, which can come across probably cancerous oral lesions at an early degree, saving lives. A novel convolutional neural network (CNN) structure, stimulated via the VGG-sixteen community, has been particularly designed for this purpose. The look evaluates various gadgets getting to know and deep studying fashions, highlighting their effectiveness in identifying and categorizing oral cancers. The Convolutional Neural Network (CNN) version gives excessive accuracy in distinguishing between ordinary and cancerous pictures, indicating the capacity for stepped-forward early detection of oral cancerous tumors. This look underscores the efficacy of computerized diagnostic tools in detecting oral cancer early, permitting spark-off and potentially life-saving intervention.

关键词： Accuracy Computational modeling Mouth Feature extraction Cancer detection Convolutional neural networks Lesions Artificial intelligence Cancer

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Dina: Deformable Interaction Analogy

SSRN

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SSRN 2024年

作者： Huang, Zeyu Dai, Sisi Xu, Kai Zhang, Hao Huang, Hui Hu, Ruizhen College of Computer Science and Software Engineering Shenzhen University China School of Computer Science National University of Defense Technology China School of Computing Science Simon Fraser University Canada

We introduce Deformable Interaction Analogy (DINA) to create similar interactions between 3D objects. Our goal is to generate multiple analogous 3D interactions between an anchor object and different target objects based on a single demo interaction. To achieve this, we optimize the anchor object's pose or shape to mimic the demo interaction with new target objects. We propose using an Interaction Interface (ITF) as a descriptive and robust representation of interactions. For interaction analogy, we transform the ITF to guide feature matching during the anchor object's repositioning and deformation. Qualitative and quantitative experiments demonstrate the effectiveness of our ITF-guided deformable interaction analogy, even when compared to state-of-the-art methods that utilize more complex interaction representations and feature learning from large datasets. © 2024, The Authors. All rights reserved.

关键词： Large datasets

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Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes

arXiv

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arXiv 2024年

作者： Su, Xiaoxin Zhou, Yipeng Cui, Laizhong Lui, John C.S. Liu, Jiangchuan College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Shenzhen University Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong HKSAR Hong Kong School of Computing Science Simon Fraser University Canada

In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds, without touching private data owned by individual clients. FL is appealing in preserving data privacy;yet the communication between the PS and scattered clients can be a severe bottleneck. Model compression algorithms, such as quantization and sparsification, have been suggested but they generally assume a fixed code length, which does not reflect the heterogeneity and variability of model updates. In this paper, through both analysis and experiments, we show strong evidences that variable-length is beneficial for compression in FL. We accordingly present Fed-CVLC (Federated Learning Compression with Variable-Length Codes), which fine-tunes the code length in response of the dynamics of model updates. We develop optimal tuning strategy that minimizes the loss function (equivalent to maximizing the model utility) subject to the budget for communication. We further demonstrate that Fed-CVLC is indeed a general compression design that bridges quantization and sparsification, with greater flexibility. Extensive experiments have been conducted with public datasets to demonstrate that Fed-CVLC remarkably outperforms state-of-the-art baselines, improving model utility by 1.50%-5.44%, or shrinking communication traffic by 16.67%-41.61%. Copyright © 2024, The Authors. All rights reserved.

关键词： Data privacy

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Makespan and Security-Aware Workflow Scheduling for Cloud Service Cost Minimization Using Firefly Optimizer 22nd

Makespan and Security-Aware Workflow Scheduling for Cloud ...

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zhou, Chengliang Wang, Tian Li, Liying Sun, Jin Zhou, Junlong School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai200062 China

ISBN: (纸本)9783031226762

As efficient commercial information technology, cloud computing has attracted more and more users to submit their requests to the cloud platform and pay for them based on the amount and quality of services. One of the most important concerns of cloud service providers is to minimize the monetary cost for completing users’ requests while meeting constraints on the quality of services such as makespan and security. To solve this concern, this paper proposes a makespan and security-aware workflow scheduling method based on an improved firefly optimizer that consists of a new solution initialization scheme, a firefly position updating scheme, a firefly-to-solution mapping operator, and two task-to-virtual machine assignment schemes. We carry out extensive simulation experiments on real-world workflows to validate our proposed workflow scheduling method. The results reveal that the monetary cost reduction achieved by our method can be up to 54.0% compared to a baseline and two state-of-the-art approaches. © 2023, Springer Nature Switzerland AG.

关键词： Cloud computing

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Comparative Analysis of Cloud computing and the Gig Economy Pricing Strategies

Comparative Analysis of Cloud Computing and the Gig Economy ...

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Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： M Jothish Kumar Gajulapalli Venkata Karthik Kumar Reddy Iyapparaja S Nowritha Department of Smart Computing School of Computer Science Engineering and Information Systems VIT Vellore Vellore Tamil Nadu India School of Computer Science Engineering and Information Systems VIT Vellore Vellore Tamil Nadu India Department of Smart Computing School of Computer Science Engineering and Information Systems VIT University Vellore Tamil Nadu India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Cloud computing services and gig economy platforms vary in flexibility, efficiency, and scalability due to their pricing schemes. Cloud computing services provide flexibility and cost control via pay-as-you-go, subscription, and freemium models. Like gig economy platforms, workers and customers pay for task completion, time-based contracts, and premium services. Assessing how successfully different pricing techniques fulfil varied demands, from short-term initiatives to long-term commitments, requires identifying their strengths and weaknesses. The goal is to evaluate these models' capacity to meet shifting demand, save money, and scale. The methodology also assesses cost predictability and company and consumer advantages of each pricing structure. The comparison seeks to improve resource allocation and financial planning by revealing how various pricing systems function in different circumstances. The research helps decision-makers use cloud services or gig economy platforms for different operational purposes by emphasizing their economic consequences. From datasets, simulated pricing model data was examined. The first example includes settings such as Hours Used (5–25), Pay-as-You-Go (10–50 USD), and Subscription-Based (30 USD). The second instance has Job Size (Resources) from 5 to 25, Freemium Model (USD) from 10 to 30, and Usage-Based Pricing (USD) from 10 to 50.

关键词： Cloud computing Adaptation models Costs Biological system modeling Computational modeling Scalability Pricing Predictive models Data models Resource management

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Enhancing Building Safety Through Machine Learning-Based Smoke Detection

Enhancing Building Safety Through Machine Learning-Based Smo...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Sulaiman Syed Mohamed Judeson Antony Kovilpillai J Pragya Mahmood Hussain Mir Golda Brunet R Tawseef Ahmad Mir Alliance School of Advanced Computing Alliance University Bangalore India Department of Computer Science & Engineering Government College of Engineering Thanjavur India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

关键词： Support vector machines Radio frequency Smart buildings Surveillance Buildings Urban areas Nearest neighbor methods Feature extraction Safety Smoke detectors

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