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DeepSwarm:towards swarm deep learning with bi-directional optimization of data acquisition and processing

Frontiers of computer science 2025年第3期19卷 125-127页

作者： Sicong LIU Bin GUO Ziqi WANG Lehao WANG Zimu ZHOU Xiaochen LI Zhiwen YU School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’an 710072China School of Data Science City University of Hong KongHong Kong 999077China College Of Computer Science And Technology Harbin Engineering UniversityHarbin 150001China

1 Introduction On-device deep learning(DL)on mobile and embedded IoT devices drives various applications[1]like robotics image recognition[2]and drone swarm classification[3].Efficient local data processing preserves privacy,enhances responsiveness,and saves ***,current ondevice DL relies on predefined patterns,leading to accuracy and efficiency *** is difficult to provide feedback on data processing performance during the data acquisition stage,as processing typically occurs after data acquisition.

关键词： drone swarm classification efficient local data processing data processing deep learning dl device deep learning bi directional optimization iot devices swarm deep learning

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Modified Bat Algorithm: a newly proposed approach for solving complex and real-world problems

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Soft Computing 2024年第13-14期28卷 7983-7998页

作者： Umar, Shahla U. Rashid, Tarik A. Ahmed, Aram M. Hassan, Bryar A. Baker, Mohammed Rashad Department of Software College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler KRI Erbil Iraq Department of Computer Science College of Science Charmo University Sulaimani Chamchamal46023 Iraq

Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge toward a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA’s performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA’s significant superiority over other algorithms. In addition, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Swarm intelligence

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Utilization of Two Cross Two Switch for Bitonic Sorting Circuit

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Micro and Nanosystems 2025年第1期17卷 45-57页

作者： Bhattacharjee, Subhasree Sarkar, Soumyadip Das, Kunal Sadhu, Arindam Sarkar, Bikramjit Department of Computer Application Narula Institute of Technology Kolkata India Department of Computer Science Acharya Prafulla Chandra College Kolkata India Department of Electronics & Communication Engineering Greater Kolkata College of Engineering and Management West Bengal India Department of Computer Science & Engineering JIS College of Engineering West Bengal India

Background: The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing. Objective: To implement the bitonic sorting circuit, 2x2 switch, is used. The overarching objective of implementing a reversible sorting circuit is to mitigate power consumption while promising secure communication. The successful implementation of the bitonic sorting circuit utilizing the proposed 2x2 switch is validated through simulations conducted in IBM Qiskit. Methods: This study begins by employing the control swap gate to implement a 2x2 switch, aimed at enabling concurrent computing operations within multiprocessor systems to enhance throughput. Subsequently, following the successful implementation of the 2x2 switch, the study proceeds to design a bitonic sorting circuit. Results: Bitonic sort using a 2x2 switch on the IBM Quantum Experience (IBMQ) platform via the QISKit SDK in Python 3.6. Specifically, Qiskit v0.29.0 is utilized, with OpenQASM. OpenQASM serves as the quantum assembly and instruction language. Conclusion: This work presents a methodology for constructing a 2x2 switch, utilizing a reversible control swap gate as its core component. Additionally, it demonstrates the intricate development and implementation of a bitonic sorting circuit, capitalizing on this innovative switch architecture. © 2025 Bentham science Publishers.

关键词： Quantum computers

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Modeling of Optimal Deep Learning Based Flood Forecasting Model Using Twitter data

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Intelligent Automation & Soft Computing 2023年第2期35卷 1455-1470页

作者： G.Indra N.Duraipandian Department of Computer Science and Engineering RMK College of Engineering and TechnologyChennai601206India Department of Computer Science and Engineering Saveetha Engineering CollegeChennai602106India

Aﬂood is a signiﬁcant damaging natural calamity that causes loss of life and *** work on the construction ofﬂood prediction models intended to reduce risks,suggest policies,reduce mortality,and limit property damage caused byﬂ*** massive amount of data generated by social media platforms such as Twitter opens the door toﬂood *** of the real-time nature of Twitter data,some government agencies and authorities have used it to track natural catastrophe events in order to build a more rapid rescue ***,due to the shorter duration of Tweets,it is difﬁcult to construct a perfect prediction model for determiningﬂ*** learning(ML)and deep learning(DL)approaches can be used to statistically developﬂood prediction *** the same time,the vast amount of Tweets necessitates the use of a big data analytics(BDA)tool forﬂood *** this regard,this work provides an optimal deep learning-basedﬂood forecasting model with big data analytics(ODLFF-BDA)based on Twitter *** suggested ODLFF-BDA technique intends to anticipate the existence ofﬂoods using tweets in a big data *** ODLFF-BDA technique comprises data pre-processing to convert the input tweets into a usable *** addition,a Bidirectional Encoder Representations from Transformers(BERT)model is used to generate emotive contextual embed-ding from ***,a gated recurrent unit(GRU)with a Multilayer Convolutional Neural Network(MLCNN)is used to extract local data and predict theﬂ***,an Equilibrium Optimizer(EO)is used toﬁne-tune the hyper-parameters of the GRU and MLCNN models in order to increase prediction *** memory usage is pull down lesser than 3.5 MB,if its compared with the other algorithm *** ODLFF-BDA technique’s performance was validated using a benchmark Kaggle dataset,and theﬁndings showed that it outperformed other recent approaches signiﬁcantly.

关键词： Big data analytics predictive models deep learning ﬂood prediction twitter data hyperparameter tuning

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Nonlinear Filtering With Sample-Based Approximation Under Constrained Communication:Progress, Insights and Trends

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1539-1556页

作者： Weihao Song Zidong Wang Zhongkui Li Jianan Wang Qing-Long Han IEEE the State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering ScienceCollege of Engineering Peking University the Department of Computer Science Brunel University London the School of Aerospace Engineering Beijing Institute of Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.

关键词： Communication constraints maximum correntropy filter networked nonlinear filtering particle filter sample-based approximation unscented Kalman filter

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An optimized congestion control protocol in cellular network for improving quality of service

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Multimedia Tools and Applications 2024年 1-26页

作者： Sandhya, V.S. Joshi, S.M. Department of Computer Science and Engineering SDM College of Engineering and Technology Karnataka Dharwad580002 India

In recent decades, Cellular Networks (CN) have been used broadly in communication technologies. The most critical challenge in the CN was congestion control due to the distributed mobile environment. Some approaches, like mobile edge computing, congesting controlling systems, machine learning, and heuristic models, have failed to prevent congestion in CN. The reason for this problem is the lack of continuous monitoring function at every time interval. So, in this present study, a novel Golden Eagle-based Primal–dual Congestion Management (GEbPDCM) has been developed for the Long-Term Evolution (LTE) Ad hoc On-demand Vector (AODV) network. Here, the Golden Eagle function features will afford the continuous monitoring function to monitor data congestion. Hence, the main objective of this research is to improve the Quality of service (QoS) by optimizing congestion controls. Here, the QoS is measured by different metrics, such as delay, packet delivery ratio (PDR), throughput, packet loss, and energy consumption. Initially, the nodes were created in the MATLAB environment, and the GEbPDCM was activated to predict the data load and estimate the node density to measure the node status. Then, the high data overload was migrated to another free status node to control congestion. Finally, the proposed model efficiency was measured regarding delay, packet delivery ratio (PDR), throughput, packet loss, and energy consumption. The proposed model has scored high throughput at 97.1 Mbps and 97.1 PDR, reducing delay to 67.4 ms and 50.6 mJ energy consumption. Hence, the present model is suitable for the LTE network. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Quality of service

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Web Page Recommendation Using Distributional Recurrent Neural Network

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computer Systems science & engineering 2023年第4期45卷 803-817页

作者： Chaithra G.M.Lingaraju S.Jagannatha Department of Computer Science&Engineering Sapthagiri College of EngineeringBangaloreIndia Department of Information Science and Engineering Sahyadri College of Engineering&ManagementMangaluruIndia Department of Computer Applications MSRITBangaloreIndia

In the data retrieval process of the data recommendation system,the matching prediction and similarity identification take place a major role in the *** that,there are several methods to improve the retrieving process with improved accuracy and to reduce the searching ***,in the data recommendation system,this type of data searching becomes complex to search for the best matching for given query data and fails in the accuracy of the query recommendation *** improve the performance of data validation,this paper proposed a novel model of data similarity estimation and clustering method to retrieve the relevant data with the best matching in the big data *** this paper advanced model of the Logarithmic Directionality Texture Pattern(LDTP)method with a Metaheuristic Pattern Searching(MPS)system was used to estimate the similarity between the query data in the entire *** overall work was implemented for the application of the data recommendation *** are all indexed and grouped as a cluster to form a paged format of database structure which can reduce the computation time while at the searching ***,with the help of a neural network,the relevancies of feature attributes in the database are predicted,and the matching index was sorted to provide the recommended data for given query *** was achieved by using the Distributional Recurrent Neural Network(DRNN).This is an enhanced model of Neural Network technology to find the relevancy based on the correlation factor of the feature *** training process of the DRNN classifier was carried out by estimating the correlation factor of the attributes of the *** are formed as clusters and paged with proper indexing based on the MPS parameter of similarity *** overall performance of the proposed work can be evaluated by varying the size of the training database by 60%,70%,and 80%.The parameters that are considered for performance analysis are Precision

关键词： Ontology data mining in big data logarithmic directionality texture pattern metaheuristic pattern searching system distributional recurrent neural network query recommendation

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Sign language detection using convolutional neural network

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2399-2424页

作者： Rakshit, Pranati Paul, Sarbajeet Dey, Shruti Computer Science & amp Engineering Department JIS College of Engineering West Bengal Kalyani India

Sign language recognition is an important social issue to be addressed which can benefit the deaf and hard of hearing community by providing easier and faster communication. Some previous studies on sign language recognition have used complex input modalities and feature extraction methods, limiting their practical applicability. This research aims to compare two custom-made convolutional neural network (CNN) models for recognizing American Sign Language (ASL) letters from A to Z, and determine which model performs better. The proposed models utilize a combination of CNN and Softmax activation function, which are powerful and widely used classification methods in the field of computer vision. The purpose of the proposed study is to compare the performance of two specially created CNN models for identifying 26 distinct hand signals that represent the 26 English alphabets. The study found that Model_2 had better overall performance than Model_1, with an accuracy of 98.44% and F1 score 98.41%. However, the performance of each model varied depending on the specific label, suggesting that the choice of model may depend on the specific use case and the labels of interest. This research contributes to the growing field of sign language recognition using deep learning techniques and highlights the importance of designing custom models. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Epidermis Bruise Analysis Using Deep Learning 6

Epidermis Bruise Analysis Using Deep Learning

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6th International Conference on Computational Intelligence and Communication Technologies, CCICT 2024

作者： Sharma, Vansheta Rani, Rupa Pathak, Priya Chakraborty, Sudeshna Department of Computer Science & Engineering- Data Science ABES Institute of Technology U.P. Ghaziabad India Department of Computer Science & Engineering Ajay Kumar Garg Engineering College U.P. Ghaziabad India Depatment of Computer Science & Engineering Banasthali Vidyapith Rajasthan India Department School of Computer Application and Technology Galgotias University UP India

ISBN: (纸本)9798350374629

Among the most vital organs that protect the body of a human from the external environment is the skin. Early skin illness identification is essential for reducing mortality because it prevents skin cancer, and any other skin disease from developing. But because it resembles skin patches, insect bites etc in appearance, early detection that might lengthen life expectancy is a fantastic experiment. We would be working with different skin diseases namely ‘Acne’, ‘Eczema or Atopic Dermatitis’, ‘melanoma’. A working model that uses image analysis and machine learning to classify and identify skin conditions. A brand-new, cutting edge automated system that can swiftly and accurately identify skin lesions is needed to deal with biomedical pictures. There is growing curiosity concerning the application of deep learning to the diagnosis and management of different diseases. For the early detection of skin disease, we therefore suggested a deep learning model based on CNN and the merger of the U-Net and ResNet models is presented as a fusion model that has been used to perform encoding decoding of the disorders using skin scans. This model integrates image processing, image enhancement, machine learning and segmentation techniques. The model yielded an overall accuracy of 95.6%. Incorporate additional skin conditions in the future to strengthen our model's suitability for medical applications. ©2024 IEEE.

关键词： Image enhancement

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On learning the right attention point for feature enhancement

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science China(Information sciences) 2023年第1期66卷 131-143页

作者： Liqiang LIN Pengdi HUANG Chi-Wing FU Kai XU Hao ZHANG Hui HUANG College of Computer Science and Software Engineering Shenzhen University Department of Computer Science and Engineering The Chinese University of Hong Kong School of Computer Science National University of Defense Technology School of Computing Science Simon Fraser University

We present a novel attention-based mechanism to learn enhanced point features for point cloud processing tasks, e.g., classification and segmentation. Unlike prior studies, which were trained to optimize the weights of a pre-selected set of attention points, our approach learns to locate the best attention points to maximize the performance of a specific task, e.g., point cloud classification. Importantly, we advocate the use of single attention point to facilitate semantic understanding in point feature learning. Specifically,we formulate a new and simple convolution, which combines convolutional features from an input point and its corresponding learned attention point(LAP). Our attention mechanism can be easily incorporated into state-of-the-art point cloud classification and segmentation networks. Extensive experiments on common benchmarks, such as Model Net40, Shape Net Part, and S3DIS, all demonstrate that our LAP-enabled networks consistently outperform the respective original networks, as well as other competitive alternatives, which employ multiple attention points, either pre-selected or learned under our LAP framework.

关键词： point convolution feature enhancement attention point deep neural network

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