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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Guru Prasad, M.S. Praveen Gujjar, J. Kotiyal, Arnav Prasanna Kumar, H.R. Devadas, Raghavendra M Jahan, Afsar Department of Computer Science and Engg. Dehradun India Faculty of Management Studies Bengaluru India Visvesvaraya Technological University PESITM Dept. of Information Science & Engg. Shivamogga India Gitam School of Technology Department of Computer Science and Engineering Gitam University Bengaluru India Department of Computer Application Dehradun India

ISBN: (纸本)9798350376470

As the field of machine learning continues to advance, the importance of effective exploratory data analysis (EDA) cannot be overstated, especially in the context of classification problems. This research paper introduces an innovative and automated approach to exploratory data analysis using Autoviz, a powerful Python library designed to streamline the visualization process for machine learning datasets. The study aims to demonstrate the efficacy of Autoviz in meaningful insights, facilitating feature understanding, and supporting data preprocessing for classification tasks. Furthermore, the paper discusses how Autoviz assists in feature selection by highlighting the relevance and importance of each feature about the target variable. This feature-centric analysis contributes to optimizing machine learning models by guiding the selection of relevant input features, thereby improving model accuracy and interpretability. Autoviz is a Python library designed to automate the exploratory data analysis (EDA) process for machine learning tasks. It is specifically tailored to generate a variety of visualizations that aid in understanding the structure and characteristics of a dataset. The results indicate that Autoviz significantly reduces the time and effort required for exploratory data analysis while maintaining the quality and depth of insights obtained. The differences between Autoviz and Pycaret is discussed in this paper. It has been observed that AutoViz automates the process of exploring and analyzing data, while PyCaret is designed for the comparison, construction, and tuning of machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning

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A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems

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ACM Transactions on Evolutionary Learning and Optimization 2023年第4期3卷 1-25页

作者： Yazdani, Delaram Yazdani, Danial Yazdani, Donya Omidvar, Mohammad Nabi Gandomi, Amir H. Yao, Xin Department of Computer Engineering Mashhad Branch Azad University Mashhad*** Iran Faculty of Engineering & Information Technology University of Technology Sydney Ultimo2007 Australia AI Lab British Antarctic Survey CambridgeCB3 0ET United Kingdom School of Computing Leeds University Business School University of Leeds LeedsLS2 9JT United Kingdom Obuda University Budapest1034 Hungary Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

Population clustering methods, which consider the position and fitness of individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm’s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method. © 2023 Copyright held by the owner/author(s).

关键词： Resource allocation

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Adaptive Bandwidth Optimization Techniques for Underwater Fiber Optic Networks 3

Adaptive Bandwidth Optimization Techniques for Underwater Fi...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Kale, Sunil D. Samrat, Bhavuk Varalakshmi, S. Tiwari, Mohit Singh, Vaishali Umamaheswari, B. Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Center for Management Studies Faculty of Engineering and Technology Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India

ISBN: (纸本)9798350319125

Underwater fiber optic networks provide hyperlinks that permit communiqué among offshore structures, deep-sea bases, and oil rigs. Despite the growing need for better facts costs, the constrained to be had bandwidth in such networks stays a major undertaking for their efficient use. To overcome this task, a variety of adaptive bandwidth optimization techniques were proposed in current years. A number of those strategies involve dynamic modulation and coding, whilst other techniques use adaptive scheduling algorithms. All tactics rely upon dynamic replace of bandwidth allocation to make the only use of the underlying fiber hyperlink. Within the case of dynamic modulation and coding, modifications are made to the physical layer configuration of the hyperlink to conform to converting conditions on the hyperlink. On the other hand, adaptive scheduling strategies use course optimization algorithms to select the maximum green route for records to tour, re-allocating bandwidth as had to accommodate heavy loads. Subsequently, new techniques such as EQUINOX are also being explored to further optimize bandwidth usage on underwater fiber optic networks by means of combining elements of each dynamic modulation and coding and adaptive scheduling. © 2023 IEEE.

关键词： Hyperlinks Modulation Optimization Scheduling Techniques

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Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

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computer Modeling in engineering & sciences 2024年第4期139卷 801-816页

作者： D.Dorathy Prema Kavitha L.Francis Raj Sandeep Kautish Abdulaziz S.Almazyad Karam M.Sallam Ali Wagdy Mohamed Department of Mathematics Voorhees CollegeVelloreTamil NaduIndia Chandigarh Group of Colleges JhanjeriMohaliPunjabIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Faculty of Science and Technology School of IT and SystemsUniversity of CanberraCanberraAustralia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan

The intuitive fuzzy set has found important application in decision-making and machine *** enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images *** image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye *** degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal *** proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between *** methodology was used to clarify the input images and make them adequate for the process of glaucoma *** objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were *** the peak regions were identified,the recurrence relationships among those peaks were then *** partitioning was done due to varying degrees of similar and dissimilar concentrations in the *** and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and *** distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

关键词： Convolutional Neural Network(CNN) glaucomatous eyes fuzzy difference equation intuitive fuzzy sets image segmentation retinal images

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DF-CFAR: deep feature constant false alarm ratio detector based on feature game for sea-surface small target with robustness to sea states

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Neural Computing and Applications 2025年 1-20页

作者： Xiang, Houhong Zhu, Liangliang Chen, Yufeng Wang, Fengyu Zeng, Xiaolu School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Department of Early Warning Technology Wuhan Early Warning Academy Wuhan430019 China Hangzhou Institute of Technology Xidian University Hangzhou311200 China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

The classical constant false alarm rate (CFAR) detector is optimal for target detection in Gaussian white noise but struggles with unknown, time-varying sea states. Data-driven target detection methods are highly sensitive to clutter distribution, leading to poor detection performance and high false alarm rates (Pfa) under unknown sea states. This paper proposes a deep feature constant false alarm ratio (DF-CFAR) detector based on a feature game model. The input to the feature extraction network is fragmented fast time-dimensional data processed by coherent integration, incorporating a feature game mechanism. This mechanism effectively mines target echo features that are uncorrelated with the background sea clutter. By transforming the target detection problem under varying sea state conditions into a regression prediction problem, we achieve constant false alarm detection through a statistical probability threshold. Simulation and real data experiments demonstrate that, compared to commonly used algorithms, the proposed method not only achieves higher detection probability and lower false alarm rates but also exhibits excellent constant false alarm characteristics and stronger robustness to unseen sea states. Results from publicly available X-band radar data processing show that the proposed algorithm can eliminate the influence of different sea clutters and improve the detection performance of sea-surface small targets by an equivalent signal-to-clutter ratio (SCR) of about 3.1 dB. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Gaussian distribution

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UMixer: A Novel U-shaped Convolutional Mixer for Multi-scale Feature Fusion in Medical Image Segmentation 16th

UMixer: A Novel U-shaped Convolutional Mixer for Multi-scal...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Su, Yongxin Huang, Hongbo Song, Zun Lin, Lei Liu, Jinhan Mechanical Electrical Engineering School Beijing Information Science and Technology University Beijing China Computer School Beijing Information Science and Technology University Beijing China School of Economics and Management Beijing Information Science and Technology University Beijing China Institute of Computing Intelligence Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9783031202322

Medical image segmentation plays a critical role in assisting diagnosis and prognosis. Since the transformer was first introduced to the field, the neural network structure has experienced a transition from ConvNet into Transformer. However, some redesigned ConvNets in recent works show astonishing effects, which outperforms classic elaborate ConvNets, and even complicated transformers. Inspired by these works, we introduced large-kernel convolutions to improve the ConvNets in capturing the long-range dependency. Cooperated with a novel multi-scale feature fusion method, we proposed a U-shaped convolutional structure, dubbed UMixer, which effectively integrates shallow spatial information with deep semantic information and high-resolution detailed information with low-resolution global information. Without any attention mechanism and pre-training on large datasets, UMixer achieves more accurate segmentation results than traditional ConvNets and Transformers on the Synapse dataset. Experiments demonstrate the effectiveness of this multi-scale feature fusion structure and its capability in modeling long-range dependency. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Large dataset

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First wetland mapping at 10-m spatial resolution in South America using multi-source and multi-feature remote sensing data

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science China Earth sciences 2024年第10期67卷 3252-3269页

作者： Weiwei SUN Gang YANG Yuling HUANG Dehua MAO Ke HUANG Lin ZHU Xiangchao MENG Tian FENG Chao CHEN Yong GE Department of Geography and Spatial Information Techniques Ningbo UniversityNingbo 315211China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences&Natural Resources ResearchChinese Academy of SciencesBeijing 100101China Natural Resources and Planning Bureau Shanghai Pudong New DistrictShanghai 200100China Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China School of Geography Science and Geomatics Engineering Suzhou University of Science and TechnologySuzhou 215009China

Wetland degradation has been accelerating in recent years globally. Accurate information on the geographic distribution and categories of wetlands is essential for their conservation and management. Despite being the world′s fourth largest continent, South America has limited research on wetland mapping, and there is currently no available map that provides comprehensive information on wetland distribution and categories in the region. To address this issue, we used Sentinel-1, Sentinel-2 and SRTM data, developed a sample collection method and a wetland mapping method with a collection of multi-source features such as optical features, polarization features and shape features for South American wetlands. We produced a 10-m resolution wetland map based on the Google Earth Engine(GEE) platform. Our Level-1 wetland cover map accurately captured six wetland sub-categories with an overall accuracy of 96.24% and a kappa coefficient of 0.8649, while our Level-2 water cover map included five sub-categories with an overall accuracy of 97.23% and a kappa coefficient of0.9368. The results show that the total area of existing wetlands in South America is approximately 1,737,000 km~2, which is6.8% of the total land area. Among the ten wetland categories, shallow sea had the largest area(960,527.4 km~2), while aquaculture ponds had the smallest area 1513.6 km~2. Swamp had the second largest area(306,240.1 km~2). Brazil, Argentina,Venezuela, Bolivia, and Colombia were found to have the largest wetland areas, with Brazil and Colombia having the most diverse wetland categories. This product can serve as baseline data for subsequent monitoring, management, and conservation of South American wetlands.

关键词： Wetland mapping Google Earth Engine Sentinel imagery South America

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Integrating Network Security into Software Defined Networking (SDN) Architectures 2

Integrating Network Security into Software Defined Networkin...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Vadakkethil Somanathan Pillai, Sanjaikanth E Polimetla, Kiran University Of North Dakota School of Electrical Engineering and Computer Science Grand ForksND United States Adobe Inc. Dept. of Information Technology AustinTX United States

ISBN: (纸本)9798350317558

The decision for introduced green and cozy networking solutions has grown as the era continues to evolve. With centralized network control and programmability functions, software applications defined as Networking (SDN) have emerged as a promising method to address this call. But, as SDN architectures become vaster, the need for strong community protection turns into even extra important. Integrating community protection into SDN structure includes enforcing safety mechanisms straight away into the manipulate aircraft, considering actual-time-grained manipulation over community site visitors. This may be done through protection guidelines and policies that can be dynamically completed and updated via the centralized controller. With the aid of leveraging the programmable nature of SDN, network protection may be, without troubles, tailored to satisfy the changing hazard panorama. The open API in SDN also permits integration with gift-day protection answers, firewalls, and intrusion detection systems, similarly, to improving the community's overall safety. © 2024 IEEE.

关键词： Architecture Dynamically Networking Protection technology Visitors

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Efficient Layer Sharing for Application Deployment in Edge Computing Environment

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IEEE Transactions on Consumer Electronics 2025年

作者： Tang, Bing Ma, Yahui Liu, Yu Yang, Qing Cao, Buqing Tang, Mingdong Guangzhou Maritime University School of Computer Science Guangzhou510725 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Hunan University of Technology School of Computer Zhuzhou412007 China Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou510006 China

Edge computing provides consumer electronics with faster and smarter responses, thereby enhancing user experience. Based on container technology, in a cloud-edge collaborative environment, edge servers pull images from image registries in the cloud and deploy them locally to serve various smart devices and consumer electronics. Leveraging image layer sharing can reduce the amount of image transmission, as locally cached layers on edge servers need not be repeatedly pulled. This paper investigates the rational deployment of applications on edge servers under a layer sharing mechanism. Considering a scenario with distributed multiple image registries, the objective is to minimize the total deployment time under the condition of image layer sharing. In this scenario, the application deployment problem can be modeled as a variable-value 0-1 multiple knapsack problem with partial item sharing. To address this improved knapsack problem, two algorithms named greedy minimum value based on layer sharing (GD-LSV) and greedy minimum value density based on layer sharing (GD-LSVD) are proposed. Experimental simulations using real layer dataset have proved the effectiveness of the proposed algorithms. Compared with representative algorithms, as the number of applications increases, the proposed algorithms shorten the total deployment time by up to 51.78% and reduce the total storage consumption by up to 22.79%. © 2025 IEEE.

关键词： Application deployment container edge computing greedy algorithm layer sharing

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Findings of the Shared Task on Multimodal Social Media Data Analysis in Dravidian Languages (MSMDA-DL)@DravidianLangTech 2024 4

Findings of the Shared Task on Multimodal Social Media Data ...

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4th Workshop on Speech, Vision, and Language Technologies for Dravidian Languages, DravidianLangTech 2024

作者： Premjith, B. Jyothish Lal, G. Sowmya, V. Chakravarthi, Bharathi Raja Nandhini, K. Natarajan, Rajeswari Murugappan, Abirami Bharathi, B. Rajiakodi, Saranya Ponnusamy, Rahul Mohan, Jayanth Reddy, Mekapati Spandana Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India School of Computer Science University of Galway Ireland School of Mathematics and Computer Sciences Central University of Tamil Nadu India SASTRA University India Department of Information Science and Technology Anna University India SSN College of Engineering Tamil Nadu India Central University of Tamil Nadu India

ISBN: (纸本)9798891760783

This paper presents the findings of the shared task on multimodal sentiment analysis, abusive language detection and hate speech detection in Dravidian languages. Through this shared task, researchers worldwide can submit models for three crucial social media data analysis challenges in Dravidian languages: sentiment analysis, abusive language detection, and hate speech detection. The aim is to build models for deriving fine-grained sentiment analysis from multimodal data in Tamil and Malayalam, identifying abusive and hate content from multimodal data in Tamil. Three modalities make up the multimodal data: text, audio, and video. YouTube videos were gathered to create the datasets for the tasks. Thirty-nine teams took part in the competition. However, only two teams, though, turned in their findings. The macro F1-score was used to assess the submissions. © 2024 Association for Computational Linguistics.

关键词： Sentiment analysis

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