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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Pujari, Jambukeshwar Wasim, Javed Tripathi, Aprna Mangalayatan University Department of Computer Engineering and Applications Aligarh20221385 India Mangalayatan University Department of Computer Engineering and Applications Aligarh India Manipal University Department of Data Science Engineering Jaipur India

ISBN: (纸本)9798331512965

In recent years, Geographic Information Systems (GIS) have garnered a significant deal of interest for their ability to detect changes in metropolitan areas. One of the uses of change detection in satellite photographs is in the field of urban planning. Other applications include environmental monitoring and remote surveillance. A number of different approaches have been suggested in recent times for the purpose of automatically identifying alterations in the land cover based on GIS photographs. However, due to poor texture, cluttered context, and considerable object scale disparities, the convolutional neural network (CNN) method is only partially successful in capturing the context from geographic information systems (GISs). This is despite the fact that it remains an effective method for detecting change in urban environments. The article presents the construction of a CNN-based change detection model using the utilization of Dung Beetle Optimization (DBO). The DBO algorithm is used to adjust the loss parameter in order to increase the overall performance. The approach that was proposed makes use of the Onera satellite data set and displays an overall accuracy of 97.50% percent. In light of this, it appears that the strategy that is proposed is both very efficient and effective. © 2025 IEEE.

关键词： Convolutional neural networks

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Revealing Security Flaws in Cross-Modal Retrieval Models through Video Poisoning

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 6184-6194页

作者： Jin, Ming Hu, Wenbo Hong, Richang Zhu, Lei Hefei University of Technology School of Computer and Information Hefei China Data Space Research Institute Hefei Comprehensive National Science Center China Tongji University School of Electronic and Information Engineering Shanghai China

Video-text cross-modal retrieval is widely studied to improve retrieval accuracy. However, the security of video-text cross-modal retrieval models receives little attention. If attackers exploit the security vulnerabilities in these models, it poses a significant threat to the retrieval models. Thus, identifying security flaws in video-text cross-modal retrieval models becomes the focus of our research. We are the first to design a video poisoning model to uncover security vulnerabilities in retrieval models. Existing poisoning models have certain limitations when it comes to exploiting vulnerabilities in retrieval models. These include failing to comprehensively embed malicious information into the original video and being unable to maintain visual consistency between the original and poisoned videos. These limitations can result in unsuccessful attacks on retrieval models and an inability to effectively identify security flaws within them. To address these shortcomings, we design an efficient poisoning model that embeds malicious information thoroughly into the original clean data to attack video-text cross-modal retrieval models. We are the first to use a poisoning model to attack retrieval models, thereby uncovering their security vulnerabilities. Second, we introduce a bi-level poisoning module to ensure that malicious information is thoroughly embedded into the original video, thereby enhancing the attack capability of the poisoning model. Finally, we design an adversarial module to improve visual consistency between the original and poisoned videos, thus enhancing the concealment of malicious information within the training data of retrieval models. Our poisoning model can identify security flaws in video-text cross-modal retrieval models, providing insights into improving the security of retrieval models. The effectiveness of our model is validated on the MSR-VTT, LSMDC, and MSVD datasets. © 1991-2012 IEEE.

关键词： Content based retrieval

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PLAME: Piecewise-Linear Approximate Measure for Additive Kernel SVM (Extended abstract) 40

PLAME: Piecewise-Linear Approximate Measure for Additive Ker...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Chan, Tsz Nam Li, Zhe Leong Hou, U. Cheng, Reynold Shenzhen University Department of Computer Science and Software Engineering China Alibaba Cloud University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China The University of Hong Kong Department of Computer Science and Musketeers Foundation Institute of Data Science Hong Kong

ISBN: (纸本)9798350317152

Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection. Since training an additive kernel SVM model is very time-consuming, which is not scalable to largescale datasets, many efficient solutions have been developed in the past few years. However, most of the existing methods normally fail to achieve one of these three important conditions which are (1) low classification error, (2) low memory space, and (3) low training time. In order to simultaneously fulfill these three conditions, we develop the new piecewise-linear approximate measure (PLAME) for training additive kernel SVM models. Experimental results verify that this approach can achieve the best trade-off between accuracy, memory space, and training time compared with different types of state-of-the-art methods. © 2024 IEEE.

关键词： Additives

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Identifying player skill of dota 2 using machine learning pipeline

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Discover Artificial Intelligence 2024年第1期4卷 1-18页

作者： Pengmatchaya, Methasit Natwichai, Juggapong Data Science Consortium Chiang Mai University Huay Kaew Muang Chiang Mai50200 Thailand Department of Computer Engineering Faculty of Engineering Chiang Mai University Huay Kaew Muang Chiang Mai50200 Thailand

The esports industry is one of the prominent business sectors in the digital era, particularly, Multiplayer Online Battle Arena (MOBA) games which gain much attention from gamers and streaming audiences. Among such games, Defense of the Ancient 2 or Dota 2 is the record holder for the highest prize esports tournament. Therefore, various companies and investors start their esports teams to compete in the Dota 2 tournaments, the Internationals. To success in the competition, player recruitment is a crucial process as it usually takes considerable effort to find a skillful player. Watching the game replay to evaluate the player’s skill is one of the approaches. However, it can be too exhaustive, also some player’s ranking, which represent the player’s skill, are often not available. In this paper, we propose an effective machine learning pipeline to evaluate the player’s skill. Our designed pipeline includes data collection, feature engineering, and machine learning modeling. We show the data collection process using open-source API. An effective method for feature engineering is proposed. Features, e.g., end-game, or tactical decision related statistics, are incorporated along with the resource in the game distribution, harassment tactic, or spatiotemporal features, in order to provide effective models. Subsequently, we apply major machine learning models based on a single game data, i.e., logistic regression and random forest, to the processed data. The most effective model can achieve up to 0.7091 precision, 0.5850 recall, 0.6411 F1-score, and 0.8123 ROC AUC score. © The Author(s) 2024.

关键词： Pipelines

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An Improved Sparrow Search Algorithm for Node Localization in WSN

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computers, Materials & Continua 2022年第4期71卷 2037-2051页

作者： RThenmozhi Abdul Wahid Nasir Vijaya Krishna Sonthi TAvudaiappan Seifedine Kadry Kuntha Pin Yunyoung Nam Department of Computer Science and Engineering College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Electronics Communication and Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502Andhra PradeshIndia Department of Computer Science and Engineering K.Ramakrishnan College of engineeringTiruchirappalli621112India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of ICT Convergence Soonchunhyang UniversityKorea Department of Computer Science and Engineering Soonchunhyang UniversityKorea

Wireless sensor networks(WSN)comprise a set of numerous cheap sensors placed in the target region.A primary function of the WSN is to avail the location details of the event occurrences or the node.A major challenge in WSN is node localization which plays an important role in data gathering *** GPS is expensive and inaccurate in indoor regions,effective node localization techniques are *** major intention of localization is for determining the place of node in short period with minimum *** achieve this,bio-inspired algorithms are used and node localization is assumed as an optimization problem in a multidimensional *** paper introduces a new Sparrow Search Algorithm with Doppler Effect(SSA-DE)for Node Localization in Wireless *** SSA is generally stimulated by the group wisdom,foraging,and anti-predation behaviors of ***,the Doppler Effect is incorporated into the SSA to further improve the node localization *** addition,the SSA-DE model defines the position of node in an iterative manner using Euclidian distance as the fitness *** presented SSA-DE model is implanted in MATLAB *** extensive set of experimentation is carried out and the results are examined under a varying number of anchor nodes and ranging *** attained experimental outcome ensured the superior efficiency of the SSA-DE technique over the existing techniques.

关键词： Localization wireless networks

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An Evolutionary Algorithm for the Travelling Sales Man problem 3

An Evolutionary Algorithm for the Travelling Sales Man probl...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Waseem, Shaik Mohammad Jeshwanth, N.L. Lingesh, A. Jayanth, Matta Bhargavi, C.H. Dhanwanth, Batini Dept. of Computer Science and Engineering Chennai India Dept. of Artificial Intelligence and Data Science Chennai India Sai Spurthi Institute of Technology Dept. of Computer Science and Engineering Gangaram Sathuaplly India Jönköping University Dept. of Ai Engineering Jönköping Sweden

ISBN: (纸本)9798331509675

This study explores the use of Genetic Algorithms (GA) to solve the NP-hard combinatorial optimization problem known as the Travelling Salesman Problem (TSP). The suggested GA, called GA-P, performs better than conventional techniques because it uses a new randomized crossover threshold. According to experimental assessments, GA-P outperforms a normal Genetic Algorithm with fixed crossover (GA-N, 628.6) and approaches the best Branch and Bound solutions (B&B, 560.6) with an average cost of 617.4 for 16-node graphs. With average execution times of 0.1347 seconds for 16 nodes, GA-P also demonstrates constant computational efficiency, outperforming B&B's exponentially growing temporal complexity (169.37 seconds for 16 nodes). In order to balance solution quality and computational cost, hyperparameter tweaking revealed the ideal values of population percentage (pp = 1.1), crossover proportion (cp = 0.8), mutation threshold (mt = 0.3), and exploration probability (ep = 0.2). The results show that GA-P is a scalable and efficient substitute for TSP, with room for improvement via parallelization and exploratory techniques. © 2025 IEEE.

关键词： Traveling salesman problem

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An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

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computers, Materials & Continua 2024年第7期80卷 389-425页

作者： Nidhi Parashar Prashant Johri Arfat Ahmad Khan Nitin Gaur Seifedine Kadry School of Computer Science and Engineering Galgotias UniversityGreater Noida203201India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates MEU Research Unit Middle East UniversityAmman11831Jordan Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction *** learning(DL)and machine learning(ML)models effectively deal with such *** research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March *** addition,it analyses the effectiveness of various input parameters considered in crop yield prediction *** conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop *** total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is *** conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research *** study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel *** also discuss the ethical and social impacts of AI on ***,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven ***,thorough research is required to deal with challenges in predicting agricultural output.

关键词： Machine learning crop yield prediction deep learning remote sensing long short-term memory time series prediction systematic literature review

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Few-shot image semantic segmentation based on meta-learning: A review

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 351-367页

作者： Liu, Yu Zhu, Ye Chong, Haoze Yu, Ming School of Electronic and Information Engineering Hebei University of Technology Tianjin China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China College of Computer and Information Engineering Tianjin Agricultural University Tianjin China

Deep learning-based image semantic segmentation approaches heavily rely on large-scale training datasets with dense annotations and often suffer from scarce semantic labels for unseen categories. This limitation has spurred a research trend in Few-shot image Semantic Segmentation (FSS), which makes it possible to segment objects of new categories using only a few labeled samples. Although more and more FSS methods are emerging and gradually integrated into practical applications, a deep understanding of its achievements and issues is still missing. In this survey, we focus on the recent developments of FSS, specifically on FSS methods based on meta-learning. According to different network architectures, we summarize the related research into three classes, that are Convolutional Neural Network-based (CNN-based) models, Graph Neural Network-based (GNN-based) models, and Transformer-based models. Then, we explore the specific implementations of these models, including parameter-based methods, metric-based methods, attention-based methods, and optimization-based methods. Furthermore, we illustrate datasets and analyze the experimental results of various kinds of methods. Toward the end of the paper, we discuss the limitations of FSS and present its applications and challenges to provide further research directions. © 2024 - IOS Press. All rights reserved.

关键词： Semantic Segmentation

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AI Sovereignty in Autonomous Driving: Exploring Needs and Possibilities for Overcoming Challenges

AI Sovereignty in Autonomous Driving: Exploring Needs and Po...

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2023 IEEE Silchar Subsection Conference, SILCON 2023

作者： Sinha, Koesha Misra, Bitan Chakraborty, Sayan Dey, Nilanjan Department of Statistics and Data Science Bangalore India Techno International New Town Department of Computer Science Engineering Kolkata India

ISBN: (纸本)9798350314144

With the development of artificial intelligence, advancements in navigation systems for self-driving cars have become a new direction over the last decade. The inclusion of AI-driven actuators in autonomous vehicles has broken the barriers in terms of real-time high-quality data processing resources, accuracy of decisive actions and generalization of environment-action pairs. Upgradation from a car with no automation to a car with minimal to no human intervention has become a boon of AI, as it resolves most of the transportation problems on roads, including human error, lack of visibility in adverse weather conditions, tiredness of drivers in long journeys, etc. This study focuses on AI-enabled tasks, including object detection and identification, lane detection, notification for lane departure and reinforcement learning from the operational environment. However, there exist serious issues in deploying AI-empowered modules in autonomous cars, as the consumer rights to explain, trustworthiness, and reliability of the machine have not yet met the requirements. Our work explores the needs and prospects of AI sovereignty in autonomous driving by overcoming the aforementioned issues so that the healthy progress of technological society can take care of the future world. © 2023 IEEE.

关键词： Autonomous vehicles

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Classification of Breast Cancer Using Computational Machine Learning Algorithms 8th

Classification of Breast Cancer Using Computational Machine ...

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Gagana, P. Aladakatti, Darshan Kodipalli, Ashwini Rao, Trupthi Kamal, Shoaib Electronics and Communication Engineering Global Academy of Technology Bangalore India Computer Science and Engineering Global Academy of Technology Bangalore India Artificial Intelligence and Data Science Engineering Global Academy of Technology Bangalore India

ISBN: (纸本)9789819976324

Cancer is a disease that brings fear in everyone’s heart. In the top 5 cancers listed by Stephanie Watson and reviewed by Medically Reviewed by Jennifer Robinson, says that breast cancer stands in the top third position among other dreadful cancers like Prostate Cancer, Pancreatic Cancer, Colorectal Cancer, and Lung Cancer. Numerous kinds of research are made on this terrifying disease yet millions and millions of women have lost their precious life due to breast cancer. A huge number of patients have been victimized by this curse on women. In severe cases, doctors fail to give hope and treatment as the patient’s conditions reach beyond medical help. We all have heard the quote "Prevention is better than cure", well in our case earlier detection is better than late realization. As earlier as modern-day treatments are based on the concept of molecular heterogeneity, which is why they can be influenced by various features. Early breast cancer, usually confined to the breast, is considered a curable disease. This stage is commonly known as benign, which in our dataset is represented as however when the symptoms are not detected and treated at the early stage the treatment becomes more complicated and cannot to treated using the present therapeutic options and might have an associated risk of spreading to other parts of the body. This condition is called malignant and this is represented as M in the dataset. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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