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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： R Padma Vamsidhar Yendapalli A P Jyothi Computer Science and Engineering GITAM School of Technology GITAM University Bengaluru India Computer Science and Engineering Ramaiah University of Applied Sciences Bengaluru India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

In the current technological landscape, the transmis-sion of data through communication channels has experienced significant advancements. The rapid growth in communication technology has brought about improved outcomes. However, challenges arise in ensuring the security and integrity of the transmitted data. It becomes crucial to safeguard from unauthorized access or users without proper authorization, it is important to implement various security parameters and best practices. This need has led to the development of various data hiding techniques. The objective of this paper is to conduct a comprehensive study on the taxonomy of Steganography and Watermarking types, as well as the taxonomy of techniques employed in Steganography and Watermarking. The paper aims to provide an overview and classification of different types of Steganography and Watermarking techniques. It also focuseson analyzing the advantages and disadvantages associated with these *** examining the taxonomy and assessing the pros and cons of Steganography and Watermarking techniques, this paper aims to contribute to a better understanding of their applications and implications in data security. The findings of this study can serve as a valuable resource for researchers and practitioners working in the field of data hiding and information security.

关键词： Knowledge engineering Steganography Technological innovation Wavelet domain Frequency-domain analysis Taxonomy Information security

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Collaborative Search With Knowledge Sharing And Summarization

Collaborative Search With Knowledge Sharing And Summarizatio...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Venkateshwaran Mohan Sivaganesan D Balasubramaniam S Vikhas S G Dhinesh S P Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore India Department of Computer Science and Engineering Dr Mahalingam College of Engineering and Technology Pollachi India Department of Computer Science and Engineering PSG College of Technology Coimbatore India Department of Computer Science Sri Krishna Arts and Science College Coimbatore India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

A collaborative search engine is used for a group of users who want to search information together for various group activities. This will be time saving as the users can split the work between them. Summarization is the process of condensing a document into a shorter version which will contain the important details about the document. This will be useful to quickly get the main ideas without having to go through the entire document. Our main aim is to combine collaborative search with document summarization so that it will be helpful when users in a group want to quickly read the summary of the documents whenever required. In the collaborative search, the results will be re-ranked based on the user's interaction with the system, so that feedback driven ranking of results is attained and the users in a session will be able to view pages that were even ranked by their co-mates. This re-raking process is done by obtaining the user's interest on a particular page using decision logic and updating it in the priority queue. User's meta-data like page size, click-through rate, time spent and scrolling speed of the document will be calculated and these will be used as parameters for the decision logic to find the interest level of the user. Extractive summarization is implemented with the concept of Maximum Independent Set. The raw text document is first converted into a graph having its sentences as nodes and the edge weight is the similar instances between sentences. Then graph MIS is identified and removed after which the top-n sentences are fetched upon its eigenvector centrality value and the summary is generated.

关键词： Collaboration Search engines Logic Expert systems

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Mining High Utility-probability Sequential Patterns in Bigdata Environments 14th

Mining High Utility-probability Sequential Patterns in Bigd...

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Wu, Jimmy Ming-Tai Liu, Shuo Wu, Mu-En Lin, Jerry Chun-Wei College of Computer Science and Engineering Shandong University of Science and Technology Shandong China National Taipei University of Technology Taipei Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

ISBN: (纸本)9789811684296

High utility-probability sequential pattern mining (HUPSPM) has become a novel research errand realm in data mining. In practical application, most of the data are collected by a variety of sensors, forming an uncertain database. Therefore, the data collected in real life can be expressed in terms of probability. In previous research work, for the sake of heightening the performance of mining in small data sets and single machine, a great deal of algorithm have been posed. Unfortunately, these algorithms have loopholes in mining large databases with mickle records. Therefore, it is very urgent to construct an efficient model to deal with large uncertain dateabas data sets. In this article, a MapReduce framework is moved to mine effective information in multitudinous uncertain data sets and reduce the number of database scans. Experiments on a large number of uncertain data sets show that the posed Hadoop algorithm has a strong ability to mine the high utility-probability sequential pattern. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Probability

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Data Availability Optimization for Cyber-Physical Systems 15

Data Availability Optimization for Cyber-Physical Systems

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Li, Liying Cong, Peijin Zhou, Junlong Gu, Zonghua Li, Keqin School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Department of Applied Physics and Electronics Ume˚a University Sweden Department of Computer Science State University of New York United States

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

As the backbone of Industry 4.0, Cyber-Physical Systems (CPSs) have attracted extensive attention from industry, academia, and government. Missing data is a common problem in CPS data processing and may cause incorrect results and eventually serious malfunction. Existing data availability optimization methods either rely on a large amount of complete training data or suffer from poor performance. To solve these problems, this paper proposes an iterative data availability optimization method for CPSs. Specifically, the proposed method first pre-processes the raw dataset by using a Singular Value Decomposition-based feature selection approach to identify crucial features and reduce computation overheads. It then makes an initial guess for missing values via a designed K-Means-based imputation approach. The appropriate initial estimation decreases the probability of the proposed method falling into the local optimum. Finally, the proposed method iteratively estimates missing data based on the Orthogonal Matching Pursuit algorithm. The proposed method optimizes data availability by accurately imputing missing values. Simulation results on two datasets demonstrate that compared to multiple state-of-the-art approaches, the proposed data availability optimization method can reduce imputation error by up to 99.65%. © 2022 IEEE.

关键词： Iterative methods

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Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuclei Instance Segmentation in Histology Images

Edge-SAN: An Edge-Prompted Foundation Model for Accurate Nuc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wu, Xuening Shen, Yiqing Zhao, Qing Kang, Yanlan Hu, Ruiqi Zhang, Wenqiang Fudan University Shanghai Engineering Research Center of Ai & Robotics Academy for Engineering & Technology Shanghai China Johns Hopkins University Department of Computer Science BaltimoreMD United States Hong Kong Polytechnic University Department of Applied Mathematics Hong Kong Fudan University Engineering Research Center of Ai & Robotics Ministry of Education Academy for Engineering & Technology School of Computer Science Shanghai China

ISBN: (纸本)9798350386226

Accurate nuclei segmentation is fundamental in histology image analysis, playing an essential role in cancer grading and diagnosis. However, this task remains challenging due to variations in staining protocols, heterogeneity among nuclei types, and the densely clustered nature of nuclei. While SAM exhibits zero-shot generalization capabilities in natural image segmentation, its performance degrades when applied to nuclei segmentation in histology images. Existing adaptations of SAM for medical imaging primarily focus on organ or lesion segmentation, which differs substantially from nuclei segmentation due to the unique characteristics of nuclei - specifically, their sparse distribution combined with dense clustering. To address these challenges, we propose Edge-SAN (Segment Any Nuclei with Edge Prompting), an interactive segmentation foundation model specifically designed for nuclei segmentation. Edge-SAN introduces a novel edge prompting method that enhances the delineation of nuclei boundaries, particularly among densely clustered nuclei, by leveraging edge information to improve segmentation accuracy. We evaluate Edge-SAN on 12 diverse datasets in both few-shot and zero-shot scenarios, demonstrating its effectiveness as a foundation model for nuclei segmentation, achieving 66.81% AJI and 73.13% DSC - improvements of 16.33% and 15.48% over SAM-Med2D, respectively. The code is available at https://***/deep-geo/Edge-SAN. © 2024 IEEE.

关键词： Deep Learning Digital Pathology Foundation Model Nuclei Segmentation Segment Anything Model

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Hand Sign Recognition using YOLOV5 13

Hand Sign Recognition using YOLOV5

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13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022

作者： Saji, Alen Charuvila Ramalakshmi, K. Senbagavalli, M. Gunasekaran, Hemalatha Ebenezer, Shamila Computer Science and Engineering K.I.T.S. India Computer Science and Engineering Alliance University Bangalore India Information Technology Alliance University Bangalore India IT Department University of Technology and Applied Sciences Oman

ISBN: (纸本)9781713857938

The deaf-mute community utilises sign language for interacting among themselves and others. The introduction of standard sign language has made their lives much easier. This paper proposes an effective hand-sign recognition method using a deep learning technique and is based on YOLOv5, which is a real-time object detection algorithm which detects a hand sign and outputs the corresponding text. The proposed model utilises various sub-models namely, Cross Stage Partial Network (CSPNet), Path Aggregation Network (PANet), Dense Prediction. This model can be conveniently deployed into an android application with a user-friendly interface. © Grenze Scientific Society, 2022.

关键词： Deep learning

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Performance Comparison of Various Dual-Band AMC Structures Using Textile Materials 13

Performance Comparison of Various Dual-Band AMC Structures U...

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13th International Electrical Engineering Congress, iEECON 2025

作者： Hengroemyat, Yotrawee Thaiwirot, Wanwisa Akkaraekthalin, Prayoot Chudpooti, Nonchanutt King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Industrial Physics and Medical Instrumentation Bangkok Thailand

ISBN: (纸本)9798331543952

This paper presents the performance comparison of various dual-band AMC structures using textile materials. The textile AMC structure includes of the inner patch and outer loop to achieve dual-band operation. The felt is used as substrate of AMC unit cell while the copper-nickel plated polyester fabric is used as conductive parts. The various geometries of AMC unit cell based on square, circle, hexagon, octagon shapes are designed to operate at 2.4/5.5 GHz for WBAN and Wi-Fi applications. Moreover, the performance of AMC unit cell structures is compared in term of size and bandwidth. It is found from simulation results that the inner square patch and outer square loop can provide compact size and wider bandwidth. The hexagon shape can achieve compact size when it is arranged in form of array. © 2025 IEEE.

关键词： Linen

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Multi-agent robotics system with whale optimizer as a multi-objective problem

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9637-9649页

作者： Gul, Faiza Mir, Imran Mir, Suleman Abualigah, Laith Electrical Department Air University Aerospace and Aviation Campus Kamra Pakistan School of Avionics and Electrical Engineering College of Aeronautical Engineering National University of Sciences and Technology Risalpur Pakistan Electrical Department Fast-National University of Computer and Emerging Sciences Peshawar Pakistan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information Technology Al al-Bayt University Mafraq25113 Jordan College of Engineering Yuan Ze University Taoyuan Taiwan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

A multi-objective optimization method for exploring uncharted space is presented in the study. Robotics exploration personnel create a map of the immediate area using sensor data. It has been observed that a single optimization technique is typically used with a specific objective function to carry out the optimization for space exploration. The optimization process can be sped up and simplified using a mono-objective function, but the map accuracy and exploration depth suffer. This work offers an optimization technique with improved multi-objective functions in recognition of this crucial factor. This not only speeds up the search procedure but also improves the precision of the maps. The Reconfigured Whale Algorithm (rWO), the suggested framework, is based on a Whale Optimizer inspired by whales’ biological behavior. Initializing the whale population, also known as waypoints, is the first step. After being established in the initial stage/iterations, these waypoints are considered constant. The location update from the robot-catered non-dominated waypoints is the next phase. The algorithm optimizes the waypoints. Extensive simulations that simulate various scenarios and environments are used to assess the performance matrices properly. Following the identification of the algorithm’s advantages, the effectiveness of the outcomes is demonstrated by contrasting its performance with that of three popular optimization algorithms: the hybrid CME-Whale Optimizer (WO), the Coordinated Multi-Robot Exploration (CME), and the Arithmetic Optimizer (AO) integrated with CME. Findings show that the suggested approach significantly improves the optimization process by expanding the area that is investigated and speeding up the search process. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Multiobjective optimization

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Machine Learning and Deep Learning Approaches for Guava Disease Detection

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SN computer science 2025年第4期6卷 1-28页

作者： Paramesha, K. Jalapur, Shruti Hanok, Shalini Puttegowda, Kiran Manjunatha, G. Kumara, Bharath Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysuru India Department of Computer Science and Engineering Christ University Karnataka Bengaluru India Department of Electronics and Communication Engineering ATME College of Engineering Karnataka Mysuru India Department of Electronics and Communication Engineering Vidyavardhaka College of Engineering Karnataka Mysuru India Department of CSD PES Institute of Technology and Management Karnataka Shivamogga India Department of ECE MS Ramaiah University of Applied Science (MSRUAS) Karnataka Bangalore India

A larger proportion of crops face disease outbreaks, making agricultural output difficult. Detecting and predicting diseases at an early stage can enhance productivity. Guava, a tropical and subtropical fruit, is cultivated in various countries. In regions such as Bangladesh, Pakistan, India, and South America, guava cultivation faces significant challenges due to diseases like Canker, Dot, Mummification, Phytophthora, Scab, and Styler and Root. Traditional diagnosis methods based on visual observation are often unreliable and time-consuming. To address this, we developed an automated system leveraging deep learning techniques. Our study utilized a dataset comprising 4046 guava leaf images categorized into these seven disease classes. We compared the performance of traditional methods with deep learning approaches using vision transformers and transfer learning. The results demonstrate the superiority of deep learning methods over traditional approaches, where traditional machine learning model SVM gave accuracy near 78% and deep learning methods gave over 90%. The transfer learning method gave an accuracy of nearly 97% and on the other hand, the vision transformer gave accuracy of 98%. This offers a promising solution for early disease detection in guava crops. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy Deep learning Guava disease Leaf images Machine learning

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FedHZone: Federated Learning Based Hazardous Zone Detection Using Internet of Drone Things

FedHZone: Federated Learning Based Hazardous Zone Detection ...

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2024 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2024

作者： Biswas, Payel De, Debashis Obaidat, Mohammad S. Hazra, Arnab Sadoun, Balqies Jogesh Chandra Chaudhuri College Department of Computer Science Kolkata India Maulana Abul Kalam Azad University of Technology Centre of Mobile Cloud Computing Department of Computer Science and Engineering West Bengal WB Nadia India School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China School of Computing SRM University Department of Computational Intelligence SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India Dr. Sudhir Chandra sur Institute of Technology & Sports Complex Department of Computer Science and Engineering Kolkata India College of Engineering Al Balqa ' Applied University Al-Salt19117 Jordan

ISBN: (纸本)9798350349832

Air pollution can affect human health, so it is necessary to predict the air quality index (AQI) in advance. In this work, air quality data collected by the Internet of Drone Things (IoDT) is predicted and analyzed to detect highly polluted zones using a distributed privacy-preserving federated learning (FL) approach. Air quality data for a particular zone is collected and then fed into a Long short-Term memory (LSTM) based model to predict the pollution level in local training. Global aggregation is done by an IoDT-mounted central server to aggregate the received models from the local IoDT level. Privacy-preserving FedHZone predicts and analyses the AQI values from the past time series dataset in a multi-drone environment. This work detects the most hazardous zone in a larger region of Jaipur, Rajasthan using real-Time data. The result shows FedHZone with stacked LSTM having ''2%-7% better accuracy than the vanilla LSTM-based model. © 2024 IEEE.

关键词： Federated learning

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