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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Deepak, B.N. Biradar, Mallanagouda Joshi, Vijayalaxmi Pawar, Krunal B. Department of Information Science and Engineering SJB Institute of Technology Bangalore India Department of Computer Science and Engineering Sharnbasva University Kalaburagi India Department of Computer Science and Engineering SJB Institute of Technology Bangalore India Pune India

ISBN: (纸本)9798331504960

Routing and clustering approaches can assist address the primary issue of dependable energy-aware data transmission in wireless sensor networks (WSN). Data transmission's use in WSN situations has grown recently due to its dynamic nature. However, WSNs face a number of difficulties when sending data, such as a shorter network lifetime, lower EE, and a worse level of security. This study develops a new cluster head selection model for WSN. In this case, a new technique called Red Panda Incorporated Lyrebird Optimization (RPI-LBO) is used to elect the CHs in the best possible way. Energy, link longevity, link quality, security, and trust are all taken into account when performing the CHS. The link lifetime for 100 nodes using RPI-LBO is 42 from the 1200th to the 1800th round. However, the suggested RPI-LBO revealed a longer connection lifetime than the examined LBO, RPA, WaOA, SBOA, and COA schemes for all node counts. © 2024 IEEE.

关键词： Clustering algorithms

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Favourable subpopulation migration strategy for travelling salesman problem

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International Journal of Business Intelligence and Data Mining 2022年第3期20卷 299-326页

作者： Chandar, Abhishek Srinivasan, Akshay Anand, G. Paavai Department of Computer Science and Engineering SRM Institute of Computer Science and Technology Tamil Nadu Chennai600026 India

Most of the existing migration strategies are complex in nature which results in high computation time. Since the introduction of parallel genetic algorithms, they are known for obtaining better optimal solutions at the cost of more computation. Migration has played a significant role in maintaining genetic diversity of the population. This paper aims to propose a new migration strategy that improves the performance of the parallel genetic algorithm in solving NP hard problems. In order to assess its behaviour, various travelling salesman problem datasets are considered since it is a perfect example of an NP hard problem. The algorithm is based on an island model approach where one subpopulation is nurtured by the other subpopulations by taking turns during the migration process to progressively reach the optimal solution. In turn, the nurtured subpopulation provides a feedback loop to the subpopulation with poor fitness value based on certain criteria to be met. An extensive study was conducted to understand the optimal parameters values in which the proposed algorithm works best. © 2022 Inderscience Enterprises Ltd.

关键词： Traveling salesman problem

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Secured Data Storage Using Deduplication in Cloud Computing Based on Elliptic Curve Cryptography

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computer Systems science & engineering 2022年第4期41卷 83-94页

作者： N.Niyaz Ahamed N.Duraipandian Department of Computer Science and Engineering Velammal Engineering CollegeChennai600066India

The tremendous development of cloud computing with related technol-ogies is an unexpected ***,centralized cloud storage faces few chal-lenges such as latency,storage,and packet drop in the *** storage gets more attention due to its huge data storage and ensures the security of secret *** of the developments in cloud storage have been positive except better cost model and effectiveness,but still data leakage in security are billion-dollar questions to *** data security techniques are usually based on cryptographic methods,but these approaches may not be able to with-stand an attack from the cloud server's ***,we suggest a model called multi-layer storage(MLS)based on security using elliptical curve cryptography(ECC).The suggested model focuses on the signiﬁcance of cloud storage along with data protection and removing duplicates at the initial *** on divide and combine methodologies,the data are divided into three ***,theﬁrst two portions of data are stored in the local system and fog nodes to secure the data using the encoding and decoding *** other part of the encrypted data is saved in the *** viability of our model has been tested by research in terms of safety measures and test evaluation,and it is truly a powerful comple-ment to existing methods in cloud storage.

关键词： Cloud storage deduplication fog computing and elliptic curve cryptography

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Developed an IoT-based smart solar energy monitoring system for environmental sustainability 3

Developed an IoT-based smart solar energy monitoring system ...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Mamun, Abdullah Al Shuvo, Mehedi Hasan Islam, Towhidul Islam, Dwin Islam, Md. Jakirul Tanvir, Faysal Amin DUET Department Of Computer Science and Engineering Gazipur Bangladesh Lamar University Department of Electrical and Computer Engineering TX United States

ISBN: (纸本)9798350388282

Currently, electricity demand is constantly increasing all over the world, and the demand for this electricity is much higher than the production. As a result, the whole world is facing a global problem. In this decade, many researchers have been concerned about it and have suggested many ways to generate electricity in several ways, but they are not all concerned about low-cost, user-friendly, reliable data loggers and monitoring systems. To resolve the issue, we proposed an intelligent and environment-friendly way to generate electricity in all areas of a developing country. Initially, heat is generated either through household cooking activities or by burning excess or unnecessary garbage. After that, the solar panels are charged from the generated heat, and the charge is stored in a battery from the solar panel. The data logger, which is based on the ESP32 microcontroller, records all monitoring parameters on the cloud and presents them in the Android application. All the processes are completed automatically without any human interaction, and burning unnecessary garbage establishes environmental sustainability. The data logger hardware prototype that has been created incorporates four sensors to measure temperature, humidity, voltage, and current, which show up on an Android application with real-time data on many metrics. Additionally, it provides maintenance professionals with crucial information about any problems related to battery charging. To complete the work, we have created the hardware implementation of photovoltaic systems that use the IoT for remote data collection to provide us sustainability, and this prototype's total cost is affordable. © 2024 IEEE.

关键词： Sustainable development

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Employing Vision Transformer for Monocular Depth Estimation 2

Employing Vision Transformer for Monocular Depth Estimation

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Chaudhary, Anubhav Tripathi, Dev Brat Mishra, Harsh Pathak, Prasoon Dixit, Ashish Department of Computer Science and Engineering Ajay Kumar Garg Engineering College Ghaziabad India

ISBN: (纸本)9798331530389

Monocular depth estimation (MDE) is an important task in computer vision, it enables a range of applications like robotic navigation, augmented reality and also used in surgical guidance. This paper shows the use of ViTs for MDE and focuses on the Depth Anything V2(DA-V2) model. DAV2 model leverages a teacher-student training approach and uses the synthetic and unlabeled real images which are designed to enhance the accuracy of depth estimation. DA-V2 is fine-tuned on EndoSLAM dataset and contains challenging visual scenes from endoscopic images of the stomach, colon and small intestine to check the adaptability of the model to medical depth estimation. Our experiment shows that the fine-tuned DA-V2 outperforms all the conventional models like Endo-SfMLearner and achieves reductions in root mean squared error(RMSE) and absolute relative error, across all anatomical datasets. DA-V2 achieves the RMSE values 1.245, 1.028, and 2.021 for the stomach, colon, and intestine datasets, respectively as compared to the SfMLearner's 6.387, 6.657, and 14.787. The findings show that ViTs are capable of achieving higher accuracy and generalization in monocular depth estimation tasks. This study highlights the effectiveness of DA-V2 and Vision Transformers in Monocular Depth Estimation and opens up possibilities for further model optimization. © 2025 IEEE.

关键词： Convolutional neural networks

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Unmasking offensive content: a multimodal approach with emotional understanding

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Multimedia Tools and Applications 2025年 1-24页

作者： Singh, Gopendra Vikram Ghosh, Soumitra Firdaus, Mauajama Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta Bihar Patna801103 India Fondazione Bruno Kessler Povo Trento38123 Italy University of Alberta Edmonton Alberta Canada Department of Computer Science and Engineering Indian Institute of Technology Bombay Powai Maharashtra Mumbai400076 India

The widespread use of social media has brought people from all over the world closer, but it has also become a platform for spreading hate, offensive content, and misinformation. Various forms of media, such as text, images, audio, and video, are used to disseminate information. In this paper, we tackle the task of identifying offensive and non-offensive text in the context of women’s harassment, considering the intensity of the offense. We propose a novel approach that leverages sentiment and emotion, including their intensity, to enhance the performance of this task. To facilitate our research, we constructed a large-scale dataset called the Women and Child-related Sentiment and Emotion-Aware offensive dataset (WCSEoff) to aid in identifying offensive content. Furthermore, we develop a multi-task model that not only detects whether images contain offensive or non-offensive content and quantifies the intensity of offensiveness as the primary task but also classifies the sentiment and emotions present, along with their respective intensities, as the auxiliary task. By adopting this multitasking framework, we achieve a performance improvement of over 7% compared to approaches that do not incorporate sentiment and emotion analysis. Code and data will be available at https://***/~ai-nlp-ml/***#MOSAIC. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Classification (of information)

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REAL-TIME PSYCHOACOUSTIC FREQUENCY MASKING COMPENSATION FOR AUDIO SIGNALS WITH OVERLAPPING SPECTRA 21

REAL-TIME PSYCHOACOUSTIC FREQUENCY MASKING COMPENSATION FOR ...

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21st Sound and Music Computing Conference, SMC 2024

作者： Presti, Giorgio Degiorgi, Nicola Fresia, Amedeo Servetti, Antonio Department of Computer Science University of Milan Italy Control and Computer Engineering Department Politecnico di Torino Italy

ISBN: (纸本)9789893520758

This work presents a prototype for the automatic and real-time psychoacoustic frequency masking effect compensation. This tool is intended to handle audio signals with overlapping spectra coming from two different mono or stereo sources in order to emphasise those frequencies of a main source that the second one masks. The goal is achieved using a dynamic equaliser controlled by a function of the differences of the input spectra psychoacoustic models. The tool has been prototyped as a standard Virtual Studio Technology (VST3) plugin and its effectiveness has been tested with a user study carried out in the context of music production. © 2024. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 Unported License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original.

关键词： Psychoacoustic

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Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

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computer Systems science & engineering 2022年第5期41卷 511-524页

作者： P.Rajeswari K.Jayashree Department of Computer Science and Engineering Rajalakshmi Engineering CollegeChennai602105India

Recent advancements in cloud computing(CC)technologies signified that several distinct web services are presently developed and exist at the cloud data ***,web service composition gains maximum attention among researchers due to its significance in real-time *** of Service(QoS)aware service composition concerned regarding the election of candidate services with the maximization of the whole *** these models have failed to handle the uncertainties of *** resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by *** the other hand,trip planning is an essential technique in supporting digital map *** aims to determine a set of location based services(LBS)which cover all client intended activities quantified in the *** the available web service composition solutions do not consider the complicated spatio-temporal *** resolving this issue,this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model(F3L-WSCM)in a cloud environment for location *** presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking,hotels,car rentals,*** the next stage,the firefly algorithm is applied to generate composition plans to minimize the number of composition *** by,the fuzzy subtractive clustering(FSC)will select the best composition plan from the available composite ***,the presented F3L-WSCM model involves four input QoS parameters namely service cost,service availability,service response time,and user *** extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy,execution time,and efficiency.

关键词： Web service composition trip planning hybrid models firefly algorithm QoS aware services fuzzy logic

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Blockchain-Based Knowledge Repository for Training Artificial Intelligence Models: Bridging AIML with Decentralized Data

Blockchain-Based Knowledge Repository for Training Artificia...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Das, Amit Kumar Tonoy, Md. Thouhedul Alam Hossain, Meherab Chandigarh University Department of Computer Engineering Mohali India United International University Department of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350364866

This paper presents a Blockchain-Based Knowledge Repository (BBKR) tailored for the secure storage and management of training data crucial for Artificial Intelligence and Machine Learning (AIML) models. Leveraging Ethereum blockchain technology and smart contracts, the BBKR ensures data integrity, enhances security measures, and promotes decentralization principles. The proposed mechanism includes a detailed explanation of data validation processes, decentralized access control, and scalable transaction mechanisms. Our empirical evaluations validate the efficacy of BBKR in upholding data integrity, enhancing scalability, and fortifying security provisions. The results show significant improvements in data management performance compared to traditional centralized repositories. This research underscores the transformative potential of BBKR in revolutionizing AIML data management paradigms and offers a robust foundation for further advancements in the field. Future work will explore real-world applications and potential enhancements using advanced cryptographic techniques. © 2024 IEEE.

关键词： Artificial Intelligence and Machine Learning (AIML) Blockchain blockhain-Based Knowledge Repository (BBKR) Data integrity Decentralization Ethereum Security Smart Contracts Training data

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DDoS Attacks Detection in IoT Networks using Naive Bayes and Random Forest 3

DDoS Attacks Detection in IoT Networks using Naive Bayes and...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Srivastava, Animesh Tiwari, Shweta Rawat, Bhupender Singh Dhondiyal, Shiv Ashish Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Institute of Engineering Chandigarh University Department of Computer Science & Engineering Department / University Punjab Gharuan Mohali India Coer University College of Smart Computing Roorkee India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350375190

The proliferation of Internet of Things (IoT) devices has resulted in numerous benefits, including streamlined tasks and improved connectivity but has also raised concerns regarding security vulnerabilities. Distributed Denial of Service (DDoS) attacks targeting IoT systems pose a significant threat, potentially disrupting operational activities. This study conducts a comparative analysis of existing methodologies to detect DDoS attacks in IoT environments, focusing on the efficacy of machine learning algorithms. Specifically, this study evaluates the performance of Random Forest and Naive Bayes classifiers in detecting DDoS attacks using an IoT dataset. The evaluation involves training and testing these classifiers and assessing their accuracy as a key performance metric. Empirical findings demonstrate enhanced precision in identifying DDoS attacks in IoT environment, indicating the effectiveness of the proposed approach. By leveraging machine learning algorithms, particularly Random Forest and Naive Bayes, traditional techniques for detecting and mitigating DDoS attacks in IoT environments are accelerated, thus enhancing the security of IoT systems. © 2024 IEEE.

关键词： Internet of things

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