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Communication, Control and Intelligent Systems (CCIS)

作者： Balaji Vijayan V K S Manojee Mohammed Ibrahim M Nivethitha R Thamaraimanalan T Ranjithkumar M Department of Information Science and Engineering HKBK College of Engineering Bengaluru Karnataka India Department of Information Technology Kongunadu College of Engineering and Technology Trichy Tamilnadu India Department of Computer Science and Engineering Sona College of Technology Salem Tamilnadu India Department of Computer Science and Engineering K.S.R. College of Engineering Thiruchengode Tamilnadu India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Coimbatore Tamilnadu India Department of Computer Science and Business Systems Knowledge Institute of Technology Salem Tamilnadu India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

In underwater acoustic sensor networks (UASNs), effective congestion control is essential for ensuring reliable data transmission and resource efficiency, especially in the face of challenging environmental factors such as water velocity, pressure, temperature, and salinity. This paper introduces a novel Adaptive Fairness-Indexed Congestion Control (AFICC) mechanism that employs Q-learning to dynamically adjust transmission rates among nodes, ensuring equitable resource distribution and minimizing congestion. Our simulations, conducted with 100 nodes at depths of up to 1000 meters, reveal that AFICC significantly outperforms two existing algorithms: Rate-Based Congestion Control (RBCC) and Energy-Aware Congestion Control (EACC). Specifically, AFICC achieved a Packet Delivery Ratio (PDR) of 93%, compared to 85% for RBCC and 80% for EACC. Additionally, the End-to-End Delay was reduced to an average of 150 milliseconds with AFICC, while RBCC and EACC recorded delays of 220 milliseconds and 210 milliseconds, respectively. AFICC also demonstrated a 25% improvement in Energy Efficiency over EACC and maintained a Jain’s Fairness Index of 0.92, surpassing the 0.82 and 0.78 indices achieved by RBCC and EACC, respectively. These results confirm that AFICC offers a robust framework for congestion control in UASNs, effectively adapting to fluctuating underwater conditions while ensuring reliable communication and equitable resource allocation.

关键词： Q-learning Salinity (geophysical) Dynamic scheduling Energy efficiency Delays Temperature control Resource management Reliability Underwater acoustics Water resources

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Adaptive Multi-Resolution Framework for Scalable and Efficient Big Data Processing 7

Adaptive Multi-Resolution Framework for Scalable and Efficie...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Asha, V. Rangaiah, Yagateela Pandu Nijhawan, Ginni Albawi, Ali Rani, K. Pushpa Kalyani, Susmita New Horizon College of Engineering Bangalore India Institute of Aeronautical Engineering Department of Electronics and Communication Enfineering Dundigal Hyderabad500043 India Lovely Professional University Phagwara India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201315 India

ISBN: (纸本)9798350350067

The increasing applicability of the big data in different domains require the formulation of effective data collection and analysis framework that can accommodate complex and large datasets. This paper proposes the new DRAF that enhance both data aggregation and mining for large datasets. The Dynamic Resolution Adaptive Framework (DRAF) takes advantage of using multiple levels of representation of data, which makes it easy to expand the workload or scale down depending on the needs of the user. This proposed framework employs methods like hierarchical clustering, branching multi-level indexing and Adaptive Resolution Switching in order to optimize the trade-off of data accuracy and computational cost. Moreover, high level of detail is maintained in the DRAF, which supports mining necessary data and model details at multiple resolutions, making the DRAF specifically appropriate for operational environments where data come from numerous different sources and types. Substantial experiments in the book prove the efficiency of the DRAF-based algorithms in terms of speed and accuracy in comparison with other existing methods. The study establishes potentiality of the DRAF toward the application of big data problems in areas of high level of resolution and immediacy of decision making. © 2024 IEEE.

关键词： Data aggregation

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Innovative Strategies for Ensuring Data Protection and Maintaining Integrity in Digital Systems 7

Innovative Strategies for Ensuring Data Protection and Maint...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Prakash, Shiva Rangaiah, Yagateela Pandu Nagpal, Amandeep Bhardwaj, Garima Mounika, T. Sharma, Saurabh New Horizon College of Engineering Department of Mechanical Engineeering Bangalore India Institute of Aeronautical Engineering Department of Electronics and Communication Enfineering Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Amity University Amity Business School Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201314 India

ISBN: (纸本)9798350350067

Data security is difficult in the ever-changing digital world. Existing techniques may not provide comprehensive security, real-time hazard monitoring, and usercentred access management. Our response must be forceful and adaptable. The study led to the development of a five-part data safety technique. AEA, DTD, SIM, UCAC, and AAP are all part of this organisation. AAP makes identification fast and secure;AEA protects data;DTD detects hazards in real time;SIM tests security on a broad scale;and UCAC regulates access. Ablation can reveal systemic roles. New methods outperformed previous ones in some key aspects. Visual tools emphasise privacy, freedom, user experience, and connection to aid decisionmaking. In ever-changing digital situations, one may employ a powerful and adaptable data security solution. © 2024 IEEE.

关键词： Data integrity

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Advancing Image Segmentation, Analysis, and Feature Extraction Techniques 7

Advancing Image Segmentation, Analysis, and Feature Extracti...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Asha, V. Rangaiah, Yagateela Pandu Nijhawan, Ginni Kalyani, Susmita Rani, K. Pushpa Albawi, Ali New Horizon College of Engineering Bangalore India Institute of Aeronautical Engineering Department of Electronics and Communication Enfineering Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Uttar Pradesh Greater Noida201315 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India The Islamic University College of Medical Technology Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350350067

This study centers on review, feature extraction, and image segmentation, integrated through the Dual-Path Convolutional Neural Network (DP-CNN). The DP-CNN is designed to accurately identify objects in images, even in the presence of intricate lines and discontinuities. The MAMAM technique efficiently prioritizes critical image regions, expediting the review process and enhancing accuracy in image analysis. Additionally, feature extraction is refined using the Adaptive Feature Selection (AFS) method, facilitating precise and adaptable image processing. Each component of this approach is systematically developed to meet the research objectives. The combined methodology demonstrates robustness and versatility, proving effective across diverse and challenging data scenarios, thereby presenting a promising solution for rapid and accurate complex image processing tasks. © 2024 IEEE.

关键词： Convolutional neural networks

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Reconsidering Feature Structure Information and Latent Space Alignment in Partial Multi-label Feature Selection

arXiv

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arXiv 2025年

作者： Pan, Hanlin Liu, Kunpeng Gao, Wanfu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Computer Science Portland State University PortlandOR97201 United States

The purpose of partial multi-label feature selection is to select the most representative feature subset, where the data comes from partial multi-label datasets that have label ambiguity issues. For label disambiguation, previous methods mainly focus on utilizing the information inside the labels and the relationship between the labels and features. However, the information existing in the feature space is rarely considered, especially in partial multi-label scenarios where the noises is considered to be concentrated in the label space while the feature information is correct. This paper proposes a method based on latent space alignment, which uses the information mined in feature space to disambiguate in latent space through the structural consistency between labels and features. In addition, previous methods overestimate the consistency of features and labels in the latent space after convergence. We comprehensively consider the similarity of latent space projections to feature space and label space, and propose new feature selection term. This method also significantly improves the positive label identification ability of the selected features. Comprehensive experiments demonstrate the superiority of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature extraction

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Next-Generation Computational Models for Enhanced Interpretation of Biospeckle Images Leveraging Deep Learning and Fourier Transform Techniques in Biomedical Applications 7

Next-Generation Computational Models for Enhanced Interpreta...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gowtham Raj, R. Sreekanth, S. Nijhawan, Ginni Gupta, Shubhi Sharma, Saurabh Mounika, T. New Horizon College of Engineering Deparment of Mechanical Engineering Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lloyd Law College Plot No. 11 Knowledge Park II Greater Uttar Pradesh Noida201314 India Lovely Professional University Phagwara India Amity University Amity School of Engineering & Technology Greater Noida India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350350067

This study looks at advanced computational techniques that enhance the understanding of biospeckle images in biological contexts. Our models use Fourier transform and deep learning techniques to achieve improved specificity, recall, accuracy, and precision. The Fourier transform may provide a more accurate description of frequency domain data as biological processes evolve over time. Deep learning algorithms recognise patterns in biospeckle images, facilitating analysis, memory, and response. The analysis research delineates the several components that influence the outcome and provides evidence of their respective roles. This research shows how helpful the models are and offers suggestions on how to make them better. Non-invasive biomedical testing uses these computer models to illustrate the temporal evolution of biological cells. Our techniques improve the interpretation of biospeckle images, potentially revolutionising non-invasive biomedical imaging. Future researchers may evaluate these models using different datasets and testing conditions. This guarantees their adaptability and power in biological contexts. © 2024 IEEE.

关键词： Frequency domain analysis

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Unified Security Framework for Cloud Applications with IoT Fusion via Decentralized Blockchain

Unified Security Framework for Cloud Applications with IoT F...

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Applied Artificial Intelligence and Computing (ICAAIC), International Conference on

作者： B Mohanraj M Kalpana A M Priya D Abdul Jaleel N Karthikeyan A A Velusamy Department of Information Technology Sona College of Technology Salem India Department of Computer Science Engineering Government College of Engineering Salem India Department of Information Technology Knowledge Institute of Technology Salem India Department of Computer Science & Technology Madanapalle Institute of Technology & Science Madanapalle India Department of CSE - Artificial Intelligence Sri Venkateshwara College of Engineering Vidyanagar India

ISBN: (数字)9798350375190

ISBN: (纸本)9798350375206

Recent advancements in blockchain-based cloud computing highlight its potential in providing robust data security, integrity, and confidentiality. While cloud computing is increasingly utilized for remote resource access, it presents significant security challenges that necessitate innovative solutions. This research proposes a novel SecureCloudChain IoT (SCC-IoT) architecture, which integrates blockchain technology into cloud-supportive platforms to enhance security measures. The SCC-IoT architecture includes a distributed blockchain approach to ensure data security while maintaining privacy and integrity. This system is designed to be adaptable and scalable, meeting real-world requirements effectively. By promoting a decentralized and efficient environment, SCC-IoT improves the reliability, stability, and load balancing of cloud services. Performance assessments through various experimental conditions have been conducted to evaluate the proposed system. The results demonstrate SCC-IoT’s effectiveness in mitigating potential attacks and ensuring secure and reliable cloud computing. This research highlights the significance of integrating blockchain technology into cloud platforms, paving way for more secure and efficient cloud computing solutions.

关键词： Cloud computing Data privacy Data security computer architecture Load management Blockchains Reliability

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Study of a nonlinear Schrodinger equation with truncated M proportional derivative

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Optik 2023年第1期290卷

作者： Abdel-Gawad, Hamdy I. Sulaiman, Tukur Abdulkadir Ismael, Hajar Farhan Mathematics Department Faculty of Science Cairo University Cairo Egypt Department of Computer Engineering Biruni University Istanbul34010 Turkey Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Mathematics Faculty of Science University of Zakho Zakho42002 Iraq Department of Computer Science College of Science Knowledge University Erbil44001 Iraq

In this work, we introduce a novel truncated M proportional (T-MP) derivative and consider a T-MP perturbed derivative nonlinear Schrodinger equation (PDNLSE). The PDNLSE is a nonlinear model which arises in nano optical fibers (photonic nanowires). While the DNLSE exhibits self-steepening (SS), the PDNLSE exhibits self-phase modulation (SPM) and Raman scattering (RS) effects, Here, the exact solutions of the PDENLSE are derived, here, by implementing the unified method. These solutions are displayed in graphs. It is found that a sufficient condition for a hyper-chaotic solution to hold is that an elliptic function solution exists. Non elliptic solutions may be, also, hyper-chaotic. It is worth mentioning that hyper chaotic may occur in economic and financial mathematics. Nonchaotic solutions exhibit many geometric structures, chirped solitons, and rhombus-shaped and M-shaped solitons. It is found that modulation instability triggers when the coefficient of the third-order dispersion exceeds a critical value. Further, the global bifurcation is investigated via phase portrait by constructing the Hamiltonian function. © 2023 Elsevier GmbH

关键词： Solitons

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Utilizing Deep Reinforcement Learning for Advanced Pattern Extraction in Big Data Analytics and Ontology Systems 2

Utilizing Deep Reinforcement Learning for Advanced Pattern E...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Karthik, A. Asha, V. Bhardwaj, Garima Nijhawan, Ginni Khan, Irfan Bader, Nabaa M. Institute of Aeronautical Engineering Dundigal Hyderabad India New Horizon College of Engineering Department of Computer Applications Bangalore India Amity University Amity Business School Uttar Pradesh Greater Noida India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Uttar PradeshKnowledge Park II Greater Noida201306 India National University of Science and Technology College of Technical Engineering Dhi Qar Iraq

ISBN: (纸本)9798350384277

The development of big data has provided unparalleled prospects for uncovering novel patterns and insights in several domains. However, the complex structure and volume of data need the use of advanced methods to successfully extract significant information. This research presents a new framework that utilizes Deep Reinforcement Learning (DRL) to improve pattern extraction in big data analytics and ontology systems. DRL, which combines deep learning with reinforcement learning, excels at dealing with data spaces that have a large number of dimensions. This makes it a good option for effectively exploring and analyzing complex datasets. The suggested framework effectively utilizes sophisticated ontological structures to integrate DRL, enabling it to accurately identify and extract complex patterns that may be overlooked by standard approaches. The study showcases the effectiveness of the framework by conducting extensive tests on diverse big datasets, demonstrating that the DRL-enhanced system surpasses previous methods in terms of both accuracy and speed. The study also addresses architectural design, the choice of reinforcement learning methods, and the found implementation issues. Moreover, it delves into the consequences of these discoveries for subsequent investigations and real-world implementations, emphasizing the capacity of DRL to transform the identification of patterns in large-scale data settings. This work enhances the area of big data analytics by offering a strong technique for extracting complex patterns, which in turn enables better informed decision-making and discovery in scientific and commercial sectors. © 2024 IEEE.

关键词： Deep reinforcement learning

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Integrity Estimation of Twitter Based Event Recognition Using Scrutiny of Analyzed Data

Integrity Estimation of Twitter Based Event Recognition Usin...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Siva Rama Lingham N M Parameswari Karthick P T. Subha Rathi Priya G Rajesh J. Jagannathan Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Knowledge Institute of Technology Salem India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India School of Engineering and Technology Jeppiaar University Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India

Twitter has been observed to be one of the essential data resources for dependable event accreditation. In any case, Twitter-based event affirmation structures can't guarantee assessment concerning their attestation results. Talk statement has been locked in starting late to enable liberal event Recognition. This problem is not yet evident in light of the assumption that most people tend to think that they have a specific limit to the data on Twitter that have frames based on events based on Twitter. Current appraisals see new pieces by seeing and looking at the significant psychological aspects of hitting the details on Twitter. No matter, the scale of the visual cues can be well established, with the intention that it is essential to design an integrated Twitter experiment with another set of extensible data resources to rectify this problem. The problem is the processes by which you should wholeheartedly investigate all converted data because it has different data structures, transfer times, etc. This issue of address, the paper proposes a framework for evaluating the use of Twitter-based events and examines two types of data resources for the reliability of impact testing. Our framework uses the resultant of the request obtaining by various events and relevant to the articles on it. This paper has improved the cleaning test with the proposed process how the integrity estimation of twitter based event can be recognized with the help of group of analyzed data. The evaluation indicates that the proposed system provides visible high-level testing events and a variety of low-level testing events.

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