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

作者： Kulkarni, Soham Sunil Kumar, Anant Gakhar, Suket Sharma, Vipin Kumar Arora, Sneha Department of Computer Science University of California Irvine Irvine United States Department of Computer Science and Engineering Manipal Institute of Technology Manipal India Department of Computer Science and Engineering Kurukshetra University Kurukshetra India Department of Electronics and Communication Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331530389

This paper introduces a secure authentication framework by applying Mutual Authentication Coupled with Optimised AES (Advanced Encryption Standard) Encryption to improve the security of the multi-server environment. By addressing inefficiencies in traditional mutual authentication protocols without encryption, this framework significantly improves both performance and resilience. Encryption time was reduced by 25% and authentication time by 16.67% while CPU (Central Processing Unit) usage was reduced by 23.08%, with a corresponding 21.43% improvement in memory efficiency. The proposed system demonstrates a 70% improvement in attack resistance. Dynamic revocation mechanisms mitigate unauthorized access and malicious activity in real time. The protocol is designed for resource-constrained environments to ensure secure, scalable, and efficient communication in multi-server architectures, further increasing system reliability and user trust in cloud-based services. © 2025 IEEE.

关键词： Authentication Protocol

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Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

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Intelligent Automation & Soft Computing 2023年第1期35卷 983-995页

作者： Ravi Samikannu K.Vinoth Narasimha Rao Dasari Senthil Kumar Subburaj Department of Electrical Computer and Telecommunications Engineering Botswana International University of Science and TechnologyBotswana Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennaiTamil NaduIndia Department of Electrical Power Engineering College of EngineeringDefence UniversityEthiopia Department Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and TechnologySembakkamTamil NaduIndia

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing *** energy sources are playing a significant role in the modern energy system with rapid *** renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental *** develop energy resources,electric power generation involved huge *** power and output voltages are plays important role in our work but it not considered in the existing *** considering the power and voltage,Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier(GPIC-MDCNNC)Model is introduced for the grid-connected renewable energy *** input information is collected from two input *** that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC *** layer 1 is transferred to hidden layer *** activation is employed for determining the output voltage with help of the *** last,the output layer offers the last value in GPIC-MDCNNC *** designed method was confirmed using one and multiple sources by stable and unpredictable input ***-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art *** control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

关键词： Multi-port converters renewable sources fuzzy PI controller gaussian activation function fuel cell

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An Adaptive Reversible Data Hiding Scheme using Image Processing and Deep Learning Techniques 5

An Adaptive Reversible Data Hiding Scheme using Image Proces...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Allwyn, Philip Hemalatha, J. Sampathkumar, Nithyanantham Ramaraj, Kottaimalai Swain, Gandharba Sekar, M. AAA College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Sivakasi India AAA College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Sivakasi India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Tamilnadu Krishnankoil India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhrapradesh Vaddeswaram India AAA College of Engineering and Technology Department of Mechanical Engineering Tamilnadu Sivakasi India

ISBN: (纸本)9798331530013

Image steganography is the process of hiding data from digital photos without changing how they look on the outside. It functions as a covert mode of communication, giving access to confidential data without raising the suspicions of opponents or unauthorized parties. It provides an extra degree of secrecy and security for digital communications. Prediction-error expansion (PEE) is an extensively used method in the type of reversible data hiding (RDH) that hiding data with intact image quality in the prediction errors. High-accuracy prediction algorithms are typically essential for a PEE-based RDH implementation to be successful. Current PEE-based Reversible Data Hiding (RDH) systems frequently use a single forecasting approach, typically with worldwide optimization, but pay little attention to the local neighborhoods' pixel distribution features. This study aims to analyze the most recent RDH techniques and investigate image processing and deep learning (DL) techniques for reversible data hiding (DH) schemes. RDH is a difficult topic in general which has potential uses in today's digital world. Reversible data hiding techniques properly restore the cover medium while safely transferring secret data (SD). The goal of RDH techniques is to achieve both large capacity and tunable quality. Even though reversible data hiding has been the subject of indepth research recently, there hasn't yet been a thorough literature analysis to identify research gaps and potential future approaches. © 2024 IEEE.

关键词： Data Hiding Data Security Image Processing Pixel Sorting Reversible Data Hiding Steganography

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A Novel Framework Leveraging Adam Optimization Techniques, Coupled with Mask R-CNN Deep Learning Mechanisms for Cervical Cancer Detection System 3

A Novel Framework Leveraging Adam Optimization Techniques, C...

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

作者： Saranya, G. Sujatha, C. Ssm Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Dindigul624 002 India Ssm Institute of Engineering and Technology Department of Computer Science Engineering Dindigul624 002 India

ISBN: (纸本)9798350375190

Cervical cancer remains a significant public health concern, with early detection crucial for successful treatment. Deep learning techniques offer promising avenues for automated cancer diagnosis, and this work explores the potential of the Mask R-CNN model with Adam optimization for cervical cancer detection in colposcopy images. This study aims to develop and evaluate a deep learning system based on Mask R-CNN and Adam optimization for automated detection and segmentation of cervical lesions in colposcopy images. The proposed system utilizes Mask R-CNN, a deep convolutional neural network (CNN) is capable of both object detection and instance segmentation. Pre-trained on the COCO dataset, the model is fine-tuned on a labeled colposcopy image dataset for cervical lesion detection and segmentation. Adam optimization, an efficient gradient descent algorithm, is used to maximize the training process of the model. High accuracy in recognizing and differentiating cervical lesions in colposcopy images is anticipated from the system. The system should demonstrate its ability to perform effectively on unseen data and handle variations in image quality. The proposed system's performance is evaluated on a separate test dataset using standard metrics like accuracy, sensitivity, specificity, and precision for both lesion detection and segmentation tasks. Additionally, the system's generalizability and robustness to variations in image quality are assessed. This novel approach has the potential to significantly contribute to the field of cervical cancer diagnosis by providing an automated, accurate, and efficient system for early detection, ultimately leading to better patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Solar Powered Terahertz Antennas for Wireless Sensor Network Applications 1

Solar Powered Terahertz Antennas for Wireless Sensor Network...

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1st International Conference on Microwave, Antenna and Communication, MAC 2023

作者： John, Asha Susan Gopalakrishnan, Usha Joseph, Teena B Siraj, Ashiya Musaliar College of Engineering and Technology Department of Electronics and Communication Engineering Pathanamthitta India Musaliar College of Engineering and Technology Department of Computer Science Engineering Pathanamthitta India

ISBN: (纸本)9798350303001

The paper suggests a solar-powered antenna to be used in wireless sensor network applications that operates in the 0.57THz band. The idea behind the work is to integrate the antenna in the communication unit and the power unit in a sensor node. This can be achieved by specially designing the antenna in such a way that it can take part in communication as well as in the generation of charge which can charge a battery. The patch material is made up of graphene which is a very good conductor of heat and electricity. The material is used to absorb the charge when the battery is being charged. The antenna is designed in the shape of a rectangular patch which is designed to operate in the 0.57THz region. The simulated antenna achieved a gain of 6.81dB with a return loss of -51 dB. The antenna has a good efficiency of 55% compared to the small antennas designed for this frequency with a VSWR of 1. This antenna was simulated in the CST studio suite. © 2023 IEEE.

关键词： Sensor nodes

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RIS-Assisted ISAC: Precoding and Phase-Shift Optimization for Mono-Static Target Detection

RIS-Assisted ISAC: Precoding and Phase-Shift Optimization fo...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Demir, Özlem Tuǧfe Björnson, Emil Tobb University of Economics and Technology Department of Electrical-Electronics Engineering Ankara Turkey Kth Royal Institute of Technology Department of Computer Science Stockholm Sweden

ISBN: (纸本)9798350351255

The reconfigurable intelligent surface (RIS) technology emerges as a highly useful component of the rapidly evolving integrated sensing and communications paradigm, primarily owing to its remarkable signal-to-noise ratio enhancement capabilities. In this paper, our focus is on mono-static target detection while considering the communication requirement of a user equipment. Both sensing and communication benefit from the presence of an RIS, which makes the channels richer and stronger. Diverging from prior research, we comprehensively examine three target echo paths: the direct (static) channel path, the path via the RIS, and a combination of these, each characterized by distinct radar cross sections (RCSs). We take both the line-of-sight (LOS) and the non-line-of-sight (NLOS) paths into account under a clutter for which the distribution is not known, but the low-rank subspace it resides. We derive the generalized likelihood ratio test (GLRT) detector and introduce a novel approach for jointly optimizing the configuration of RIS phase-shifts and precoding. Our simulation results underscore the paramount importance of this combined design in terms of enhancing detection probability. Moreover, it becomes evident that the derived clutter-aware target detection significantly enhances detection performance, especially when the clutter is strong. © 2024 IEEE.

关键词： Radar clutter

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Energy-Efficient Cell-Free Massive MIMO with Wireless Fronthaul 58

Energy-Efficient Cell-Free Massive MIMO with Wireless Fronth...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Topal, Ozan Alp Demir, Özlem Tuǧfe Björnson, Emil Cavdar, Cicek Kth Royal Institute of Technology Department of Computer Science Kista Sweden Tobb University of Economics and Technology Department of Electrical-Electronics Engineering Ankara Turkey

ISBN: (纸本)9798350354058

Cell-free massive MIMO improves the fairness among the user equipments (UEs) in the network by distributing many cooperating access points (APs) around the region while connecting them to a centralized cloud-computing unit that coordinates joint transmission/reception. However, the fiber cable deployment for the fronthaul transport network and activating all available antennas at each AP lead to increased deployment cost and power consumption for fronthaul signaling and processing. To overcome these challenges, in this work, we consider wireless fronthaul connections and propose a joint antenna activation and power allocation algorithm to minimize the end-to-end (from radio to cloud) power while satisfying the quality-of-service requirements of the UEs under wireless fronthaul capacity limitations. The results demonstrate that the proposed methodology of deactivating antennas at each AP reduces the power consumption by 50% and 84% compared to the benchmarks based on shutting down APs and minimizing only the transmit power, respectively. © 2024 IEEE.

关键词： Benchmarking

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Parametric Channel Estimation for RIS-Assisted Wideband Systems

Parametric Channel Estimation for RIS-Assisted Wideband Syst...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Kosasih, Alva Demir, Özlem Tuǧfe Björnson, Emil Kth Royal Institute of Technology Department of Computer Science Kista Sweden Tobb University of Economics and Technology Department of Electrical-Electronics Engineering Ankara Turkey

ISBN: (纸本)9798350351255

A reconfigurable intelligent surface (RIS) alters the reflection of incoming signals based on the phase-shift configuration assigned to its elements. This feature can be used to improve the signal strength for user equipments (UEs), expand coverage, and enhance spectral efficiency in wideband communication systems. Having accurate channel state information (CSI) is indispensable to realize the full potential of RIS-aided wideband systems. Unfortunately, CSI is challenging to acquire due to the passive nature of the RIS elements, which cannot perform transmit/receive signal processing. Recently, a parametric maximum likelihood (ML) channel estimator has been developed and demonstrated excellent estimation accuracy. However, this estimator is designed for narrowband systems with no non- line-of-sight (NLOS) paths. In this paper, we develop a novel parametric ML channel estimator for RIS-assisted wideband systems, which can handle line-of-sight (LOS) paths in the base station (BS)-RIS and RIS-UE links as well as NLOS paths between the UE, BS, and RIS. We leverage the reduced subspace representation induced by the array geometry to suppress noise in unused dimensions, enabling accurate estimation of the NLOS paths. Our proposed algorithm demonstrates superior estimation performance for the BS-UE and RIS-UE channels, outperforming the existing ML channel estimator. © 2024 IEEE.

关键词： Channel state information

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Enhancing Syndicate Lending Through AI-Powered Fine-Tuning: Leveraging LLAMA2 for Dynamic Decision Support

Enhancing Syndicate Lending Through AI-Powered Fine-Tuning: ...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Kumar, Ashish Jindal, Kanika Sharma, Himanshu Chaudhary, Ankur Noida Institute Of Engineering And Technology Department Of Computer Science And Engineering Uttar Pradesh Greater Noida India Noida Institute Of Engineering And Technology Department Of Electronics And Communication Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350366846

This electronic document describes the involvement of finance agencies and various types of corporate industries through 'syndicated loans' that simplify financial transactions for both agencies and corporations. This will be a revolutionary step towards the 'fintech' sector as it is all about decentralization and how it changes the whole game during the lending process. This paper describes a proposed process of lending, monitoring, evaluation, and development in the lending process between borrowers (enterprises) and lenders using Large Language Model. After highlighting the difficulties in implementing this proposal, the paper also discusses its impact on the transition process and outlines what has already been done. The proposal increases the transparency of the entire process among stakeholders, simplifies manual work, reduces operational costs, and provides data to support the analysis and summarization of benefits derived from the lending process. A secure system offers the ease of use and dexterity of the technology that makes the agenda highly reliable in all aspects of test cases. The study includes the experimentation of proposed system that how it reduces the failure rate of startups by 70% by providing financial aids to startups and suggesting a better business model if needed. © 2024 IEEE.

关键词： Finance

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Emotion Recognition in Visual Art on Color and Texture Analysis using Improved Vision Transformer 1

Emotion Recognition in Visual Art on Color and Texture Analy...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Suma, K.G. Balasm, Zaed Sasirekha, N. Veera Manikandan, P. Sandhya, K. Vellore Institute of Technology School of Computer Science and Engineering Department of Ai & Ml Andhra Pradesh India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Electronics and Communication Engineering Salem India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Electrical and Electronics Engineering Mamallapuram India Tirupati India

ISBN: (纸本)9798331529246

Nowadays, Emotion Recognition plays a key role in social communication interaction in which human emotions are interpreted from facial expressions and speech. Several challenges occur in emotion recognition such as capturing context of any emotion also facial attributes make the recognition process complex. To overcome these drawbacks Improved Vision Transformer (IViT) classifier is proposed for emotion recognition. Initially, the input data is collected from the Japanese Female Facial Expression (JAFFE) to collect color features and visual features. Then, the input data is processed using min max scaling method which normalizes pixel values. After that, colour and texture-based features are extracted by utilizing Rotation Invariant Local Binary Patterns (RI-LBP) with Hue Saturation and Value (HSV) and Red Green Blue (RGB). Finally, classification is performed by IViT, which processes images as sequences of patches. Hence, the proposed IViT model achieved better output when compared to existing Visual-Audio Attention Network (VAANet) in terms of accuracy by obtaining 96.6% respectively. © 2025 IEEE.

关键词： Local binary pattern

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