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IDOL: IoT based intrusion detection using Conv- biLsTM

Australian Journal of Electrical and Electronics engineering 2025年

作者： C, vijayalakshmi v, Padmajothi R, suguna Devi Whitin, Priscilla s, Rajakumar Rajaretnam, subraja Department of Computer Science and Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai India

Internet of Things (IoT) networks have produced copious amounts of data that can be used to identify intrusions. Network Intrusion Detection systems are crucial for spotting attacks before they cause any damage as part of a layered defence strategy. In this paper, a novel Intrusion Detection using Optimized deep Learning (IDOL) has been proposed to detect the intrusions like DDos attacks, botnet attacks, Convergence attacks, and Ransomware attacks. Initially the IXIA Perfectstorm tool display data with malicious attack from the Network Environment and the data are converted in the data Pre-Processing method. Then the needed data are extracted in the feature extraction process by using boW technique. From the Extracted data the Feature selection process will select the essential data using Ant Colony Optimization. Finally, the Conv-biLsTM model will classify the attack into four classes namely DDos attack, botnet attack, Convergence attack, and Ransomware attack. The proposed IDOL method achieves the highest accuracy rate of 98.93%. Using the UNsW-Nb15 and NsL-KDD datasets, the suggested model achieves an accuracy rate of 98.92% and 98.94% which is greater than the existing techniques such as ML-IMID, IDs-sLoEL, and POs-LightGRM respectively. © 2025 Engineers Australia.

关键词： Network intrusion

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blockchain-based system for virtual Power Plants: Enhancing Efficiency and security in Operations and Transactions 8th

Blockchain-Based System for Virtual Power Plants: Enhancing ...

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8th International Conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Ganesh Kumar, G. Kanakaprabha, s. Gopal, Gaddam venu shaik, Riaz srija, N. Ashok, K. Department of Information Technology Hindusthan Institute of Technology TN Coimbatore India Department of Computer Science and Engineering Rathinam Technical Campus TN Coimbatore India B V Raju Institute of Technology Telangana Narsapur Medak India Department of Internet of Things KL Deemed to be University AP Vijayawada India Department: Information Technology M. Kumarasamy College of Engineering TN Karur India Vignana Bharathi Institute of Technology Hyderabad Ghatkesar India

ISBN: (纸本)9789819757855

The concept of virtual Power Plant (vPP) has become an attractive solution to optimize the integration of renewable energy sources into the grid. However, the operation and transaction system of vPPs still face several challenges, such as the lack of transparency, security, and efficiency. In this learning, we propose a new operation and transaction system built on blockchain technology for vPPs. The proposed system contains of two layers, the blockchain layer and the application layer. The blockchain layer is responsible for ensuring the security and transparency of transactions, while the application layer provides an interface for vPP operators and users. We implemented the proposed system using Ethereum, one of the most popular blockchain platforms. We also conducted a case study to evaluate the routine of the proposed system in terms of transaction throughput, latency, and scalability. The outcomes show that the proposed system can handle a large quantity of transactions with low latency and high scalability. Our study demonstrates the possible of blockchain technology to enhance the operation and transaction system of vPPs, which can contribute to the integration of renewable energy bases into the grid. © The Author(s), under exclusive license to springer Nature singapore Pte Ltd. 2025.

关键词： blockchain Cybersecurity Decentralized consensus Energy trading Energy transactions Operational efficiency Renewable energy smart contracts virtual power plants vPP

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Uniform Coherence in Amalgamated Algebra Along an Ideal

Uniform Coherence in Amalgamated Algebra Along an Ideal

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International Conference on Algebra and its Applications, ICAA 2023

作者： Ismaili, Karima Alaoui Kim, Hwankoo Mahdou, Najib Department of Mathematics Faculty of Sciences of Rabat Mohammed V University in Rabat B.P. 1014 Rabat Morocco Division of Computer Engineering Hoseo University Dongnam-gu Korea Republic of Modelling and Mathematical Structures Laboratory Department of Mathematics Faculty of Science and Technology of Fez University S.M. Ben Abdellah Fez Fez city2202 Morocco

ISBN: (纸本)9789819767977

The concept of uniform coherence was first introduced by soublin and has since been extensively explored by numerous researchers. A ring A is defined as uniformly coherent if there exists a function : N → N, where N represents the set of natural numbers, such that for every n ∈ N and each nonzero homomorphism f: An → A, the kernel ker( f ) can be generated by (n) elements. In this paper, we investigate how the property of uniform coherence is transferred to commuta-tive ring extensions. We establish necessary and sufficient conditions for rings such as A ⋈f J, A ⋈ I, and A ∝ E to possess uniform coherence, covering various classes of ideals and A-modules. Our study contributes to the development of new classes of rings that meet this criterion, and we also introduce new families of rings that demonstrate the distinctiveness of the categories of Noetherian and uniformly coherent rings. © The Author(s), under exclusive license to springer Nature singapore Pte Ltd. 2025.

关键词： Amalgamated algebra Coherent Noetherian Trivial ring extension Uniformly coherent

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software Requirements Prioritization Using Fuzzy based TOPsIs Methods 2nd

Software Requirements Prioritization Using Fuzzy Based TOPSI...

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2nd International Conference on business Data Analytics, ICbDA 2023

作者： saxena, virat Raj Kumar, babloo Goel, Navnish sharma, Yogesh Kumar CSE-AI&M Department Raj Kumar Goel Institute of Technology Ghaziabad India Department of Computer Science and Engineering Dewan V S Institute of Engineering and Technology Meerut India Department of CSE IIMT Engineering College Meerut India

ISBN: (纸本)9783031807770

The success and quality of a project are directly impacted by the software development process’ crucial step of prioritising software requirements. The fuzzy Analytic Hierarchy Process (AHP) and fuzzy Technique for Order of Preference by similarity to Ideal solution (TOPsIs) are combined in this study to provide a thorough method for the methodical prioritisation of software requirements. We demonstrate the efficacy of this strategy using a real-world case study that makes use of an Institute Examination system (IEs). The technique is used to assess Functional Requirements after first identifying Non-Functional Requirements (NFRs), which include things like security, dependability, and economy and serve as crucial criteria for doing so. Pairwise comparison matrices (PCMs) are used to represent the various stakeholder perspectives. The Extent Fuzzy AHP is then used to calculate the weights of NFRs while accounting for decision-making’s inherent subjectivity. The Objective-based Requirements Elicitation Technique (ObRET) is used to elicit FRs, which are then evaluated in light of linguistic considerations. Our plan is based on the fuzzy TOPsIs method, which prioritises FRs by methodically processing the data. The process is completed by calculating Closeness Coefficients (cc) for each FR, which can be used to determine whether software is required. With the help of this integrated approach, project managers and software developers can use data to organise and prioritise needs, ensuring that software systems satisfy both crucial functional requirements and quality standards. In the end, it improves software project success by balancing the perspectives of various stakeholders, managing uncertainty, and raising software quality and user satisfaction. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： software quality

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Detection of Knee Osteoarthritis from Magnetic Resonance Imaging Using a 3-D Independent Component Analysis Method in Machine Learning 1st

Detection of Knee Osteoarthritis from Magnetic Resonance Ima...

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1st International Conference on Advanced Network Technologies and Computational Intelligence, ICANTCI 2024

作者： Karve, swagat Dhope, Tanuja satish Kaushal, Rajesh Kumar, Naveen Chippalkatti, Pranav Jadhav, Akshay S. B. Patil College of Engineering Maharashtra Indapur India College of Engineering Maharashtra Pune India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Pune India MIT College of Railway Engineering Maharashtra Barshi India

ISBN: (纸本)9783031860683

Osteoarthritis (OA) is a widespread condition without a currently available treatment, significantly affecting individuals’ quality of life as this is a progressive condition that deteriorates with time, frequently leading to persistent Joint pain and stiffness can become severe enough to make daily tasks difficult. Magnetic resonance (MR) images are commonly employed in OA diagnosis, where medical experts assess changes, especially within the tibio-femoral cartilage compartment for knee OA. This study presents a new diagnostic technique for knee OA that detects the illness from MR images using a support vector Machine (svM) algorithm. Our suggested method employs the Independent Component Analysis (ICA) technique on 3-D MR imaging data from a real-world cohort. The experimental results show that our ICA-svM machine learning model obtained 86% testing accuracy and 72% specificity. and sensitivity of 100%, having been trained on a limited MR image dataset. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： support vector machines

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selective Oxidation of benzyl Alcohol to benzaldehyde under solvent Free Aerobic Oxidation Condition Using Pd-supported CeO2 Catalyst

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Russian Journal of General Chemistry 2025年第5期95卷 1270-1279页

作者： suryam, N. Jagadish, T. Krishna, T. R.s.D, Anitha swapna, s. Rakesh, C. Mallesh, D. Reddy, K. Laxma Department of Chemistry University College of Science Osmania University Hyderabad India Department of chemistry (H&S) CVR College of Engineering Hyderabad India Department of Chemistry Nizam College Osmania University Hyderabad India B.V. Raju Institute of Tecchnology Hyderabad India

In this research, the homogenous PdNPs were developed with varying weight percentages (1 to 4 wt %) on the CeO2 support using a simple impregnation process with a mean size of 5 nm. Furthermore, homogenous PdNPs were examined for the selective oxidation of benzyl alcohol (bA) to benzaldehyde (bZA) in a solvent-free oxidation technique. The developed Pd/CeO2 heterogeneous nanocatalysts were thoroughly studied utilizing various techniques such as P-XRD, TEM, and XPs analysis. Among various Pd loadings, 3 wt % (3Pd/CeO2) catalysts exhibit enhanced catalytic activity for the bA to bZA oxidation process, with 9.5 times greater bA conversion than bare CeO2. Additionally, experiments were conducted to determine the effect of temperature, catalyst weight, reaction time, and catalyst recyclability. The highest benzyl alcohol conversion (95%), benzaldehyde selectivity (99%) and benzaldehyde yield (94%) achieved for 3Pd/CeO2. The 3Pd/CeO2 catalyst not only exhibited superior catalytic activity, but also showed stable activity during recyclability.

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Artificial Intelligence based smart vision based on segmentation of Image Processing

Artificial Intelligence based Smart vision based on Segmenta...

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Data science, Agents & Artificial Intelligence (ICDsAAI), International Conference on

作者： M. Amina begum Jayaraj Ramasamy Er. s. John Pimo veeramalai sankaradass Tushar v Mohite Patil v.Jayalakshmi Department of ECE Sri Venkateswaraa College of Technology Chennai India Faculty of Engineering and Technology Botho University Botswana Department of CSE St. Xavier's Catholic College of Engineering India Dept of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Industrial Electronics V.P.M’s Polytechnic Thane Easwari Engineering College Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Evaluating the simulation process of digital image processing and application in various fields with its image analysis technique is the essence of this article. The upcoming problems of segmentation of digital images for artificial intelligent vision are focused. The wide range of problems of computer vision can be solved by simulating digital images. This article discusses the proper evaluation of the different methods of segmentation and its importance in today's application. It discussed how algorithms help in digital image processing for face recognition, Irish recognition, and fingerprint. This study goanna help you improve the problem faced by digital image processing and create its various application in the upcoming future. Also, helps you to detect primary objective, improve performance, and perform better quality work in the field of image processing.

关键词： Image segmentation computer vision Digital images Face recognition Morphology Object detection Fingerprint recognition Artificial intelligence biomedical imaging Iris recognition

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Optimizing blood bank Management with Cloud-Hosted Long short-Term Memory Models for Inventory Forecasting and Utilization

Optimizing Blood Bank Management with Cloud-Hosted Long Shor...

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Electronics and Renewable systems (ICEARs), International Conference on

作者： R. Aiyshwariya Devi sree southry s s. Rajanarayanan s. Chandra sekaran Audithan sivaraman s. srinivasan Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Department of Computer Science and Engineering Vinayaka Mission’s Research Foundation (DU) Vinayaka Mission’s Kirupananda Variyar Engineering College Salem Tamil Nadu India Department of Computer Science and Engineering P.S.V College of Engineering and Technology Krishnagiri Tamil Nadu India Department of Information Technology Netaji Subhas University of Technology (Govt of NCTDelhi) Jafferpur New Delhi India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This research presents an innovative method for blood bank management using Cloud-based Long short-Term Memory (LsTM) models for precise inventory forecasting and optimization. The objective of this research is to increase blood bank efficiency by utilizing LsTM models to accurately estimate demand, optimize inventory levels, and enhance overall resource utilization. The system uses cloud computing to provide real-time demand forecasting, minimizing blood waste and assuring a reliable supply for healthcare requirements. LsTM models use historical data to identify long-term trends and variations in blood demand, facilitating accurate inventory forecasting. This predictive system optimizes the allocation process, resulting in improve resource utilization and increased operational efficiency. In compared to traditional techniques, the proposed system provides a more scalable and flexible solution by using cloud technology to provide continuous monitoring and dynamic modifications of inventory levels. It provides a robust framework for management of blood supply and enhancing patient care for improved resource management. Future enhancements may include broadening the system to incorporate real-time multi-source data integration and investigating hybrid machine learning models to better optimize demand forecasts.

关键词： Cloud computing Computational modeling Demand forecasting Medical services Predictive models Real-time systems Resource management blood Long short term memory Optimization

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Enhanced Monitoring and security Measures with video Analysis

Enhanced Monitoring and Security Measures with Video Analysi...

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Multi-Agent systems for Collaborative Intelligence (ICMsCI), International Conference on

作者： v. Esther Jyothi G. Rakesh vipparla Aruna shobana Gorintla Yalanati Ayyappa U Ganesh Naidu Department of Computer Applications Velagapudi Ramakrishna Siddhartha Engineering College Deemed to be University Vijayawada A.P. India Department of Computer Science and Engineering NRI Institute of Technology (Autonomous) Pothavarapadu A.P India Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu India B V Raju Institute of Technology Narsapur Telangana India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

In modern surveillance, activities have increasingly become dependent on the continuous observation offered by CCTv systems. still, with massive amounts of video data generated in a minute, sifting through this information manually to pick up any anomalies would be extremely burdensome and nearly impossible without significant human labor and vigilant watchfulness. CNN and CLIP models changed everything with regards to the working of surveillance systems. It makes use of CNN's capability for video frame processing and analysis to detect and classify these activities in the frames as either normal or suspicious. At the same time, it improves this by adding the CLIP model's capability of understanding textual descriptions and visual content together for better nuance detection in suspicious activities. This methodology transforms video surveillance by breaking video streams into frames and analyzing the behavior and interactions of persons in those frames. The synergy of CNN and CLIP models not only ensures real-time, efficient, and accurate surveillance but also minimizes dependency on extensive manual labor for monitoring. This paper aims to provide a contribution toward developing better security infrastructures in public spaces, transportation hubs, and private facilities. The introduction of this anomaly-detection mechanism can hugely improve the capability of implemented surveillance systems with a proactive response based on real-time detection and response capabilities against suspicious activities.

关键词： visualization Transportation Transforms Manuals streaming media video surveillance Real-time systems security Feeds streams

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Machine Learning-Driven Hybrid Adaptive beamforming for Efficient Interference Mitigation in Dense Wireless sensor Networks

Machine Learning-Driven Hybrid Adaptive Beamforming for Effi...

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2025 International Conference on visual Analytics and Data visualization, ICvADv 2025

作者： Loganathan, P. Priyeshkumar, A.T. Kodeeswari, K. Reena, K Jose Poonguzhali, I. balaji, M. Department of Electronics and Communication Engineering Namakkal India Mahendra College of Engineering Department of Electronics and Communication Engineering Salem India Department of Electronics and Communication Engineering Erode India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Vandalur India Panimalar Engineering College Department of Electronics and Communication Engineering Chennai India Vivekananda College of Technology for Women Department of Electronics and Communication Engineering Tiruchengode India

ISBN: (纸本)9798331521394

The Adaptive beamforming Algorithm (AbA) represents a promising approach to suppress interference in dense wireless sensor networks (WsNs), enhancing signal fidelity and network throughput. In dense WsNs, the proximity of sensor nodes leads to high interference levels, which degrade energy efficiency and communication reliability. This is exacerbated by static beamforming methods that oversimplify time and frequency utilization. This work proposes a set of adaptive beamforming solutions, including digital, machine learning (ML)-empowered, and hybrid beamforming approaches. The Adaptive Array signal Processing (AAsP) method introduces real-time interference analysis with null steering to suppress unwanted signals and maximize the signal-to-interference-plus-noise ratio (sINR). Hybrid beamforming, combining analog and digital domains, balances computational cost with performance. Additionally, a deep learning-based beam steering module dynamically adjusts beam patterns based on interference forecasts. Extensive simulations demonstrate the proposed methods' superiority over conventional beamforming, achieving significant sINR improvement, enhanced network throughput, and improved energy efficiency. In ultra-dense environments, the results indicate a 10-fold reduction in interference power alongside better scalability. Proactive interference mitigation using ML models further ensures adaptability to varying network conditions. These techniques establish a baseline for deploying adaptive beamformers in dense WsNs, addressing scalability and real-time computational challenges. They also pave the way for implementing advanced 5G/6G-capable sensor systems, enabling resilient communication in high-interference environments. Key-words: signal to interference plus noise ratio(sINR), adaptive beamforming, interference mitigation, hybrid beamforming, null steering, beam steering, computational efficiency. © 2025 IEEE.

关键词： beamforming

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