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6th International Conference on Mobile Computing and sustainable Informatics, ICMCsI 2025

作者： Kumar, Kusuma Ashok Mohandas, R. suneel, sajja Mahalakshmi, M.R. Kumar, Avinash singh, Manmohan Department of Physics B V Raju Institute of Technology Medak District Telangana state Narsapur502313 India Chennai Institute of Technology Chennai India Institute of Aeronautical Engineering Dundigal Telangana Hyderabad500043 India Department of ECE Sri Muthukumaran Institute of Technology Tamil Nadu Chennai600 069 India Department of Computer Science and Engineering IES College of Technology Bhopal India

ISBN: (纸本)9798331522667

The integration of quantum communication principles with 5G technology presents a groundbreaking approach to addressing the growing demand for high-speed, secure, and resilient communication systems. This paper proposes a Quantum-Driven Hybrid Network Architecture that leverages Quantum Key Distribution (QKD) for unparalleled security and combines it with 5G's ultra-fast data transmission capabilities. The architecture integrates key components such as QKD modules, 5G infrastructure, and edge computing, organized into a layered framework comprising quantum, network control, and application layers. simulation results demonstrate significant improvements in throughput (up to 23.5%), latency reduction (up to 25%), and error rate reduction in high-noise environments (from 0.25% to 0.05%). The proposed system also exhibits enhanced security, achieving a 100% success rate in mitigating eavesdropping and man-in-the-middle attacks. Using resource-efficient mechanisms, the hybrid network achieves an overall accuracy of 97.8% in secure and resilient data transmission. This work validates the potential of quantum-enhanced 5G systems to meet the challenges of future communication networks. © 2025 IEEE.

关键词： Quantum communication

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Advancing HRM with AI Powered Recruitment Performance Management and Employee Engagement Using One Class svMs 3

Advancing HRM with AI Powered Recruitment Performance Manage...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACs 2025

作者： banu, Rafiya Tripathi, Ruchi sharma, saumya Chenniappan, Loganathan Chandrasekaran, K. Umanesan, R. B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Commerce Chennai India SRM-IST Faculty of Management Studies Ghaziabad India The Northcap University Department of Management & Commerce Gurugram India Business Administration IBRI Oman K.S.R College of Engineering Department of Mechanical Engineering Tiruchengode India School of Computing Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508456

AI is making labour-intensive HRM operations like hiring, performance review, and employee engagement more efficient and effective. The incorporation of AI technology in HRM could change several sectors. Through efficient applicant sourcing, screening, and selection, AI-driven solutions simplify hiring processes and ensure fair and accurate picks. Performance management using AI-powered solutions provides data-driven feedback and customized development plans to align individual progress with organizational goals. Predictive analytics improve employee engagement by personalizing experiences and meeting needs. The method focuses on preprocessing and model training. Data are cleansed, ordinal and categorical variables encoded, scaling characteristics and engineering features applied, and dimensional homogeneity assured before processing. For model training, the simple and robust one-class svM technique is used. One-class svM model outperformed others with 95.28%. This method worked best compared to GAN and GRU models. AI could improve HRM by improving decision-making and offering data-driven solutions. The findings show that the proposed model outperforms current options, providing the framework for HRM AI applications. © 2025 IEEE.

关键词： Human resource management

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Combination kernel support vector machine based digital twin model for prediction of dyslexia in distributed environment 2nd

Combination kernel support vector machine based digital twin...

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2nd International Conference on Next-Generation Computing and Information systems, ICNGCIs 2023

作者： Kareemullah, H. Quraishi, Aadam Prashant, G.C. shabaz, Mohammad Kollu, Archana sandhu, Mukta B.S.A Crescent Institute of Science and Technology Tamil Nadu Chennai India Interventional Treatment Institute Houston Texas USA McAllenTX United States USA University Texas Tech University Lubbock StateTX United States Model Institute of Engineering and Technology J&K Jammu India Pimpri Chinchwad College of Engineering and Research Ravet Pune India Skill Department of Computer Science and Engineering Shri Vishwakarma Skill University Haryana Palwal India

ISBN: (纸本)9781032738659

Dyslexia is a learning disability that is widespread and marked with a persistent problem with word identification and spelling. It has an impact on a person’s capacity to decipher letters and words accurately and fluently. Dyslexia has emerged as one of the most widespread learning disabilities, though the medical experts have not yet come out explaining its core causes. In this research, the prediction and classification of dyslexia is effectively carried out with the help of brain images which acts as input to machine learning techniques towards the prediction of dyslexia. The proposed dyslexic prediction model uses a Combination digital twin Kernel-based support vector Machine (KsvM) optimized by Whales algorithm. The combination kernel svM shows better accuracy and less computational time compared to single kernel of svM in a distributed environment. The proposed combinational kernel is less complex than deep learning models. © 2025 The Author(s).

关键词： support vector machines

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AI Dynamics: Revolutionizing Organizational skills and Contributing to Enhanced business in Morocco 9th

AI Dynamics: Revolutionizing Organizational Skills and Contr...

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9th International Conference on smart City Applications, sCA 2024

作者： Chegri, badre Eddine Aiche, Karim berhich, Asmae Mettiti, Abderrahman El Rhajbal, Zineb saadane, Rachid Chehri, Abdellah Mohammed V University in Rabat Rabat Morocco AMIPS Research Team E3S Research Center Computer Science Department EMI Mohammed V University in Rabat Rabat Morocco LRIT-GSCM Faculty of Sciences University Mohammed V Rabat Morocco Laboratory Engineering System SIRC/LAGeS EHTP Casablanca Morocco Department of Mathematics and Computer Science Royal Military College Kingston Canada

ISBN: (纸本)9783031886522

This study investigates the transformative effects of Artificial Intelligence (AI) on organizational skills within the Moroccan context. Utilizing a mixed-methods approach, combining qualitative interviews and a quantitative survey across diverse industries, the research explores AI's impact on crucial competencies like adaptability, communication, decision-making, and collaboration. The findings reveal a nuanced picture of both positive and challenging aspects of AI adoption, emphasizing the need for strategic planning and skill development. Focused on Morocco, this research contributes unique insights into the socio-economic implications of AI in a developing economy. The study's outcomes provide actionable recommendations for policymakers, business leaders, and educators, highlighting the imperative of fostering an environment that maximizes the potential of AI for organizational skills enhancement, ultimately bolstering overall competitiveness. © The Author(s), under exclusive license to springer Nature switzerland AG 2025.

关键词： strategic planning

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Enhancing IoT-based smart Farming security with a Hybrid GRU Intrusion Detection system

Enhancing IoT-Based Smart Farming Security with a Hybrid GRU...

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Interdisciplinary Approaches in Technology and Management for social Innovation (IATMsI), IEEE Conference on

作者： sivakumar Nagarajan s. vijay s. Karthi Rvs Praveen Ramesh vijaya babu K burri Naresh Technical Architect I & I Software Inc Bethlehem USA Department of Data Science Sacred Heart College (Autonomous) Tamilnadu India Department of Computer Science and Engineering V.S.B. Engineering College Karur Tamilnadu India Director Digital Engineering & Assurance LTIMindtree Limited Hyderabad Telangana India SRKR Engineering College Bhimavaram Andhra Pradesh India Department of CSE - Cyber Security CVR College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The growing use of IoT devices in smart agriculture makes the industry vulnerable to several cyber risks. This study introduces an innovative hybrid Intrusion Detection system (IDs) utilising Gated Recurrent Units (GRUs) to improve the security of IoT-enabled smart agriculture. compared to current LsTM and CNN-based intrusion detection systems, the proposed model effectively incorporates sequential data dependencies, hence minimising false positives and enhancing detection accuracy. Experimental findings indicate that the suggested model surpasses conventional techniques, with an accuracy of 97.5%, an F1 score of 0.96, and a recall of 0.97. These developments create a strong, efficient, and scalable solution for safeguarding IoT-based agricultural systems from cyber-attacks.

关键词： smart agriculture Technological innovation Accuracy Computational modeling scalability Intrusion detection Internet of Things Object recognition Protection Long short term memory

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A generative framework for detection and classification of plant leaf disease using diffusion network

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Applied soft Computing 2025年 177卷

作者： Das, Aryan Mahto, Rajul Wang, Wentao Jamali, Ali singh, Pravendra Roy, swalpa Kumar School of Computer Science Engineering Vellore Institute of Technology Bhopal Madhya Pradesh 466114 India Department of Computer Science University of Alabama at Birmingham 10th Ave S 35294 AL United States Department of Geography Simon Fraser University 8888 University Dr Burnaby BCV5A 1S6 Canada Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand 247667 India Department of Computer Science and Engineering Alipurduar Government Engineering and Management College West Bengal 736206 India

Agriculture is a crucial driver of global economic growth, significantly contributing to employment, Gross Domestic Product (GDP), and the provision of essential food and raw materials. However, crop diseases, which often go unnoticed and untreated, cause substantial yearly reductions in farm productivity. While effective on small-scale farms, traditional manual methods for disease detection are impractical for larger operations due to their inefficiency, subjectivity, and vulnerability to human error. In contrast, deep learning technologies offer a promising solution by automating the accurate recognition and classification of crop diseases, thus enhancing efficiency and accuracy without requiring highly specialized expertise. This work presents a novel approach by introducing a diffusion model for classifying plant leaf diseases. We propose a novel diffusion-based deep learning technique (LeafDisDiff), which employs a U-Net-style architecture with position embeddings, group normalization, attention blocks, and integrated residual blocks, centring around a denoising approach for each time step. The proposed LeafDisDiff algorithm has shown superior classification accuracy across three crop disease data benchmarks: Plant village, bangladeshi Crop Disease, and Apple Disease. For example, LeafDisDiff outperforms several deep learning classifiers such as viT, Cross viT, vGG-19, CNN, ResNet-50, and EfficientNet-b3 and improves classification accuracy by approximately 9.2, 6.2, 5.7, 5.5, 2.5, and 0.7 percentage points, respectively, achieving an average accuracy of 99.49% over the Plant village dataset. This work is motivated by recent advancements in diffusion probabilistic algorithms for generative image modelling and demonstrates the potential of these techniques in agricultural applications. Moreover, the implementation of this model can facilitate early and accurate detection of plant diseases, enabling prompt interventions like targeted pesticide application and sel

关键词： Crops

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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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visual Analytics and Data visualization (ICvADv), International Conference on

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

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

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.

关键词： Wireless sensor networks Adaptive systems Array signal processing scalability Prevention and mitigation Interference Throughput Real-time systems Energy efficiency signal to noise ratio

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IDOL: IoT based intrusion detection using Conv- biLsTM

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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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