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A Novel Key Generation Algorithm Utilizing Lattice-Based Cryptography Principles 3rd

A Novel Key Generation Algorithm Utilizing Lattice-Based Cry...

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3rd international conference on computing and Communication Networks, ICCCN 2023

作者： Arul, Rajakumar Sivaselvam, B. Choudhary, Ankush Centre for Smart Grid Technologies Vellore Institute of Technology Chennai India Department of Computer Science and Engineering IIITDM Kancheepuram Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819726707

Technology advancement is inevitable, and it has created a revolution in all the sectors creating huge opportunities. Artificial intelligence and machine learning have boosted the advancement of all the sectors making the life easier and comfortable. On the other hand, these advancements highly depend on the connectivity through which all the terminals are communicating. Thus, all the technology developments depend on the communication paradigm, particularly on the wireless medium. Securing the information and infrastructure is crucial, and it demands highly complex problems that cannot be breached with high computation machines like quantum computer. The emergence of quantum computer has made all the existing cryptographic algorithms vulnerable creating a space for advanced algorithms that are safe from quantum computer attacks. Key generation is the foundational process of any cryptographic algorithm, and in this research, we propose a novel key generation mechanism based on lattice using mathematical functions. The proposal is evaluated using theoretical assumptions, and it is found to be safe and secure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： encryption algorithms

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Design and Development of Smart Monitoring System for Reducing early Chicken Mortality 3

Design and Development of Smart Monitoring System for Reduci...

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3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Oeishee, Moslema Hoque Ahammad Muyeed, Munyem Mehedi, Rejoan Nayan Molla, Md Alvi, Mishkat Brac University Electrical and Electronic Engineering Department Dhaka Bangladesh United International University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798331523893

This paper presents an IoT-based autonomous farming method for lowering the mortality rate of 7-10-day-old chickens by preventing the flow of infection and continuously monitoring and controlling some very pivotal environmental parameters. In this system, an eSP32 microcontroller is used as the central unit which manages and controls all the sensors, ensuring healthier environmental conditions within the farm. The system uses several sensors like the temperature and humidity sensor DHT11. To create a user-friendly interface, real-time data is displayed via a 16x2 LCD monitor presenting the consumers with instant status of the crucial parameters. Moreover, this system will integrate two specialized robotic arms, identifying inactive or dead chickens and separating them to a designated place from the hatching area. This is proven to be significantly efficient in minimizing infection rates and ensuring a healthier surrounding for the remaining chickens. The incorporation of a buck converter allows high-power sources to step down so that low-power devices can run smoothly. In the implementation phase, we bought 250 chickens which from 222 chickens survived yielding a favorable cost benefit ratio of 0.48 indicating the economic validity of the system. The results of this study show that advanced sensor systems and robotic technology can be used in poultry farming to improve farm productivity reducing mortality rates. © 2025 ieee.

关键词： Robotic arms

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A Detection Method for Visual Texture of Malware Based on Improved R-FCN and Transfer Learning 3rd

A Detection Method for Visual Texture of Malware Based on Im...

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3rd international conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Chen, Jian Yang, Zhibin Wang, Lei Jiang, Jun Liu, Ruitong Ji, Heng Zhao, Yuntao Li, Yuerong State Grid Liaoning Electric Power Research Institute Shenyang China State Grid Liaoning Electric Power Co. Ltd. Shenyang China State Grid Liaoning Electric Power Co. Ltd. Chaoyang Power Supply Company Chaoyang China School of Information Science and Engineering Shenyang Ligong University Shenyang110159 China

ISBN: (纸本)9789819624676

Traditional signature-based malicious code detection methods often need better resistance to obfuscation and variability. In this paper, we propose an improved deep learning detection method with R-FCN and migration learning based on malicious family code visualization of typical texture features by constructing a shareable fully convolutional network independent of ROIs and an unshared ROI-wise word sub-network, which improves the detection location sensitivity problem caused by texture image translation due to the invariance-induced degradation of detection location sensitivity while accelerating model convergence. Aiming at the problem of uneven distribution of malicious texture and background regions during code visualization and the decrease of detection rate due to the negative samples of malicious code texture after obfuscation and mutation, the model introduces the complex negative samples mining algorithm OHeM and non-maximum suppression algorithm NMS, which, by calculating the IoU cross ratio between the malicious texture and the background texture, eliminating the similar edges of the malicious texture, and re-training the complex negative samples, can effectively improve the classification and localization detection accuracy. experimental results show that the improved method proposed in this paper outperforms other malicious code visual texture detection methods regarding classification accuracy, border regression detection rate, and mAP. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Improved Deep Reinforcement Learning Based Resource Allocation for 5G and Beyond Networks in edge computing environment 3

Improved Deep Reinforcement Learning Based Resource Allocati...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Balasm, Zaed Shashidhara, K.S. Sandhiyarajeshwari, B. Nigileeswari, B. Swarnalakshmi, G. College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India Rajalakshmi Institute of Technology Department of Artificial Intelligence & Data Science Poonamalle India Dhanalakshmi Srinivasan College Of Engineering Technology Department of Electronics and Communication Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798331508456

energy-efficient Resource Management (RM) for 5G and beyond networks has become challenging because of increasing Small Cells (SCs) densities and high Quality of experience (Qoe) requirements of business users. However, demands generated through plethora of innovations and concurrent IT services need high quality of service and quality of experience level overwhelm the resource available in 5G and Beyond scenarios. In this manuscript, developed the novel solution based on improved Deep Reinforcement Learning (DRL) which considers maximizing of overall value of data delivered to end users as coherent and comprehensive criterion of resource management. The developed algorithm has ability of learning efficient automatic resource management increasing total value delivered to end-users. The developed resource allocation and optimization technique addresses the changing user Qoe requirements and minimizes energy consumption policies which allocates service metrics for in 5G networks. Based on simulations, it is clear that the developed method is highly effective and minimizes energy consumption to provide the Qoe for users compared to conventional methods. © 2025 ieee.

关键词： Resource allocation

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Artificial Intelligence in Mobile computing for english Language Testing: Redefining Automated Language Assessment 3

Artificial Intelligence in Mobile Computing for English Lang...

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3rd international conference on Intelligent Systems, Advanced computing, and Communication, ISACC 2025

作者： Muththamizh Selvi, S.I. Senthamarai, T. Kumar, S. Karthik Sheik Hameed, N. Vijayakumar, S. Raj, I Infant B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Chennai India Annamalai University Chidambaram India B. S Abdur Rahman Crescent Institute of Science and Technology Chennai India K. Ramakrishnan College of Engineering Department of CSE Tamil Nadu Trichy India

ISBN: (纸本)9798331523893

Artificial intelligence's (AI) quick development has created new opportunities in a number of domains, including education. Language evaluation is one such field that has experienced a great deal of innovation. The utilization of auto encoders to redefine automated language assessment is the focus of this work, which investigates the integration of artificial intelligence (AI) in mobile computing for english language evaluation. Traditional language assessment approaches are being challenged by AI's capability to offer real-time, scalable, and personalized evaluations. By leveraging autoencoders deep learning models optimized for unsupervised learning and feature extraction - the proposed system evaluates diverse linguistic attributes such as grammar, spelling, pronunciation, fluency, and comprehension. Autoencoders transform high-dimensional input data into low-dimensional latent spaces, enabling efficient assessment of nuanced language patterns. The methodology incorporates the LibriSpeech ASR Corpus for data preprocessing, feature extraction, and training. Key innovations include robust handling of diverse input types (text and audio), accurate reconstruction of linguistic features, and scalable evaluation of proficiency. Performance metrics such as accuracy, reconstruction error, and fluency scores demonstrate the model's superior learning and assessment capabilities. Compared to other deep learning methods like LSTMs, CNNs, and Transformers, the autoencoder-based approach excels in accuracy and adaptability, offering impartial and precise assessments. The study "concludes"with a discussion of the benefits and challenges of AI-driven automated testing, providing insights into its potential to revolutionize educational assessment and reduce the burden on instructors by automating routine evaluations © 2025 ieee.

关键词： Deep learning

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Robust Signal Processing Approaches for enhancing Communication Reliability in Ultra-Dense Massive MIMO Networks Facing Fading Challenges 3

Robust Signal Processing Approaches for Enhancing Communicat...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Moray, Rashmy Kumari, Savita Upadhyay, Deepak Sharma, Rashmi Ranswal, Aakash Singh Venu, Nookala Symbiosis International Deemed University India School of Computing Science and Engineering Galgotias University Greater Noida India Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Department of Cse Nagpur Campus Pune India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798331542375

The goal of this paper is to introduce robust signal processing techniques for realistic communication scenarios with fading in ultra-dense massive MIMO communications. The proposed techniques tackle major challenges like channel estimation accuracy, interference management, and energy efficiency, which are essential for sustaining high performance in highly dense and dynamic scenarios. The proposed system outperforms traditional algorithms through advanced techniques and pilot contamination mitigation, interference-to-signal ratio optimization, and hybrid relaying. It is shown by the simulation results those considerable gains in spectral efficiency, energy efficiency and packet errors are achieved, even in the presence of varying user mobility and hardware impairments. These approaches provide reliable communication and optimize the network performance and thus are desirable for future ultradense 6G networks. © 2025 ieee.

关键词： Network coding

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Hybrid CNN-RNN Models for Multimodal Analysis of Autism Spectrum Disorder Neuroimaging 3

Hybrid CNN-RNN Models for Multimodal Analysis of Autism Spec...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Varshney, Rajat Kishor Katiyar, Alok Johri, Prashant School of Computing Science & Engineering Galgotias University Greater Noida203201 India School of Computing Science & Engineering Galgotias University Greater Noida203201 India School of Computer Application & Technology Galgotias University Greater Noida203201 India

ISBN: (纸本)9798331542375

ASD is a developmental disorder in the neurological category and has numerous and diverse symptoms. Diagnosis is still problematic because symptoms of parkinsonism are diverse, and the dimensionality of the neuroimaging data is high. This paper presents a combination of CNN and RNN for classifying the ASD by analyzing multimodal neuroimaging data such as fMRI data, MRI data, and DTI data. The CNN component analyzes spatial characteristics of the neuroimaging data, and the RNN learns temporal patterns in fMRI series. enhancements include a novel preprocessing step called 3D Brain Region Alignment (3D-BRA) as well as the 3D version of Adaptive Temporal, minimizing dimensionality through an innovative feature selection technique employing eN-CC. Predictive analysis with the hybrid model utilizes standard benchmark datasets such as ABIDe I, ABIDe II, HCP, and SFARI and records classification performances over 95 percentage. To enhance the interpretability of the model, the Grad-CAM algorithm is used, which marks specific regions of the brain important in the context of ASD. The proposed framework seems to possess considerable feasibility for enhancing the diagnosis of ASD and improving the understanding of its neural substrates, making the developed method highly advantageous for applications in neuroinformatics and clinical utility. © 2025 ieee.

关键词： Deep learning

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Reliable Multihop Routing and Congestion Aware Chicken Swarm Optimization in Vehicular Ad-Hoc Network 3

Reliable Multihop Routing and Congestion Aware Chicken Swarm...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Anitha, Cuddapah Al-Tarazi, Dalia Vignesh, G.D. Vasuki, A. Vinotha Giri, Jayant Dinesh, M. School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Department of Computer Science and Engineering Tirupati India Zarqa University Faculty of Engineering Department of Architecture Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia St. Joseph's College of Engineering Department of Electronics and Communication Engineering Chennai India J.P. College of Engineering Department of Computer Science and Engineering Tenkasi India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Lovely Professional University Division of Research and Development Phagwara India Department of Vlsi Microelectronics Chennai India

ISBN: (纸本)9798331508456

Background: Vehicular Ad Hoc Networks (VANeTs) play a crucial role in intelligent transportation by facilitating communication between vehicles and vehicles with infrastructure-based models. They encounter problems such as significant movement, overcrowding, and loss of data. This research presents a novel deep reinforcement learning-based resource allocation and congestion optimization (DRLRCO) framework aimed at improving communication efficiency and high reliability using an effective learning process. Method Used: The DRLRCO framework employs agent-based learning to ensure precise data transmission, AdMAC Protocol for dynamic packet delivery, and Congestion-Aware Chicken Swarm Optimization (CSO) to enhance network performance. Result: The DRLRCO framework surpasses reinforcement learning models for comprehensive testing. Significant enhancements involve improved accuracy, reduced data loss, minimized overhead, increased throughput, and shorter average delays for real-time communication. Conclusion: The DRLRCO framework successfully tackles congestion and data loss in vehicular communication. Future studies might involve Artificial Intelligence (AI) for traffic forecasting, experimentation among high-speed vehicles and investigating blockchain for enhanced security. © 2025 ieee.

关键词： Deep reinforcement learning

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IoT-enabled Smart Irrigation System with ABiLSTM for Optimized Water Management and Crop Yields 3

IoT-Enabled Smart Irrigation System with ABiLSTM for Optimiz...

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3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Chaturvedi, Abhay Rao, Praveena Venkata Maruti Prasad, S. Malini, T. Prabagar, S. Bharathi, K. Gla University Department of Electronics and Communication Engineering Mathura India Institute of Aeronautical Engineering Department of Civil Engineering Hyderabad India Aditya Institute of Technology & Management Department of Bs & H Tekkali India Sri Krishna College of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India School of Advanced Computing Alliance University Department of Computer Science and Engineering Bangalore India Erode Sengunthar Engineering College Department of Agricultural Engineering Perundurai India

ISBN: (纸本)9798331508456

An efficient management of water resources in agriculture is now crucial in light of the growing global population and increased demand for water. Innovative solutions are needed to improve water efficiency since conventional irrigation methods are not sustainable anymore. Through the use of precision agriculture and the Internet of Things (IoT), smart irrigation has revolutionised water management, leading to increased crop yields. In order to improve the efficiency of decision-making, this research presents an Internet of Things (IoT) smart irrigation system that uses SA-BiLSTM (Self-Attention Bidirectional Long Short-Term Memory). Using Min-Max and Z-score normalisation approaches for feature scaling, the methodology includes data preparation, feature selection, and model training. Methods based on ranking, such as the Chi-Square Test, Pearson Correlation Coefficient (PCC), and Mutual Information (MI), were used to prioritise feature selection. Classification and prediction testing showed that the proposed SA-BiLSTM model outperformed state-of-the-art models like HGAT and Linear SVM, with an astounding 95.63% accuracy. Improved resource allocation and irrigation effectiveness, leading to increased agricultural output, are highlighted by the results, which highlight the effectiveness of advanced machine learning approaches in precision agriculture. © 2025 ieee.

关键词： Resource allocation

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Maximizing Renewable energy Utilization in a Hybrid grid-Connected PV-Biomass System with Demand Response Strategies 3

Maximizing Renewable Energy Utilization in a Hybrid Grid-Con...

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3rd ieee international conference on Automation and Computation, AUTOCOM 2025

作者： Thakur, Ankita Abidi, Shabeeh Asghar Kataria, Ansh Panda, Monalisa Jain, Romil Mithun Kumar, S. Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Bengaluru India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Engineering Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India

ISBN: (纸本)9798331542375

Over recent years, the world has been increasingly fundus of renewable energy avenues to respond to the global thirst for electricity. Among these sources, solar photovoltaic (PV) systems have become popular due to their cost and ease of integration. Nevertheless, the irregular nature of PV systems makes it very difficult to merge them with grid-connected systems. In this study, through research, we work towards achieving the dream of utilizing the highest possible renewable energy in a Hybrid grid-connected PV -Biomass System. It can integrate intermittent PV power well with controllable and dispatch able biomass power and has solved many problems for stable and reliable energy supply (grid side). Demand response programs will be used to improve overall system efficiency, allowing consumers to adapt their electricity consumption to the availability of renewable energy. The performance of the proposed system will be benchmarked using simulations for different PV and biomass generation capacities, battery storage, and demand response scenarios. The outcomes will show what the system can achieve regarding increased fossil-free energy use and lower dependence on conventional, fossil-fuel-based energy providers. This study will help create new avenues for sustainable energy and encourage using renewable resources in the electricity grid. © 2025 ieee.

关键词： energy utilization

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