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Generative AI in Agri: Sustainability in Smart Precision Farming Yield Prediction Mapping System Based on GIS Using Deep Learning and GPS

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Procedia computer science 2025年 252卷 365-380页

作者： Senthil G. A S.U. Suganthi L. Prinslin R. Selvi R. Prabha Associate Professor Department of Information Technology Agni College of Technology Chennai India Associate Professor Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Assistant Professor Department of Computer Science and Engineering Agni College of Technology Chennai India Assistant Professor Department of Computing Technologies SRM Institute of Science and Technology kattankulathur Chennai India Associate Professor Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India

Agriculture faces many challenges of precision farming, such as the need for sustainable practices, improving yields, ensuring high yields. In resolution to these challenges, the present research provides an AI-based system that enables the use of deep learning, Global Positioning System (GPS), and Geographic Information System (GIS) technologies to create a highly intelligent smart agricultural precision farming system. Its goal is to monitoring crop health and reduce disease risk, which will lead to improved resource utilization and environmentally sustainability techniques. The proposed framework addresses the urgent need for consistency in agricultural practices, especially as global agriculture deals with pressures from climate change, resource shortages, and increasing demand for food. Traditional agricultural methods for predicting and optimizing crop yields due to increasing factors affecting crop performance Not enough generative AI, especially the use of deep learning models, supports agricultural research in many cases, allowing patterns to be identified and future results to be predicted accurately. The integration of GPS and GIS allows for more accurate mapping, real-time analysis, and effective decision-making. Weather forecasting variability, resource constraints, and demand for more food are isolated from environmental influences using deep learning models, especially Artificial Neural Networks (ANN). By using large data sets, including historical crop yield performance, soil properties, and weather conditions, the system provides highly accurate crop forecasts. Generative Adversarial Networks (GANs) and You Only Look Once (YOLO) hybrid model is playing a key role in generating crop yield and growth potential under different conditions, adjusting model accuracy over time, and this combination of ANN, GANs and YOLO optimization algorithms ensures that the system continuously enhances its predictive accuracy and overall effectiveness. The proposed gene

关键词： Generative AI Deep Learning Agriculture Smart Farming Crop Health Yield Prediction Global Positioning System Geographic Information System Artificial Neural Networks Generative Adversarial Networks Sustainability You Only Look Once (YOLO)

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Integrating Generative Artificial Intelligence Into Future Internet of Vehicles [From the Guest Editors]

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IEEE Vehicular Technology Magazine 2025年第2期20卷 40-42页

作者： Gang Sun Hongyang Du Abbas Jamalipour Gabriel-Miro Muntean Lin Cai Jiawen Kang Dusit Niyato University of Electronic Science and Technology of China Chengdu China Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China University of Sydney Sydney NSW Australia Performance Engineering Laboratory Dublin City University Dublin Ireland Department of Electrical and Computer Engineering University of Victoria Victoria BC Canada Guangdong University of Technology Guangzhou China College of Computing and Data Science Nanyang Technological University Singapore Singapore

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Retraction Note: A Systematic Survey on Implementation of Fuzzy Regression Models for Real Life Applications

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Archives of Computational Methods in engineering 2025年 1-2页

作者： Khan, Mufala Kumar, Rakesh Aledaily, Arwa N. Kariri, Elham Viriyasitavat, Wattana Yadav, Kusum Dhiman, Gaurav Kaur, Amandeep Sharma, Ashutosh Vimal, S. Department of Mathematics Lovely Professional University Phagwara India College of Computer Science and Engineering University of Ha’il Hail Saudi Arabia College of Computer Science and Engineering Prince Sattam Bin Abdulaziz University Al‑Kharj Saudi Arabia Business Information Technology Division Department of Statistics Faculty of Commerce and Accountancy Chulalongkorn University Bangkok Thailand Department of Computer Science Government Bikram College of Commerce Patiala India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon University Centre for Research and Development Department of Computer Science and Engineering Chandigarh University Gharuan India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajaplayam India

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

arXiv

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arXiv 2025年

作者： Al-Shareeda, Sarah Ozguner, Fusun Redmill, Keith Duong, Trung Q. Canberk, Berk Electrical and Computer Engineering Department The Ohio State University United States BTS Group Turkey Department of AI and Data Engineering Istanbul Technical University Turkey School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John’sNL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom

Task offloading management in 6G vehicular networks is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50% due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1]. © 2025, CC BY.

关键词： Deep reinforcement learning

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Predicting Lung Cancer with Artificial Neural Network: Analyzing Performance and Feature Importance

Predicting Lung Cancer with Artificial Neural Network: Analy...

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Cognitive computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Vatsal Mehta Varun M Ashwini Kodipalli Roshan Joy Martis Shridhar B. Devamane Ushasree. A Dept. of AI & Data Science Global Academy of Technology Bangalore Karnataka India Dept. of Electronics & Communication Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science School of Computing Mysuru Dept. of Computer Science & Engineering Gokaraju Lailavathi Engineering College Hyderabad India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Lung cancer is the primary cause of cancer-related deaths globally. This cancer is majorly observed in smokers, increasing air pollution has also begun to affect non-smokers. Research indicates that exposure to polluted air significantly raises the risk of developing lung cancer, posing a major threat to public health. Despite advancements in medical technology, the mortality rate from lung cancer remains high, highlighting the importance of early diagnosis. Machine learning and deep learning offer promising solutions for early and accurate prediction through advanced data analysis. In our study, we utilized a dataset with 26 attributes for discern lung cancer, categorizing cases into three risk levels: high, medium, and low. We applied various algorithms and develop an Artificial Neural Network (ANN), comparing the overall performance with various machine-learning and deep-learning models. Our results show that the ANN achieved a high accuracy of 94.9% using the Adam optimizer, making it an effective model for lung cancer prediction.

关键词： Deep learning Accuracy Computational modeling Biological system modeling Lung cancer Mortality Artificial neural networks Predictive models Prediction algorithms Public healthcare

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Machine Learning for Wireless Network Optimization: Algorithms and Applications for Adaptive Communication

Machine Learning for Wireless Network Optimization: Algorith...

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Advances in computer science, electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Madhira Srinivas S. Surya Prachi Deepak Thakar Amit Kumar Jain Department of Computer Science & Engineering Data Science Geethanjali College of Engineering & Technology (GCET) Hyderabad India Sri Ramachandra Faculty of Engineering & Technology (SRET) Sri Ramachandra Institute of Higher Education & Research Porur Chennai India Department of Computer Engineering KJ college of Engineering & Management Research Pune India Department of Electrical & Electronics Engineering Poornima University Jaipur Rajasthan India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

This project looks into the possibility of applying machine learning to optimize wireless networks for adaptive communication. Using 5G resource data, it applies preprocessing, exploratory analysis, and visualization to identify key trends: signal strength, latency, and bandwidth utilization. Machine learning algorithms predict resource allocation based on latency-sensitive and bandwidth-demanding applications. The approach emphasizes energy efficiency, scalability, and service quality, and dynamically adjusts communication strategies to meet diverse network demands. The findings indicate the machine learning importance in enhancing elasticity and robustness in a wireless network and its general function.

关键词： Machine learning algorithms Adaptive systems Wireless networks Scalability Machine learning Bandwidth Predictive models Prediction algorithms Resource management Optimization

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Blockchain-Based Secure Cloud Storage for IoT Healthcare Using BiGRU and BiLSTM Models

Blockchain-Based Secure Cloud Storage for IoT Healthcare Usi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Prachi Arihant Chougule Sarumathi S Ravi Kant Raj Kumar Gupta U. Pushpa Latha Anvesh Perada Department of Electrical Engineering DKTE Society's Tetile and Engineering Institute Ichalkaranji India Department of Computer Science and Engineering HKBK College of Engineering Bangalore India Department of Computer and Data Sciences YSAICDS Shoolini University Solan India Department of Physics Sardar Vallabhbhai Patel College Bhabua VKSU Ara India Department of English Malla Reddy Engineering College (Autonomous) Hyderabad India Department of Electrical and Computer Engineering Drexel University Philadelphia United States of America

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Machine Learning (ML) algorithms have experienced a significant increase in popularity owing to the digitisation of analogue processes and other technological advancements, like the Internet of Things (IoT). Dependable datasets are crucial for the precise forecasting and resolution abilities of these algorithms, which are indispensable in healthcare, cloud computing, engineering, and finance. Conversely, training datasets are vulnerable to manipulation, thereby skewing the results. To address this issue, blockchain-based approaches have been proposed to enhance the reliability and security of cloud-stored E-Health data generated by the Internet of Things. To improve the accuracy and efficacy of healthcare prediction models, our study focusses on an advanced strategy that integrates feature selection with model training. Optimal input data is refined by feature selection employing entropy and correlation coefficient methodologies. The proposed model, employing BiGRU and BiLSTM, surpasses conventional CNNs and BiRNNs, with an average accuracy of 94.57%. When integrated with the Internet of Things (IoT), blockchain technology ensures the secure storage and transmission of electronic health records (EHRs). This strategy enhances predictive accuracy and safeguards sensitive health data in cloud infrastructures by amalgamating feature selection techniques with robust deep learning models.

关键词： Training Cloud computing Accuracy Bidirectional long short term memory Medical services Predictive models Feature extraction Internet of Things Security Secure storage

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Low-latency control system for feedback experiments with optical tweezer arrays

arXiv

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arXiv 2025年

作者： Dadpour, Amir H. Khayrullin, Timur Afiouni, Fouad Sabeh, Remy El Mouawad, Amer E. Hajj, Izzat El Cooper, Alexandre Institute for Quantum Computing University of Waterloo Canada Department of Computer Science American University of Beirut Lebanon School of Electrical and Computer Engineering Purdue University United States David R. Cheriton School of Computer Science University of Waterloo Canada Department of Physics and Astronomy University of Waterloo Canada

We present and characterize a modular, open-source system to perform feedback control experiments on configurations of atoms and molecules in arrays of optical tweezers. The system features a modular, cost-effective computer architecture with a motherboard and peripheral cards. It supports efficient data transfer to and from graphics processing units (GPUs) using Remote Direct Memory Access (RDMA), leveraging GPU efficiency in matrix multiplication and parallelism, while enabling direct data transfer between devices without involving the CPU. We first describe the architecture and workflow of the system, detailing its hardware components and software modules. We then evaluate the computational runtime for preparing defect-free chains and grids of atoms using efficient implementations of atom reconfiguration algorithms. Finally, we discuss timing bottlenecks and strategies to reduce latency. Beyond solving reconfiguration problems, the system can readily be used to implement adaptive and feedforward protocols, as well as digital quantum algorithms relying on particle displacement. Our results lay the groundwork for developing low-latency feedback control systems, benchmarking their performance, and advancing scalable quantum hardware. © 2025, CC BY.

关键词： Feedback control

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Integrating Instruction-based Learning with Bidirectional LSTM for Autonomous Vehicle Pedestrian Detection

Integrating Instruction-based Learning with Bidirectional LS...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： B Shoba T Raghunathan S.Sam Peter Karthick K Akshya. J M. Sundarrajan Mani Deepak Choudhry Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tirunelveli Department of Artificial Intelligence and Data Science Dr.NG.p.Institute of Technology Coimbatore Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore Department of Information Technology Sona College of Technology Salem Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Networking and Communications SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Pedestrian detection is a crucial aspect of autonomous vehicle (A V) safety, as it enables accurate identification and tracking of individuals in an A V environment. Effective pedestrian detection supports essential A V functions like braking, steering, and speed control to prevent accidents in urban settings. Traditional computer vision methods, such as Support Vector Machines and Random Forests, rely on pre-designed features like the Histogram of Oriented Gradients. While the existing techniques offer some performance, it face significant challenges in dynamic environments, especially under varying lighting conditions, occlusions, and diverse pedestrian poses. Traditional models also require extensive manual feature engineering and tuning, which limits scalability and adaptability. Recent advances in deep learning, particularly with Convolutional Neural Networks (CNNs), have allowed for automatic feature learning, yielding practical improvements over classical methods. However, CNNs can struggle in low light and when facing adversarial attacks, as they mainly capture spatial features. To address these limitations, this research proposes a new methodology combining CNNs for spatial feature extraction with Bidirectional Long Short-Term Memory (BiLSTM) networks to capture temporal dependencies. This hybrid approach enhances detection capabilities by learning both static and dynamic patterns. Our proposed CNN-BiLSTM model achieves a 92 % detection accuracy and an F1 score of 88.5% across diverse conditions, outperforming existing models by up to 15%. By integrating temporal features, the model reduces missed detections and false positives by up to 20%, providing a more reliable and adaptable solution for pedestrian detection in autonomous vehicles.

关键词： Adaptation models Pedestrians Heuristic algorithms Feature extraction Real-time systems Vehicle dynamics Collision avoidance Autonomous vehicles Optimization Logistics

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Preserving Location Privacy of IoT Devices in Heterogeneous Edge computing Architecture Through Deniability-Based Authentication

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IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Ikram Li, Jianqiang Chen, Jie Chen, Yong Ullah, Shamsher Wakeel, Abdul Shenzhen University National Engineering Laboratory of Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China National University of Sciences and Technology Military College of Signals Department of Electrical Engineering Islamabad Pakistan

Edge computing moves cloud services closer to consumer Internet of Things (IoT) devices, reducing latency and bandwidth usage. This setup enables faster responses but also introduces new security challenges, particularly concerning location privacy when sender and receiver use different security techniques. The authentication process in such architectures can expose the location of IoT devices. To address this issue, researchers have proposed various privacy-preserving schemes. However, these are computationally heavy for IoT devices. To tackle this issue, we propose two novel heterogeneous deniable authentication schemes: HDA-IoT-I and HDA-IoT-II. These schemes use public-key infrastructure and identity-based cryptography to protect the location privacy of IoT devices in heterogeneous edge computing environments. Our schemes enable the designated edge device to verify the source of a message without being able to prove its origin to a third party, thus preserving privacy. Additionally, they incorporate a batch verification method, which speeds up the verification of multiple deniable authenticators. The proposed schemes are formally proven secure in the random oracle model based on the hardness assumption of elliptic curve inverse computational Diffie-Hellman problem. The performance evaluation demonstrate that our schemes significantly enhance computational and communication efficiency, making them suitable for IoT devices. © 2025 IEEE.

关键词： Differential privacy

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