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Transforming Agriculture with Cloud computing: Data Processing and Analysis Innovations 3

Transforming Agriculture with Cloud Computing: Data Processi...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Kaur, Harpreet Lovely Professional University Department of Computer Application Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798331515911

Cloud computing is a game-changer in modern agriculture, providing new forms to access enormous data analysis. In this paper, we investigate how cloud computing solutions are being integrated with agricultural practices to improve the efficiency, scalability and availability in managing data pertaining to agriculture. Farming operations generate massive amounts of data from a multitude of sources like sensors, drones, and satellite imagery., the performance bottleneck becomes the traditional methods with which the data are being processed. So the common factor in all these problems lies with Cloud computing itself, it has a scalable infrastructure that provides reliable analysis and Real-time processing of large data sets throwing light on an array of questions people have about various aspects. In this paper, we explore the major innovations in cloud computing that power precise agriculture and examine how machine learning algorithms, data fusion methods, and distributed processing platforms are utilized. It also discusses cloud-based solutions, which can reduce costs of the use of resources, crop yield prediction, and risk management to weather/climate and pests. Cloud computing opens up a world of monitoring, analysis, and optimization to help provide higher yields more efficiently while maintaining a commitment to sustainable agriculture. Challenges and considerations in deploying cloud computing in agriculture, e.g., data security risks, privacy concerns, and the need for stable internet connection for rural environments are also articulated in the paper. The results of this project underscore the vital importance of cloud technology in redefining the agri-future and providing a robust platform for smarter and more sustainable agriculture. © 2025 IEEE.

关键词： agricultural data processing Cloud computing data analysis machine learning precision agriculture sustainable farming

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Intrusion Detection at the Edge computing: A Deep learning Approach Using the UNSW-NB15 Dataset 14

Intrusion Detection at the Edge Computing: A Deep Learning A...

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14th IEEE international conference on Communication Systems and Network Technologies, CSNT 2025

作者： Kumar, Vipin Kumar, Vivek Bhadauria, Abhay Pratap Singh Dixit, Jay Kumar, Amit Haridwar India Department of Computer Engineering & Applications GLA University UP Mathura India

ISBN: (纸本)9798331531935

Edge computing has transformed technology by enabling seamless connections between IoT devices, but it also introduces significant security challenges. EC is crucial for providing minimal latency processing and reducing the load on centralized servers, ensuring timely and scalable intrusion detection for IoT environments. The findings underscore the potential of combining edge computing, IoT, and deep learning to address evolving security challenges in distributed systems. To mitigate these risks, efficient models for detecting malicious activities are crucial. This study investigates the application of edge computing, Internet of Things (IoT), and deep learning to improve the performance of Intrusion Detection Systems (IDS). By leveraging the UNSW-NB15 dataset, the research assesses the capability of deep learning models to identify and mitigate cyber threats effectively. The results demonstrate better performance, showing their suitability for real-time security applications. These findings highlight the potential of intelligent intrusion detection systems powered by robust datasets and deep learning for addressing evolving security threats in edge computing environments. © 2025 IEEE.

关键词： Edge computing

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Online Collaborative Training Tools and Practice in engineering Pedagogy and Teachers’ Training 27th

Online Collaborative Training Tools and Practice in Engineer...

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27th international conference on Interactive Collaborative learning, ICL 2024

作者： Benedek, András Horváth, János Cz. Department of Technical Education Budapest University of Technology and Economics Műegyetem Blv. 2 Budapest1117 Hungary

ISBN: (纸本)9783031835193

The research conducted in a technical university environment has nearly a decade of history, which we first reported on in the context of the 21st international conference on Interactive Collaborative learning (ICL2018), analyzing the connections between Digital Transformation and Interactive Collaborative learning. The five-year project was understandably affected by the rapid transformation of the educational environment due to COVID-19, and the reinterpreted online teaching space [1] posed a new challenge. This study aims to systematize the lessons learned from the implementation phase of the project's results and present the methodological experiences we gained in the practice of engineering and professional teacher training. The micro-content created by the students, on their initiative, with the visual presentation technique of their choice, made it possible to analyze the micro-content developed by 230 students in the context of the case study in terms of genre and professional methodology. In the current phase of the project's implementation, a university-level knowledge repository has been created, which is based on the characteristics of community content development, is not profit-oriented, and at the same time, provides an opportunity to share the development competencies acquired in professional teacher training in an open innovation space. The development history of the Open Content Development project, especially the online turn in teaching and learning, shows the impact of Open Science on everyday practice. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Collaborative learning

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Sustainable Development Goals in Computer engineering: A Curriculum Integration 17

Sustainable Development Goals in Computer Engineering: A Cur...

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17th international conference on Computer Supported Education, CSEDU 2025

作者： Markiegi, Urtzi Aldalur, Iñigo Electronics and Computing Department Mondragon Unibertsitatea Arrasate-Mondragon Spain San Sebastian Spain

ISBN: (纸本)9789897587467

This contribution delves into the incorporation of Sustainable Development Goals (SDGs) into the Computer engineering curriculum. The study addresses challenges associated with integrating SDGs as cross-cutting content in higher education. Employing a Problem-Based learning approach, semester projects are aligned with SDGs, and their impact is systematically assessed. Key research questions guide the study, evaluating students’ knowledge, perceptions, and the alignment of projects with SDGs. The methodology integrates SDG-focused learning units within the existing Problem-Based learning structure. New activities, including initial training, initial assessment, impact assessment, and SDG learning assessment, guide and evaluate students’ integration of SDGs. Results demonstrate successful implementation across three years of the Computer engineering degree. The study involves a significant number of subjects, students, and teams, ensuring a comprehensive evaluation. A rubric assesses SDG impact, emphasizing justification and direct positive contributions to SDG objectives. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Students

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teaching Lean Software Development to Software Engineers 18th

Teaching Lean Software Development to Software Engineers

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18th international KES conference on Agents and Multi-Agent Systems: Technologies and Applications, KES-AMSTA 2024

作者： Kholmatova, Zamira Succi, Giancarlo Tulkunova, Navruza Faculty of Computer Science and Engineering Innopolis University Universitetskaya Ul. 1 Innopolis420500 Russia Department of Computer Science and Engineering Alma Mater Studiorum – Università di Bologna Mura Anteo Zamboni 7 Bologna40126 Italy

ISBN: (纸本)9789819764686

This research paper proposes the implementation of a teaching strategy for the Lean software development course targeting third-year undergraduate students. Additionally, the intermediate results of applying the proposed methodology are provided. The primary objective is to suggest an instructional approach and enhance the overall learning experience. The proposed methodology employs a diverse set of research methods, including both qualitative and quantitative approaches, to comprehensively assess the outcomes associated with the Lean software development course. Specifically, the teaching strategy encompasses preliminary grade selection, mid-oral exams, quizzes, project-based learning, and a final exam. The proposed strategy will inform future advancements in curriculum design and instructional strategies, ultimately benefiting both students and the broader software engineering community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Collaborative learning

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AI/ML Applications in Spoofing Detection in 5G Networks: Challenges and Solutions 3

AI/ML Applications in Spoofing Detection in 5G Networks: Cha...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Singh, Monika Hegde, Raveesh Jha, Mahesh Kumar Choudhary, Amar Rubini, P. Cmr Institute of Technology Bengaluru India Alliance University Alliance College of Engineering and Design Bengaluru India Cmr University School of Engineering and Technology Bengaluru India

ISBN: (纸本)9798331515911

5G networks has advanced connectivity, high data, low latency and reliability. Along with these features new challenges come with respect to security, particularly spoofing attacks. One of the necessity in 5G and future wireless communication is to prevent the spoofing attacks depending on the applications. Artificial Intelligence (AI) and Machine learning (ML) present promising solutions for detecting and mitigating spoofing threats in 5G networks. This paper explains the different types of spoofing and applications, challenges, and future prospects of AI/ML in addressing spoofing in 5G networks. AI/ML algorithms can be used in detection of the location of attackers. This paper also analyses current methodologies, highlight key challenges, and discuss potential advancements that could enhance the security of 5G networks. © 2025 IEEE.

关键词： Adversarial machine learning

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Transformative Impact of Predictive and Generative AI on Education Workforce in Developing Countries 5

Transformative Impact of Predictive and Generative AI on Edu...

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5th international conference on Advanced Research in computing, ICARC 2025

作者： Weligodapola, Mano Kumarapperuma, Chathurini U. Sri Lanka Institute of Information Technology Sliit Business School Department of Business Management Malabe Sri Lanka

ISBN: (纸本)9798331530983

This study investigates the transformative impact of predictive and generative artificial intelligence (AI) on the education workforce in developing countries, focusing on its challenges, opportunities, and implications. Utilizing a qualitative case study approach, we explored the AI's impacts, challenges, and opportunities on work within the education sector in ten educational institutions across three countries, through qualitative data collection methods, including semi-structured interviews, observations, informal discussions, and focus group discussions with students, lecturers, and administrative staff. The findings highlight the role of AI in revolutionizing pedagogy, enhancing student engagement, and optimizing administrative workflows. Educators reported improvements in teaching strategies driven by AI-powered adaptive learning platforms and AI-generated resources, which enable personalized and dynamic learning experiences. However, the research also revealed key challenges, such as resistance to AI adoption, concerns over data privacy, and infrastructure limitations that hinder effective integration. A cross-case analysis emphasized the evolving role of educators, highlighting shifts in teacher-student dynamics and potential increases in job satisfaction, tempered by new responsibilities. While students benefit from personalized learning paths, they also face hurdles related to digital literacy and unequal access to AI-driven resources. Ethical considerations around AI deployment emerged as critical, underscoring the need for clear guidelines and strong policies. The study concludes by offering recommendations for educational institutions, policymakers, and AI developers to collaborate in fostering an innovative, inclusive, and ethically responsible educational landscape. Future research is encouraged to expand on these findings across different educational contexts and explore the long-term effects of AI on teaching, learning, and workforce dynamics. © 20

关键词： teaching

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Personalized learning pathway design and course recommendation based on K-means, Apriori and Genetic algorithm 2

Personalized learning pathway design and course recommendati...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zhang, Jiajia Zhenjiang Vacational Technical College Zhenjiang212000 China

ISBN: (纸本)9781510689053

With the development of big data technology, personalized learning path design becomes more and more important in the field of education. In order to identify students' learning behaviors and styles and recommend personalized learning paths, the study used K-means algorithm, Apriori algorithm, and Genetic algorithm to complete the analysis of students' historical learning data and the recommendation of personalized courses. In the results of the study, the method has high accuracy in predicting students' ratings for different courses, and in practical applications, students' completion rates, satisfaction and learning progress indexes for personalized course recommendations are good, especially in mathematics courses, where the course completion rate reaches 100%, the participation rate is 90%, and the learning progress index is 85%. The results of the study demonstrate the effectiveness as well as the accuracy of the adopted algorithm in personalized learning path design. The study can provide theoretical support for the development of students' abilities as well as the optimization of teaching resources. © 2025 SPIE.

关键词： Contrastive learning

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Ensemble and Attention-Driven Deep learning Model for Grapevine Leaf Variety Identification 3

Ensemble and Attention-Driven Deep Learning Model for Grapev...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Kaur, Jashanpreet Singh, Gurpreet Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

ISBN: (纸本)9798331515911

The research paper introduces an advanced Mo- bileNetV3Large model incorporating attention mechanisms and ensemble learning, which will be applied in identifying grapevine leaf varieties with high accuracy. The approach enhances classification accuracy by applying attention mechanisms that target critical features within the image and by using ensemble learning to further stabilize predictions and prevent overfitting. The model was trained on and tested against a dataset that consisted of five varieties of grapevine leaves, and in that the model's classification accuracy is 98. Extensive metrics that accounted for precision, recall, and the F1 score all average in at 0.98. Comparisons with state of art models, such as MobileNetV2, PNN, and Deep CNN, show the adequacy of the proposed method over these methods with respect to accuracy. The results have revealed that the capabilities of attention mechanisms combined with ensemble learning capabilities introduce additional robustness to the model and results which make it a real world based applicability to grapevine leaf variety classification. The proposed method would provide a promising solution to precision agriculture, with an accurate identification of different grapevine leaf varieties that would eventually lead to further improvements in vineyard management and crop quality. © 2025 IEEE.

关键词： Contrastive learning

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Privacy-Preserving Asynchronous Vertically Federated learning against Batch Level Label Inference Attacks 24

Privacy-Preserving Asynchronous Vertically Federated Learnin...

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2024 international conference on Artificial Intelligence of Things and computing, AITC 2024

作者： Jin, Yu Liu, Ying College of Information Science & Electronic Engineering Zhejiang University Zhejiang Hangzhou China

ISBN: (纸本)9798400709869

Vertical Federated learning (VFL), which enables multiple parties that hold different features of the same data set to collaboratively train a global learning model without sharing their raw data, is an important paradigm in FL. However, VFL is vulnerable to privacy threats, particularly Label Inference Attacks (LIA), where passive participants try to deduce sensitive labels of the active party. Existing defense strategies, such as Homomorphic Encryption (HE) and Differential Privacy (DP), often face challenges related to high computational overhead or tradeoff between privacy and accuracy, especially in asynchronous settings. Considering this, in this paper, we propose a novel defense mechanism against batch-level label inference attacks (BLI) in vertical asynchronous federated learning (VAFL), where client-level differential noise is added to obscure batch-level gradients such that the passive participants cannot derive the label information from the active participant. Besides, to address party's asynchronicity, our approach incorporates a temporal weight decay strategy according to the staleness of each party. A series numerical simulations are then performed to verify the effectiveness and robustness in privacy protection for VAFL scenarios. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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