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Food and Humanity 2025年 4卷

作者： Gavai, Anand K. bouzembrak, Yamine Xhani, Donika sedrakyan, Gayane Meuwissen, Miranda P.M. souza, Renata Guizzardi-silva Marvin, Hans J.P. van Hillegersberg, Jos Industrial Engineering & Business Information Systems University of Twente Enschede Netherlands Information Technology Group Wageningen University and Research Wageningen Netherlands Business Economics Group Wageningen University and Research Wageningen Netherlands Research department of HAYAN Group B.V Rhenen Netherlands Jheronimus Academy of Data Science (JADS) s-Hertogenbosch Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven 5612 AP Netherlands

background: In today's world, grappling with the dual challenges of energy scarcity and climate change, the agricultural and food supply chains are at a crucial juncture for transformation. Data privacy within these sectors is increasingly significant, as the integration of advanced digital platforms becomes essential for improving efficiency and sustainability. scope and Approach: This systematic literature review (sLR) explores the development and application of privacy-preserving data platforms specifically tailored to the agricultural and food supply chains. The review focuses on the evolving landscape of data architectures, data sovereignty, and advanced privacy-preserving techniques. Techniques such as anonymization, encryption, differential privacy, and federated learning are examined, along with the legal and ethical considerations surrounding data sharing in the context of global energy and climate-related challenges. Key Findings and Conclusions: The review synthesizes findings from a broad spectrum of studies published over the last decade, uncovering significant advancements in privacy-preserving technologies which may show their benefit for the agricultural and food sectors. It identifies dominant research trends and promising future directions for enhancing data security and sustainability. The key findings underscore the vital importance of safeguarding data privacy in these sectors, highlighting the potential of advanced privacy techniques to protect sensitive agricultural data while balancing the need for transparency and operational effectiveness. The review concludes that adopting these innovative privacy methods is crucial for fostering a more sustainable and secure future for agriculture and food systems, contributing to the global sustainable Development Goals such as sDG 2 (Zero Hunger) and sDG 12 (Responsible Consumption and Production) by improving precision agriculture, optimizing yields (crop and animal) and reducing food waste. © 2025 The

关键词： Data Infrastructure Data platforms Data privacy Food systems sustainability

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Artificial Intelligence-Enabled Cooperative Cluster-based Data Collection for Unmanned Aerial vehicles

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computers, Materials & Continua 2022年第11期73卷 3351-3365页

作者： R.Rajender C.s.s.Anupama G.Jose Moses E.Laxmi Lydia seifedine Kadry sangsoon Lim Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Engineering Sungkyul UniversityAnyang14097Korea

In recent times,sixth generation(6G)communication technologies have become a hot research topic because of maximum throughput and low delay services for mobile *** encompasses several heterogeneous resource and communication standard in ensuring incessant availability of *** the same time,the development of 6G enables the Unmanned Aerial vehicles(UAvs)in offering cost and time-efficient solution to several applications like healthcare,surveillance,disaster management,*** UAv networks,energy efficiency and data collection are considered the major process for high quality network *** these procedures are found to be challenging because of maximum mobility,unstable links,dynamic topology,and energy restricted *** issues are solved by the use of artificial intelligence(AI)and energy efficient clustering techniques for UAvs in the 6G *** this inspiration,this work designs an artificial intelligence enabled cooperative cluster-based data collection technique for unmanned aerial vehicles(AECCDC-UAv)in 6G *** proposed AECCDC-UAv technique purposes for dividing the UAv network as to different clusters and allocate a cluster head(CH)to each cluster in such a way that the energy consumption(ECM)gets *** presented AECCDC-UAv technique involves a quasi-oppositional shuffled shepherd optimization(QOssO)algorithm for selecting the CHs and construct *** QOssO algorithm derives a fitness function involving three input parameters residual energy of UAvs,distance to neighboring UAvs,and degree of *** performance of the AECCDC-UAv technique is validated in many aspects and the obtained experimental values demonstration promising results over the recent state of art methods.

关键词： 6G unmanned aerial vehicles resource allocation energy efficiency artificial intelligence clustering data collection

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Load balancing in multimedia system using generic and Fibonacci approach

Load balancing in multimedia system using generic and Fibona...

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International Conference on (IsCO) Intelligent systems and Control

作者： D. Dhivya U. Gowrisankar V.S.B Engineering College Computer Science and Engineering Karur India

Cloud computing is collection of distributed hosts which allow services on demand to user. The service that provide to the user will include multimedia such as image/video retrieval. More number of users demands various multimedia computing resources and storage services through internet at same time in order to resolve this cloud based multimedia system emerged. The CMs will receives client's request for multimedia service task through resource manager and assign those task requests to different server clusters according to the characteristics of the requested tasks. In the existing system genetic algorithm is used for resource allocation where the videos get spitted and they are uploaded to server where the resource manager will retrieve the video files which has the drawbacks as it consumes more time and increase complexity. In the proposed system Fibonacci based splitting strategy used to split a video file into number of chunks that allows to reduce the delay, optimize the resource utilization and increases the performance by fast video download.

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Prediction of Risk in Cardiovascular Disease using Machine Learning Algorithms

Prediction of Risk in Cardiovascular Disease using Machine L...

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2022 International Conference on sustainable Computing and Data Communication systems, ICsCDs 2022

作者： Kurian, Nami susan Renji, Kathu sara sajithra, s. Yuvasree, R. Jenefer, F. Anne swetha, G. Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai India V.S.B Engineering College Department of Biomedical Engineering Karur India R.M.K Engineering College Department of Computer Science and Engineering Tamilnadu India Panimalar Engineering College Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665478847

Prediction of heart disease is one of the most complex tasks in medical field and prior detection of heart disease become an area of research to save patient lives. During the pandemic period, the number of cardiac arrest cases at home has drastically increased due to inaccurate predictions and delay in seeking medical attention. The health care industry works on processing of huge data and the solution for this is machine learning. Data science process large amount of data to make intelligent health care decisions thereby avoids risk and alert the patients. In this paper, a comparative analysis of different machine learning classifiers based on dataset to predict the chance of heart disease with minimal attributes. The ML algorithms used for HD prediction are K- Nearest Neighbour, Gradient boosting Classifier, support vector Machine, Naive bayes, Logistic Regression and Random Forest algorithm. The paper also finds the correlation between different attributes and hence using them efficiently for prediction of heart attack. © 2022 IEEE.

关键词： Forecasting

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Dynamic Fuzzy Parser to Parse English sentence Using POs Tagger and Fuzzy Max-Min Technique

Dynamic Fuzzy Parser to Parse English Sentence Using POS Tag...

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2018 International Conference on Current Trends towards Converging Technologies, ICCTCT 2018

作者： Kanakaraddi, suvarna G. Nandval, suvarna s. Department of Computer Science Engineering B v B College of Engineering Technology Hubli Karnataka India Department of Computer Science Engineering PDA College of Engineering Kalaburagi Karnataka India

ISBN: (纸本)9781538637012

Natural Language (NL) is an essential part of ourlife. Humans use language for communication. NL is a prevailing tool used by the humans to convey the information. Natural Language Understanding (NLU) is a major challenge in Natural Language Processing (NLP). NLP is a part of Artificial Intelligence (AI). NLP provides a significant tool for communication. It attempts to produces noise free data and conversion of noise to text. NLU is having different levels. This paper presents the issue with respect to one of the level such as syntax analysis. To provide a solution for syntax analysis, dynamic fuzzy parser is designed and implemented to parse the English input sentences. Traditional approach of parsing is enhanced by applying fuzzy logic. This helps to know the syntactic correctness of the sentence. Penns tree bank parts of speech tags are used for the Parts of speech Tagger (POs). POs tagger assigns the parts of speech tags for the input English sentence. Then these tags of the words are parsed using the grammar rules. Finally the result is displayed to represent the number of words parsed in a sentence with its associated fuzzy membership value. This parser produces Precision value of 1(100%), Recall value of 0.92 (92%) and F-measure value of 0.9583 for the sample of 50 correct and 50 incorrect sentences. © 2018 IEEE.

关键词： syntactics

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Problem and project-based learning in scripting lab 4

Problem and project-based learning in scripting lab

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4th IEEE International Conference on MOOCs, Innovation and Technology in Education, MITE 2016

作者： Giraddi, shantala Yaligar, shilpa Kavitha, H.s. Department of Computer Science and Engineering B.V.B College of Engineering and Technology Karnataka India

ISBN: (纸本)9781509010622

scripting language employ high-level constructs to interpret and execute one command at a time. In general scripting languages are easier to learn and faster to code than structured and compiled languages such as C and C++. scripting languages have many important advantages over traditional programming languages. In future the usage of these languages is likely to increase. In this paper we discuss and report our experience in teaching scripting languages lab at the undergraduate level, 4th semester. scripting language is an umbrella term used for languages like unix shell, TCL, perl, Java, python and LIsP. Out of these, we have chosen UNIX shell programming and python for our curriculum. The authors report various pedagogical activities like multiple assignments, peer assessment within a group, self learning through e-resources and course project that were employed during the course. The course projects were specially designed so as to make students explore the vast number of python packages. The authors found that these activities definitely enhance the learning experience and there was a remarkable change in the learning level of the students as compared to previous years as evident in the grades obtained by the students. © 2016 IEEE.

关键词： Python

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Preferential theme matrix for minor project 4

Preferential theme matrix for minor project

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4th IEEE International Conference on MOOCs, Innovation and Technology in Education, MITE 2016

作者： sujatha, C. Jayalaxmi, G.N. vijaykumar, b. Raikar, M.M. suvarna, G.K. Karibasappa, G.K. Totad, s.G. Department of Computer Science and Engineering B v B College of Engineering and Technology Hubballi Karnataka India

ISBN: (纸本)9781509010622

We propose a preferential theme matrix for selecting a problem in carrying out the minor project for 6th semester computer science and engineering (CsE) students. The matrix comprises of emerging technologies, research areas and the application domains as the parameters. It included Internet of Things, Cloud Computing, Mobility and Web Technology as emerging technologies. The focused research areas were on big data & analytics and image processing. The application domains were focused on social relevance such as environment monitoring, health care, safety and security, agriculture. And an additional open choice (others) was mentioned which gave an opportunity for students to explore other new technologies and research areas. broadly the phases of conducting minor project are classified as team formation, problem identification/selection and project execution which in turn has several sub phases. In team formation students were given freedom to choose their partners with a team size of 4/5 within the same division. This phase was analyzed with respect to Cumulative Grade Point Average (CGPA) grades of team members, which showed a good distribution of grades within the teams and across the entire teams. In problem identification the teams discussed about theme matrix with the assigned guides and presented the project synopsis to the evaluators. After these phases the project execution which in turn included requirement specifications, design, implementation, testing and deployment. To meet some of the pre-requisites for carrying out the projects, workshops on IoT, Cloud computing and open source design tools were organized. For promoting self learning in students, MOOC on the relevant technologies of their projects was introduced as part of minor project. The Continuous Internal Evaluation (CIE) of minor project focused on the processes of software engineering and semester End Exam (sEE) was focused on product/prototype development, technical skills and external recog

关键词： smart city

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Enhancing Consumer sentiment Analysis and Product Improvement with Aspect-Optimized Adaptive GCN (AOA-GCN) 2

Enhancing Consumer Sentiment Analysis and Product Improvemen...

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2nd IEEE International Conference on Integrated Intelligence and Communication systems, ICIICs 2024

作者： Annamalai, sivakumar Nandhini Priya, T. basker, N. Yuvaraj, s. Ramasami, s. suresh, D. Department of Safety and Fire Engineering Excel Engineering College Namakkal Komarapalayam India Department of Electrical and Electronics Engineering Excel Engineering College Namakkal Komarapalayam India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Mechanical Engineering V.S.B Engineering College Karur India

ISBN: (纸本)9798331504960

The exponential growth in online reviews offers a company the most precious source of information regarding the customers' emotions and potential product improvement areas. However, it is very challenging to derive meaningful information from the unstructured data. The work presents a new model called Aspect-Optimized Adaptive GCN (AOA-GCN), which combines hierarchical structuring with aspect-based optimization for effective consumer sentiment analysis. The AOA-GCN applies a two-level analysis framework that covers both global sentiments and detailed aspect-specific sentiments in order to build a full understanding of the customer review. The model is essentially based on an Adaptive Lightweight GCN with pruning techniques, ensuring that it does not only reduce computations but also takes into account influential aspects along with its high accuracy, even across extended datasets. Dynamic attention in AOA-GCN will help make critical product features in the customer reviews focus on the issues so improvements can be made on those products specifically. Experimental results show that the improved performance is actually as high as 6.45% over global sentiment classification, 9.12% over aspect-based sentiment analysis, and 7.35% over cross-domain generalization, and training time is reduced by 35%. The model also exhibits improved interpretability with clearer insights into customer preferences. The proposed AOA-GCN becomes an effective, efficient, and interpretable business tool for optimizing products through insights from consumers. © 2024 IEEE.

关键词： sales

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Machine Learning-based Detection of stress Levels in students with social Media Addiction

Machine Learning-Based Detection of Stress Levels in Student...

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Integrated Circuits and Communication systems (ICICACs), IEEE International Conference on

作者： Anuradha Parasar sujatha N sweta Priya Pramod s A. sasi Kumar Kogila Palanimuthu School of Liberal Education Galgotias University Dankaur India Department of Mathematics B. M. S. College of Engineering Bangalore India Amity School of Communication Amity University Patna India Department of Mathematics Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Applications (BCA & MCA) School of Science and Computer Studies (SSCS) CMR University Bangalore India Department of Pediatric and Child Health Nursing Institute of Health Sciences Dambi Dollo University Kellam Ethiopia

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The epidemic has shown that social networks (sNs) are prevalent and necessary for human connections. However, sNs are easy to access, which may make them addictive, especially for kids. The study literature suggests several psychological and physiological risk factors for social media addiction. The suggested method includes preprocessing, feature selection, and model training. The content was preprocessed to remove all URLs to guarantee that web links would not interfere with the analysis. The mRMR method and Chi-square test were the most practical and effective feature selection methods. Training the model using the Attention-based GCN. The recommended model outperformed the popular Random Forest (RF) and established GCN models. Attention-based GCN boosted accuracy to 92.84%. It systematically identifies social media addiction risk factors using cutting-edge feature selection, model training, and preprocessing. Results suggest Attention-based GCN can overcome social media addiction identification challenges.

关键词： Training Radio frequency Accuracy Uncertainty social networking (online) Addiction Human factors Feature extraction Integrated circuit modeling stress

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Detecting dynamic topics in social network using citation based anomaly detection

Detecting dynamic topics in social network using citation ba...

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International Conference on (IsCO) Intelligent systems and Control

作者： P. Kayalvizhi C.Anoor selvi Department of Computer Science and Engineering V.S.B Engineering College Karur India

sharing of information with their friends on social networks has become an integral part in everyone's life. The post made by the social network user may have text, image and video. The previous approaches, [1] link anomaly detection method and [2] text-based anomaly detection method may not discover the dynamic post immediately as it concentrates on either links or text respectively, but the posts contain not only text or only links it may have images, videos or the combinations of links, text and image. The proposed model is to identify the new dynamic topics by analyzing the content of the message by extracting features of text, using [6] WordNet tool, this tool takes the words as synsets and the synonym, homonyms are identified The user may share the post by forwarding the post to their friends that may create a forward link. The normal behavior of user such as frequent forwarding friends list and the number of people on the list is considered for training model, future forwarding behavior is predicted and the anomalous behavior is detected using the training data set, from that the anomaly score is calculated. Aggregate the anomaly score from hundred users for each post, with that aggregated score and analyze the aggregated result with sequentially Discounting Normalized Maximum-Likelihood (sDNML) coding [3] and Moving Average Convergence Divergence (MACD) burst-detection method [5] and pinpoint which post is about the dynamic topic that is discussed in a social network. In this approach, the change-point score is compared with the dynamically optimized threshold, if the score exceeds the threshold value that particular post will be pinpointed.

关键词： Convergence Encoding semantics Data mining

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