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2024 international conference on Emerging smart computing and Informatics, ESCI 2024

2024 International Conference on Emerging Smart Computing an...

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6th IEEE international conference on Emerging smart computing and Informatics, ESCI 2024

ISBN: (纸本)9798350306613

The proceedings contain 192 papers. The topics discussed include: reducing gender and ethnicity bias in personality trait prediction;MDLGO: integrated multimodal deep learning framework for enhanced precision in glaucoma diagnosis using OCT A-scans;advancing manufacturing energy efficiency: the role of AI and web-based tools;integrated healthcare system for saline monitoring, patient communication, and heart disease prediction using iot and machine learning algorithms;iot based real time drainage monitoring system;software defect prediction by logistic regression with gradient descent cost computation;ONU: A reinforcement load balancing approach for resource-aware task allocation in distributed systems;a novel approach using feature fusion for android malware detection and classification;and graph based analysis technique for identification of key functionalities in malicious iot binaries.

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Cropsmart-A Crop Prediction Application Using machine learning 12th

CropSmart-A Crop Prediction Application Using Machine Learni...

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12th international conference on Frontiers of Intelligent computing: Theory and Applications, FICTA 2024

作者： Madugula, Vidya Perumandla, Anshu Likhitha, Singari Gupta, Manu Dholvan, Mohan Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819601424

India is a country whose primary sources of income are agriculture and farming. Varied soil in the nation enables farmers to grow a wide range of crops all year round. The agricultural industry has been the focus of in-depth investigation in the past several years due to recent technological advances, including machine learning and smart computing. The proposed work aims to develop a web application for crop selection supporting farmers and improving agricultural productivity. Various supervised machine learning classifiers are used for crop prediction based on environmental factors and soil conditions. Performance metrics are included in the evaluation of several machine learning models. The results attained show that the bagging classifier achieved better results for crop prediction with an accuracy of 97%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： smart agriculture

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15th international Symposium on Ambient Intelligence, ISAmI 2024

15th International Symposium on Ambient Intelligence, ISAmI ...

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15th international Symposium on Ambient Intelligence, ISAmI 2024

ISBN: (纸本)9783031831164

The proceedings contain 36 papers. The special focus in this conference is on Ambient Intelligence. The topics include: Adaptive Vehicle Detection in Urban Environments: A Self-learning Approach;next Application Prediction Enhanced for New Users by Using Data Augmentation with Generative Adversarial Networks;addressing Privacy Challenges in Internet of Things (iot) Applications;AI-Based Recommendation System for the Retail Industry;m-Health Solution to Support the Monitoring of Elderly Patients After Hospital Discharge;reusing Past machine learning Models Based on Data Similarity Metrics;a Do-It-Yourself Approach to Ambient Assisted Living;integrating Artificial Intelligence and Heterogeneous Sources in smart Environments – Part 1: The Scalable Architecture;a Multi-scene Dataset for Leading Blind Individual Identification in Public Spaces;ADEPT Framework: Optimizing Data Flow in Dynamic Environments for Ambient Assisted Living;KPIs for the Evaluation of Footwear Supply Forecasting Using Holt-winters;head Mapping as a Strategy for Firearms Detection: A Literature Review;evaluation of Employment Contentment Among Nurses in Critical Care Settings;architecture of an iot Platform for the Optimization of Water Resources in Community Kitchens in Cali, Colombia;An AI-Based System for the Automatic Assessment of Open Data Sources’ Quality and Legality;hybrid Approach to Voice-Based Classification of Parkinson’s Disease;Application of Federated learning and xAI in I4.0 - A Case Study;inventory Management and Traceability in Civil Engineering Through iot and Explainable Artificial Intelligence;artificial Intelligence Applied to Counterfeit Websites Detection;integrating Artificial Intelligence and Heterogeneous Sources in smart Environments Part 2: A Case Study;optimizing Building Energy Management Leveraging Adaptive Edge computing for Enhanced Efficiency and Occupant Well-Being;redefining Transportation Modelling with Advanced Agent-Based Simulations;Connecting Real-World Da

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19th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

19th International Conference on Soft Computing Models in In...

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19th international conference on Soft computing Models in Industrial and Environmental Applications, SOCO 2024

ISBN: (纸本)9783031750090

The proceedings contain 61 papers. The special focus in this conference is on Soft computing Models in Industrial and Environmental Applications. The topics include: Improving the Efficiency of Production Processes by Reducing Human Errors Using Intelligent Methods;automated Semantic Labelling of Images Generated with Deep Diffusion Probabilistic Models;wastewater Quality Indicator Estimation Using machine learning and Data Augmentation Techniques;a Virtual Avatar for Sign Language Signing;comparison of D-Wave Quantum computing Environment Solvers for a Two-machine Jobs Scheduling Problem;hybrid Fuzzy-Based Method for Optimally Selecting Aircraft for Low-Cost Carriers Operating Medium-Haul Flights;two Way Ranging Positioning Methods Analysis Under Optimized Sensor Deployments;human Acoustic Events Detection as Anomalies in Industrial Environments Using Shallow Unsupervised Techniques;machine learning in the Last-Mile Delivery: Modified Q-learning for the TSP;iot and AI Based Security System – Design and Implementation;characterization of Cattle Behavior Based on Dimensional Reduction Techniques;time Series Forecasting in Agriculture: Explainable Deep learning with Lagged Feature Selection;artificial Intelligence Methodology for Run-to-Target Manufacturing;multi-robot Path Planning Problem Applied to Real World Enviroment;an Approach of Optimization in Capacitated Vehicle Routing Problem in Logistics Distribution;bayesian Generation of Synthetic Data;learning-Based Models for Intelligent Control Over Air Conditioning Units in a smart Building;predicting Power Generation from Photovoltaic Energy System;photovoltaic Generation Forecasting for Intelligent Management in Energy Communities;application of Neural Models in Solar Radiation Measurements for a Better Data Gathering Quality;caravels: A Decentralized Container-Based Infrastructure for Sustainable Human-Centric Intelligent Energy Communities;Production Scheduling for Energy Cost Optimization with Constraints: Com

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Trailblazing Strategy: Implementing iot-Powered machine learning to Identify Harmful Potatoes 12th

Trailblazing Strategy: Implementing IoT-Powered Machine Lear...

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12th international conference on Recent Trends in computing, ICRTC 2024

作者： Gopinath, D. Yashoda, M.B. Ananthi, P. Balamurugan, M. Bengaluru India Department of Computer Technology Kongu Engineering College Tamil Nadu Erode India Department of MCA Acharya Institute of Graduate Studies Bengaluru India

ISBN: (纸本)9789819789450

The agricultural Internet of Things (iot) has revolutionized agricultural output by improving productivity, quality of products, reducing labour costs, increasing farmer incomes, and modernizing agriculture. With agriculture consuming over 70% of freshwater and utilizing excessive chemicals, the need for efficient management of data and information is crucial in making timely decisions. To achieve a smart approach, farming needs to embrace new technologies to maximize inputs and improve production sustainability. By integrating iot and communication technologies, farmers can efficiently manage soil tillage, agrochemicals, fertilizers, and water inputs to enhance profitability and output. The proposed control system using node sensors aims to detect poisoned potatoes while providing data management through smartphone and web applications. The system consists of hardware for collecting field data, a web application for data manipulation and analysis, and a mobile application for user control. This paper provides an overview of agricultural iot, analysing its current state and system architecture. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： smart agriculture

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Proceedings - 2024 IEEE international conference on smart computing, smartCOMP 2024

Proceedings - 2024 IEEE International Conference on Smart Co...

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10th IEEE international conference on smart computing, smartCOMP 2024

ISBN: (纸本)9798350349948

The proceedings contain 63 papers. The topics discussed include: farm households food security status automation through supervised learning approach: a look at agroecological farms;from calls to scales: harnessing smartphone accelerometer and vibration for daily mass measurement;a machine learning-based temperature control and security protection for smart buildings;advancing smart computing: a comprehensive tutorial to 3D point clouds from installation to efficient processing and context recognition for next-gen applications;DP-MTFL: differentially private multi-tier federated learning for iot applications;acoustic camera-based anomaly detection for wind turbines;and clustering-enhanced reinforcement learning for adaptive offloading in resource-constrained devices.

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Advanced Education and smart learning Technologies

Advanced Education and Smart Learning Technologies

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international conference on smart Business and technology, ICSBT 2024

作者： Zizoune, Asmae Dakki, Mohamed Zizoune, Aicha Ziti, Soumia Hajhouj, Salah Eddine El IPSS Faculty of Sciences University Mohammed V in Rabat Rabat Morocco Scientific Institute University Mohammed V in Rabat Rabat Morocco LMSA Faculty of Sciences University Mohammed V in Rabat Rabat Morocco

ISBN: (纸本)9783031866975

A paradigm change in learning methodologies has been sparked by the introduction of intelligent learning systems and smart education, which have had a significant impact on the field of education. The goal of improving the effectiveness of teaching and learning processes is made possible by the use of technologies like artificial intelligence (AI), the Internet of Things (iot), and data analytics. Personalized learning experiences and dynamic, adaptable learning settings, such as interactive classrooms, are used to accomplish this. AI and machine learning-powered intelligent educational systems analyze student data in real time and provide prompt feedback and customized recommendations. All educational levels make use of these tools, which facilitate both blended and distance learning. Although they recognize their capacity to close gaps, issues like digital literacy and data privacy must be addressed. It is thought that by offering interesting, adaptable, and data-driven learning experiences, intelligent learning systems and smart education, when properly implemented, can revolutionize education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Attack Detection in smart Home iot Networks: A Survey on Challenges, Methods and Analysis 14th

Attack Detection in Smart Home IoT Networks: A Survey on Cha...

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14th EAI international conference on Broadband Communications, Networks, and Systems, BROADNETS 2024

作者： Rreddy, M. Vinay Kuma Lathigara, Amit Reddy, Muthangi Kantha RK University Gujarat India Computer Engineering RK University Gujarat India Shri Vishnu Engineering College for Women Andhra Pradesh India

ISBN: (纸本)9783031811678

The ubiquity of Internet of Things (iot) gadgets in smart homes has transformed our interactions with our living environments by providing never-before-seen levels of automation and convenience. However, because iot devices are becoming possible targets for malicious attacks, this broad connectivity also poses serious security risks. Ensuring the privacy, safety, and integrity of smart home ecosystems requires prompt detection and mitigation of these threats. Data from iot devices is gathered, pre-processed, feature engineered, labelled, and divided into training, validation, and testing sets as part of a machine learning method to threat detection in smart home iot networks. The process of choosing and training appropriate machine learning models—which can include everything from classification techniques to anomaly detection algorithms—is crucial. Methods are surveyed to review different types of cyber-attacks, such as denial-of-service (DoS), distributed denial-of-service (DDoS), probing, user-to-root (U2R), remote-to-local (R2L), botnet attack, spoofing, and man-in-the-middle (MITM) attacks. To protect user information, data anonymization and encryption techniques are used with privacy considerations. Another strategy that has been put forth aims to improve the security of iot networks in smart homes by providing a strong defence against new threats and equipping users with the information and resources they need to keep their connected world safe. To provide a full overview of the numerous advancements in this field, a list of all works published in the literature to date is incorporated. Lastly, the study also includes suggestions for future research directions. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： smart homes

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Anomaly detection using different types of machine learning models in the context of "smart maintenance technologies in the manufacturing industry" 6

Anomaly detection using different types of machine learning ...

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6th international conference on Industry 4.0 and smart Manufacturing, ISM 2024

作者： Giliyana, San Mälardalen University Universitetsplan 1 Västeras Sweden Mälardalen Industrial Technology Center John Engellaus Gata 1 Eskilstuna633 61 Sweden

Industry 4.0 presents nine technologies, including the Industrial Internet of Things (Iiot), Big Data and Analytics, Cloud computing, etc. The progress of Industry 4.0 places a new demand for maintenance. Some of the nine technologies of Industry 4.0, such as Iiot, Big Data and Analytics, Cloud computing and Augmented Reality (AR), as well as Cyber-Physical System (CPS) and machine learning, play an important in the development of smart maintenance technologies. In smart maintenance research, it is presented how Iiot can be used for machine connection and maintenance data collection, machine learning models for maintenance data analysis and failure prediction, AR for maintenance instructions, etc. Although previous smart maintenance research presents many technologies for smart maintenance, the manufacturing companies still face many implementation challenges when implementing and using to add benefits to maintenance organizations in line with companies main goals. Many manufacturing companies still utilize reactive maintenance and are experiencing too much downtime. In this paper, I have shown how two machine learning models, Isolation Forest and Regression Learner, and the statistical technique, Interquartile range (IQR), can be applied in order to detect anomalies in an unsupervised dataset, consisting of travel time for a linear guide, and temperature, in a drill station, which is part of a Cyber-Physical production system, located at a smart production laboratory in Sweden. © 2025 The Authors.

关键词： smart manufacturing

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22nd international conference on Service-Oriented computing, ICSOC 2024

22nd International Conference on Service-Oriented Computing,...

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22nd international conference on Service-Oriented computing, ICSOC 2024

ISBN: (纸本)9789819608041

The proceedings contain 55 papers. The special focus in this conference is on Service-Oriented computing. The topics include: ABBA-VSM: Time Series Classification Using Symbolic Representation on the Edge;An Energy-Efficient Partition and Offloading Method for Multi-DNN Applications in Edge-End Collaboration Environments;crowdsourcing Task Assignment with Category and Mobile Combined Preference learning;federated learning as a Service for Hierarchical Edge Networks with Heterogeneous Models;optimizing Traffic Allocation for Multi-replica Microservice Deployments in Edge Cloud;an Event-B Based Approach for Horizontally Scalable iot Applications;efficient Provisioning of iot Energy Services;attention-Driven Conflict Management in smart iot-Based Systems;benchmarking Deep learning Models for Object Detection on Edge computing Devices;LLM Enhanced Representation for Cold Start Service Recommendation;Combining Generative AI and PPTalk Service Specification for Dynamic and Adaptive Task-Oriented Chatbots;Automated Generation of BPMN Processes from Textual Requirements;Plug-and-Play Performance Estimation for LLM Services without Relying on Labeled Data;UELLM: A Unified and Efficient Approach for Large Language Model Inference Serving;Service-Oriented Requirements Elicitation Through Systematic Questionnaire Design: A Problem-Driven GenAI Approach;assessing Large Language Models Effectiveness in Outdated Method Renaming;DynaEDI: Decentralized Integrity Verification for Dynamic Edge Data;Heterogeneous Multi Relation Trust for Siot Service Recommendation;a Context-Aware Service Framework for Detecting Fake Images;Bias Exposed: The BiaXposer Framework for NLP Fairness;flowShredder: A Protocol-Independent in-Network Security Service in the Cloud;HiGPP: A History-Informed Graph-Based Process Predictor for Next Activity;from Visual Choreographies to Flexible Information Protocols;architectural Elements of Decentralized Process Management Systems;LLM-Based Business Process Docume

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