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Enacting Rural Empowerment: Implementing Cooperative Commerce in Communities using Machine Learning 2

Enacting Rural Empowerment: Implementing Cooperative Commerc...

引用

2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Skandarsini, R.R. Sangavi, M.S. Harika, Ravitla Varun, C.M. RMK Engineering College Dept. of Computer Science and Business Systems Thiruvallur India

ISBN: (纸本)9798350365269

In order to revitalize rural economies through the empowerment of self-help organizations and the promotion of economic growth, this study offers a revolutionary cooperative commerce platform. The platform integrates cutting-edge features such as digital ledger and inventory management for sellers, demand forecasting, and dynamic pricing, thereby fostering optimized production and efficient marketplaces. Central to the platform's efficacy is the implementation of user segmentation, which categorizes users based on income and spending behaviors, enabling targeted marketing strategies and continuous application improvements to enhance user engagement and profitability. Leveraging machine learning algorithms, personalized user experiences are cultivated, elevating buyer satisfaction and overall engagement. Additionally, administrative tools provide valuable insights for informed decision-making. Preliminary results demonstrate improved financial management for self-help groups, heightened economic activities, and an enriched user-centric experience. Identified challenges and proposed future enhancements underscore the platform's commitment to adaptability and responsiveness to the evolving needs of rural communities, ensuring sustained relevance and impact. © 2024 IEEE.

关键词： Predictive analytics

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Vyoma-ADS : An AI-Driven Tool for Adaptive Monitoring and Anomaly Detection for Space Agriculture

Vyoma-ADS : An AI-Driven Tool for Adaptive Monitoring and An...

引用

2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Das, Rajdeep Sharma, Vaishnavi Kumari, Priyanka Raj, Abhishek Das, Sudipta De, Indrajit Institute of Engineering and Management Dept. of Computer Science and Business Systems Kolkata India

ISBN: (纸本)9798350390759

As humanity ventures further into space exploration, securing sustainable resources becomes paramount. The success of space farming depends not only on the ability to cultivate crops but also on the effective management of environmental conditions. Introducing Vyoma-ADS: an innovative system that integrates IoT technology with an adaptive Anomaly Detection System, specifically designed for space agriculture. Our study underscores the crucial role of environmental monitoring within space shuttles, where even minor fluctuations can have a significant impact on plant growth and productivity. To tackle these challenges, we designed a compact IoT-enabled system to monitor the moisture content in the soil, the temperature and humidity of the surrounding environment, and the ppm value of unwanted gases in the atmosphere. Through rigorous testing and analysis, we established optimal thresholds conducive to plant growth in the unique conditions of space. Recognizing the inherent variability and occasional anomalies in environmental data, we investigated three distinct machine learning models for anomaly detection: Isolation Forest, One-Class Support Vector Machines (OCSVM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Our findings reveal that DBSCAN outperforms other techniques in identifying anomalies amidst fluctuating data. Additionally, we detail our approach to data visualization using a SaaS cloud platform, which enhances the comprehensibility of the study and facilitates better decision-making processes. This paper demonstrates how the integration of IoT monitoring and advanced anomaly detection methods can significantly improve the reliability and efficiency of space agriculture. By leveraging these technologies, we aim to create a robust system capable of maintaining optimal growing conditions for plants in space. © 2024 IEEE.

关键词： Support vector machines

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Acoustic Device for Detecting Red Palm Weevil Using Deep Learning and IoT

Acoustic Device for Detecting Red Palm Weevil Using Deep Lea...

引用

2023 International Conference on Microelectronics, ICM 2023

作者： Atia, Maryam M. Gouda, Amr I. Ismail, Mahmoud G. Salem, Mohammed A.-M Elghany, Mohamed A. Abd German University in Cairo Computer Engineering Dept. Egypt German University in Cairo Electronics Engineering Dept. Egypt Tu Darmstadt Integrated Electronic Systems Lab Germany

ISBN: (纸本)9798350380828

The Red Palm Weevil (RPW) is a major threat to the date palm industry, requiring early detection strategies. This paper presents a comprehensive exploration of two bioacoustic sensor prototypes tailored for the early detection of RPW. The first prototype employs a Raspberry Pi 4 coupled with a Convolutional Neural Network (CNN), achieving a detection accuracy of 99.02%. The second device, built with an ESP32-WROVER module, integrates a Simple Neural Network (SNN) for audio classification and attains an accuracy of 98.79%. This design's affordability and low power consumption make it particularly suited for large-scale deployment in agricultural settings. This paper delves deeper into the bioacoustic sensor technology used, the dataset chosen for CNN and SNN models training, and provides a comparison between the two device designs in terms of cost and efficiency. Our project's commitment to iterative improvement aims to provide an effective solution to counteract RPW's destructive effects on date palm cultivation. © 2023 IEEE.

关键词： Deep learning

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Intelligent Doctor Chatbot 4

Intelligent Doctor Chatbot

引用

4th International Multidisciplinary Information Technology and engineering Conference, IMITEC 2024

作者： Khoza, Mahlori Olaifa, Moses Owolawi, Pius Malele, Vusi Tshwane University of Technology Dept. of Computer Systems Engineering Tshwane South Africa

ISBN: (纸本)9798350387988

This study proposes a cutting edge Intelligent Doctor Chatbot that improves healthcare delivery and provides patients with easily accessible and customized health related information through the use of Natural language processing (NLP) and artificial intelligence (AI) technologies. These technologies are used by the chatbot to provide accurate medical advice based on symptom analysis and general health guidance in an interactive manner. The doctor chatbot addresses the growing demand for reliable healthcare information and assistance, particularly in situations where access to medical professionals is limited. By leveraging its advanced algorithms and medical knowledge base, the chatbot provides users with quick and accurate answers to their health-related questions. A key feature of the doctor chatbot is that users describe their symptoms and receive preliminary assessments on potential causes and suggested courses of action. It allows people to take an active role in their healthcare management and preventive measures. © 2024 IEEE.

关键词： Diagnosis

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Dual Auction Peer-to-Peer Control Scheme for Empowering Prosumers Participation in the Local Energy Markets 4

Dual Auction Peer-to-Peer Control Scheme for Empowering Pros...

引用

4th International Conference on Computing, Mathematics and engineering Technologies, iCoMET 2023

作者： Ikram, Muhammad Ahmed, Salman Khan Marwat, Safdar Nawaz University of Engineering and Technology Dept. of Computer Systems Engineering Peshawar Pakistan

ISBN: (纸本)9798350335316

The emerging trend of distributed and renewable energy sources encourages energy trading in the electricity market. Various forms of energy markets are evolving for successful energy trading. The local energy market is one of them in which various participants are actively coordinating for optimal utilization of power. Such type of local energy markets provides individual prosumers to trade their surplus energy in the neighbourhood in peer-to-peer topology. The complexity of such type of local energy market is increasing in terms of structure, control, coordination, and cost estimation. For the cooperative behaviour of the local energy markets, a dual auction coordination control scheme is important to encourage more prosumers for energy trading. We proposed a novel scheme that performs dual auctions for prosumers in a decentralized fashion. We assessed dual auction matching by coordinating prosumers and consumers to sell and purchase energy in the local energy market. The aggregator performed balancing in the proposed dual auction scheme as an autonomous entity. The results were simulated in MATLAB and JADE for the complex prosumer peer-to-peer network. Our proposed scheme shows each peer dual auction matching improves from 0.95 to 2.03 for optimal hierarchal energy trading in energy markets. For peer participation, the participation ratio increased from 0.684 to 1.74 and optimal aggregator coordination from 9.18% to 19% in the local energy markets. © 2023 IEEE.

关键词： Smart power grids

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LightNN: An Optimized Deep Learning Framework for Enhanced Fruit Classification

LightNN: An Optimized Deep Learning Framework for Enhanced F...

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2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Afroj, Maharin Jahan, Israt Islam, Md Polash Saifur Rahman, Md. Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Stockholm University Dept. of Computer and Systems Sciences Stockholm Sweden

ISBN: (纸本)9798331529765

The crucial field of fruit classification in computer vision and deep learning brings about breakthroughs that have broad applications in the retail and agricultural industries. By automating the identification and sorting of various fruit types, these technologies enhance efficiency, accuracy, and scalability, benefiting processes from farm production to market distribution. The integration of sophisticated algorithms and neural networks not only improves the speed and reliability of fruit classification but also offers the potential for real-time quality control and inventory management, revolutionizing traditional methods and paving the way for more innovative, data-driven approaches in these sectors. This study introduces LightNN, a novel and effective paradigm with a lightweight architecture that tackles the complex problems related to computational efficiency. Combining a small classifier with a simplified feature extractor, LightNN achieves the ideal balance between simplicity and accuracy. The feature extractor is capable of extracting hierarchical features from RGB images by using convolutional layers with ReLU activations and max-pooling procedures. The flattened output is then processed by the classifier, which consists of fully connected layers with ReLU activations and dropout regularisation. Remarkably, LightNN, with just 298,835 parameters, manages to get a respectable 96.37% test accuracy in fruit categorization tasks. The results highlight LightNN's promise as an effective and efficient fruit classifying method, particularly in situations with constrained computational resources. © 2024 IEEE.

关键词： Fruits

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Augmented Karuta: Interactive Playing Cards on the Floor for Learning Local Culture 7

Augmented Karuta: Interactive Playing Cards on the Floor for...

引用

7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Umezu, Nobuyuki Bando, Hideki Ibaraki University Dept. of Computer and Information Sciences Hitachi Japan Ibaraki University Dept. of Mechanical Systems Engineering Hitachi Japan

ISBN: (纸本)9798331521578

This paper introduces Augmented Karuta, an interactive floor projection experience inspired by traditional Japanese playing cards, known as 'Karuta'. Designed to engage users and stimulate interest in local cultures, history, and traditions, the system utilizes three projectors. Two projectors project 46 'grabbing' cards onto the floor screen of 4.1 x 3.7 (m), while the third displays a corresponding 'reading' card on a wall screen. Each matching pair of cards shares a poem or sentence that begins with one of the Japanese phonograms. During a session, users actively engage by stepping onto the projected 'grabbing' card that matches the 'reading' card displayed on the wall, effectively blending physical movement with cognitive skills to find the correct match. By integrating a two-dimensional LiDAR sensor, the system tracks the user's position on the interactive floor. This tracking is vital for the proposed system to immediately respond to the user's actions, ensuring an immersive and seamless interaction with the digital karutas on the floor. We conducted evaluation experiments at two museums, gathering positive feedback from 57 participants. Future work will focus on quantitatively evaluating the system's impact on learning effectiveness. © 2025 IEEE.

关键词： Tropics

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

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2024 International Conference on Artificial Intelligence, computer, Data Sciences, and Applications, ACDSA 2024

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Thiagarajan, Priya Sundarakantham, K. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350394528

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better. © 2024 IEEE.

关键词： Recurrent neural networks

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Enhancing Train Transportation in Sri Lanka: A Smart IOT Based Multi-Subsystem Approach Using MQTT

Enhancing Train Transportation in Sri Lanka: A Smart IOT Bas...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Balasingham, Dhanushka Samarathunga, Sadeesha Bandara, Anuththara Arachchige, Gayantha Godakanda Gamage, Narmada Wijayaraja, Jaliya L. Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Sri Lanka

ISBN: (纸本)9798350350821

This research proposes a system as a solution for the challenges faced by Sri Lanka's historic railway system, such as scheduling delays, overcrowding, manual ticketing, and management inefficiencies. It proposes a multi-subsystem approach, incorporating GPS tracking, RFID-based e-ticketing, seat reservation, and vision-based people counting. The GPS-based real-time train tracking system performs accurately within 24 meters, with the MQTT protocol showing twice the speed of the HTTP-based system. All subsystems use the MQTT protocol to enhance efficiency, reliability, and passenger experience. The study's data and methodology demonstrate the effectiveness of these innovations in improving scheduling, passenger flow, and overall system performance, offering promising solutions for modernizing Sri Lanka's railway infrastructure. © 2024 IEEE.

关键词： Global positioning system

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Explainable Long-Term Forecasting of Air Pollution

Explainable Long-Term Forecasting of Air Pollution

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Mutakabbir, Abdul Ajila, Samuel A. Carleton University Dept. of Systems and Computer Engineering OttawaON Canada

ISBN: (纸本)9798350324457

Long-term air pollution forecasting is essential for making public policies and issuing warnings. This will reduce the impact of pollution on the environment and human health. This research focuses on achieving long-term forecasting of air pollution attributes $(PM 2.5, PM_{10}, SO_{2}$, $NO_{2}, CO$, and $O_{3})$ by providing minimal historical data as input to the model. The best-performing models in this research produced between a 1% increase in RMSE for certain pollutants to a 50% decrease in RMSE for other air pollution attributes (except $CO)$ compared to the initial research on compositional learning, while being able to forecast the trend and seasonality accurately for more than one year into the future. The training time for each pollution attribute model dropped to 4 seconds compared to 1800 seconds in the case of the compositional learning method. This paper also delves into the challenges with long-term forecasting for the Beijing air quality dataset and discusses approaches to overcome these challenges. © 2023 IEEE.

关键词： Air Quality Index ARIMA Bagging Big Data Analytics Boosting Deep Learning Explainable AI Forecasting Machine Learning NeuralProphet Random Committee Random Forest Timeseries Forecasting

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