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Automated Solar-Powered Corn Planting Robot 8

Automated Solar-Powered Corn Planting Robot

引用

8th IEEE Symposium on Wireless Technology and Applications, ISWTA 2024

作者： Agustin, Michael Adrian P. Lopez, Ferdinand H. Tiu Huan, Adrean Rey L. Guaro, Jenith F. Tiba, Joriel S. College of Engineering Architecture and Computing Notre Dame of Marbel University Koronadal City Philippines

ISBN: (纸本)9798350351354

The Philippine economy is based primarily on agriculture. Nowadays, there's a bigger need than ever for efficient and automated farming methods. Slow and poor production has been one of the major problems of agriculture. The best approach to deal with these problems is for the agricultural industry to become more automated. This leads to more systematic farming, which raises productivity rates. The purpose of this study is to design and create an automated robot that plants corn seeds automatically. To make the most of the tropical climate in the area, this automated robot is also integrated with a solar power. The Automated Solar-Powered Corn Planting Robot (ASP-CPR) was designed by the researchers with the following functions: it can drill farmed soil with a regular depth and distance and release a standard amount of corn seeds on the drilled soil automatically that follows the standard set-up of the Department of Agriculture. Upon multiple testing, results revealed the accuracy on releasing the standard number of seeds is 94%, 98 % for the depth of drilled soil, and 92% accuracy for the distance taken by ASP-CPR between planted seeds. Additionally, the researchers integrated a notification system that notifies the user if the robot experiences low power or immovability, or if the seed container is empty. Moreover, the researchers conducted a product quality evaluation ISO/IEC 25010 for 20 respondents where the gathered data and results revealed an overall weighted mean of 4.13 which indicates a strong agreement amongst respondents that ASP-CPR adhere to the quality model. © 2024 IEEE.

关键词： Seed

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Classifying Fake News in Filipino Language using Improved DistilBERT Model 11

Classifying Fake News in Filipino Language using Improved Di...

引用

11th International Conference on Information Technology, Computer and Electrical engineering, ICITACEE 2024

作者： Ruel Espiritu, Jan Mar Policarpio, Ralph John E. Sabado, Aliah Chavy B. Sabado, Vince Marc B. Sueno, Hajah T. College of Engineering Architecture and Computing Notre Dame of Marbel University Koronadal City Philippines

ISBN: (纸本)9798350389289

Detecting misinformation in the Filipino language poses unique challenges, especially when working with limited data resources. This study explored the use of transformer models, specifically pre-trained models like BERT, to construct a fake news classification model for Filipino. However, BERT's large size and computation hinder its deployment in resource-constrained environments. To address this, the study focuses on developing a lightweight fake news classifier using the DistilBERT architecture, fine-tuned for Filipinos. The model's performance was evaluated against BERT based on accuracy, precision, recall, F1 score, inference time, memory requirement, and parameter size. Results indicate that while BERT exhibits slightly better classification performance, DistilBERT offers significant advantages in efficiency with faster inference time and smaller resource requirements. © 2024 IEEE.

关键词： BERT Classification model Data augmentation DistilBERT Fake news Fine-tune Transformer

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Utilising Machine Learning for Precise State-of-Charge Prediction in Li-Ion Batteries 12

Utilising Machine Learning for Precise State-of-Charge Predi...

引用

12th IEEE International Conference on Intelligent Systems, IS 2024

作者： Khatri, Avinash Lota, Jaswinder Nepal, Prasanna Amirhosseini, Mohammad Hossein School of Architecture Computing and Engineering University of East London London United Kingdom Nepal Engineering College Department of Electrical and Electronics Engineering Bhaktapur Nepal

ISBN: (纸本)9798350350982

As the popularity of electric propulsion using batteries rises alongside the demand for renewable energy, effective battery management and monitoring are crucial for sustain ability and efficiency in electric vehicles (EVs). The battery monitoring system (BMS) employs IoT/sensor networks to estimate crucial battery metrics like remaining useful life (RUL), state-of-health (SoH), and state-of-charge (SoC), using data on current status, temperature, and voltage. Machine learning (ML) and artificial intelligence (AI) are increasingly utilized to enhance BMS accuracy, addressing challenges like real-time data processing and the accuracy of estimations. This paper investigates the effectiveness of Linear Regression and Random Forest models in estimating SoC. During the hyperparameter tuning phase, the models were optimized using the Grid Search method, and their performance was evaluated at various temperatures: 25°C, 10°C, 0°C, and -10°C. The findings indicate that the models' effectiveness enhances as the temperature increases. The Random Forest model exhibited superior performance at 25°C, achieving an R2 score of 0.99646 and an RMSE score of 0.000264. This paper not only contributes to advancing Li-ion battery monitoring system, but also empowers professionals in this field to harness machine learning capabilities effectively. © 2024 IEEE.

关键词： State of charge

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Predicting Intention to Recommend Digital Citizen Participation using Machine Learning 13

Predicting Intention to Recommend Digital Citizen Participat...

引用

13th IEEE International Conference on System engineering and Technology, ICSET 2023

作者： Hernandez, Alexander A. Abisado, Mideth B. Maceda, Lany L. Rodriguez, Ramon L. Montefalcon, Myron Darrel L. Padilla, Jay Rhald C. Sangalang, Hersel Danna E. National University College of Computing and Information Technologies Philippines Bicol University College of Science Albay Philippines National University - Dasmarińas School of Engineering Computing and Architecture Philippines

ISBN: (纸本)9798350340891

Digital citizen participation uses innovative technologies to enable, engage, and empower citizens to communicate with the government on different needs, concerns, and challenges. To this date, limited studies explain the intention and recommendation for digital citizen participation, particularly from the Philippines. Thus, this study aims to understand digital citizen participation through a survey that examines factors on the intention to use and recommend digital citizen participation among Filipino citizens. A conceptual model was developed based on trust, technology acceptance, and empowerment theory. The framework relationships were tested using machine learning algorithms. The results show that perceived ease of use, perceived usefulness, trust, and empowerment theory positively predicted the intention to recommend digital citizen participation. Furthermore, this study indicates that Filipinos have positive intention and are willing to recommend digital citizen participation. Finally, this work provides evidence of the explanatory power of the conceptual model through machine learning models on digital citizen participation. This work presents some important research and practical implications in digital citizen participation. © 2023 IEEE.

关键词： Developing countries

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Application of Clustering Algorithms to enhance Personalized Learning through Recommendation Model

Application of Clustering Algorithms to enhance Personalized...

引用

2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Sharif, Mhd Saeed Tamang, Madhav Theeng Mani, Dyuthi Elmedany, Wael Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Bahrain

ISBN: (纸本)9798331533137

Technology and education have recently changed student involvement with learning resources and educational experiences. It is very important to have a platform which can provide personalized learning resources and group students by learning patterns. The research develops recommendation systems and grouping algorithms to help students with their learning patterns and performance measures. To record multidimensional student learning experiences, demographics, learning activities, problem-solving behaviours, and performance measures are extracted and processed. This system makes personalised student suggestions via collaborative filtering, especially Alternating Least Squares (ALS). The model predicts learning materials and problem sets based on student preferences and ability levels by analysing historical student interactions with learning resources. The research also uses clustering methods like K-means clustering to group students with similar learning and performance patterns. Clustering analysis lets instructors discover student group traits and tailor interventions and support techniques. This research also examines temporal relationships in student learning sequences using sequential models. Sequential learning activities and problem-solving behaviour of students help recurrent neural networks (RNNs), or sequential pattern mining algorithms predict their next activities. This research used a huge dataset of over sixteen million exercise logs and applied collaborative filtering (ALS) and K-means clustering to find learning patterns. The ALS-based recommendation system achieved a Mean Squared Error (MSE) of 0.23, showed excellent predictive accuracy. Clustering grouped students by learning behaviours, enabling targeted interventions. This research improves personalised learning using machine learning and data analytics in education. The recommendation system can help instructors tailor training, give individualised support, and boost academic success for various

关键词： Federated learning

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A Comparative Study of Sales Prediction Using Machine Learning Models: Integration of PySpark and Power BI

A Comparative Study of Sales Prediction Using Machine Learni...

引用

2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Sharif, MhD Saeed Tamang, Madhav Theeng Nepal, Anup Elmedany, Wael Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Bahrain

ISBN: (纸本)9798331533137

Retail management requires accurate sales forecasting for strategic planning, inventory control, and revenue maximisation. In this research, we are predicting sales using PySpark-built ML models and the Big Mart dataset. We evaluate and demonstrate the prediction skills of numerous machine learning algorithms, concentrating on XGBoost. Big Mart offers item weight, visibility, type, and outlet data. We use these properties as prediction model features. PySpark, a strong distributed computing platform, manages massive datasets for analysis and model training. In massive trials, we test decision trees (DT), XGBoost, linear regressions (LR), and random forests (RF). Training and testing enhance model accuracy. RMSE and R-squared measure our model quality. Our metrics evaluate the model’s data fit and prediction accuracy. XGBoost performed best with an RMSE of 1081 and an R-squared of 0.59. The XGBoost algorithm accurately predicts Big Mart sales. The model performs well because of its ensemble learning and understanding of intricate dataset links. We also used Power BI to present analytical insights, helping decision-makers design sales-estimated business plans. This study employed several machine learning algorithms, XGBoost gave the best performance. This research provides insights into how organisations might use these technologies to improve resource allocation and inventory management decision-making. ©2024 IEEE.

关键词： Prediction models

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Revolutionizing Loan Approval: Harnessing the Power of K-Nearest Neighbors for Predictive Eligibility

Revolutionizing Loan Approval: Harnessing the Power of K-Nea...

引用

2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Sharif, Mhd Saeed Tamang, Madhav Theeng Hussain, Niyas Jaheer Elmedany, Wael Intelligent Technologies Research Group School of Architecture Computing and Engineering UEL London United Kingdom College of Information Technology University of Bahrain Bahrain

ISBN: (纸本)9798331533137

A bank has become a basic need of our daily lives. People use banks for several tasks, such as depositing or withdrawing money and borrowing loans. The number of loan applications is rising significantly. It is crucial for the bank and financial sectors to determine loan eligibility and approve loans to suitable applicants only. This study attempts to design a system which can easily predict loan eligibility with the application of several machine learning (ML) algorithms. We employed the five different machine learning algorithms to achieve this research’s aim. These algorithms were trained using the pre-processed dataset to create predictive models. F1 score, accuracy, Recall and Precision are used to evaluate the performance of the proposed approaches. The result showed that K-nearest neighbor (KNN) demonstrated outstanding performance, with an accuracy rate of 88.89%. The Random Forest (RF) model had an accuracy of 84.44%, whereas the Support Vector Machine (SVM) and Linear regression (LR) models achieved 82.22% and 80% accuracy, respectively. The Decision Tree (DT) algorithms showed the worst performance among the five algorithms, with an accuracy of 79.88%. The achievement of this research focuses on the possibility of machine learning algorithms as an efficient method for predicting loan eligibility in banks, contributing to the advancement of financial technology by automating and optimizing key banking processes, reducing processing times, and enhancing overall bank efficiency. ©2024 IEEE.

关键词： Nearest neighbor search

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Risk Management for 5G-Enabled Internet of Things by Using Machine Learning: A Survey

Risk Management for 5G-Enabled Internet of Things by Using M...

引用

2024 Arab ICT Conference, AICTC 2024

作者： Alfaw, Aysha Elmedany, Wael Sharif, Mhd Saeed College of Information Technology University of Bahrain Sakhir Bahrain School of Architecture Computing and Engineering Department Computer Science and Digital Technologies UEL London United Kingdom

ISBN: (纸本)9798350342086

Nowadays, there is a noticeable increase in the development and use of the Internet of Things(loT). With this rapid increase, IoT devices may face several challenges when used in the real world through applications. Using 5G with large numbers of Internet-connected devices of IoT puts many devices at risk and requires risk management. The growing of IoT leads to the growth of cyber-attackers and allow them to expose the vulnerabilities. This paper will present the risk management of 5G-enabled IoT technology and the use of machine learning to mitigate the risk and reduce the attacks on these technologies, and finding solutions through previous researches. This research aims to identify and eliminate potential risks in the IoT based 5G by machine learning. The paper also aims to present a solution to the security problems and risks faced when integrating the fifth-generation network and the Internet of Things. With 5G-enabled IoT, the risk management helps organizations use emerging technologies effectively while mitigating potential underlying risks such as security breaches, and data loss. Authentication,encryption, access control, and communication security are essential for making security. Machine learning algorithms have the potential to remove many obstacles to implementing the security of the Internet of Things, paving the door for the use of sophisticated technology like 5G. With new 5G networks, it is expected that the current IoT will be significantly expanded, which will improve cellular operations and the security of IoT, as well as push the future of the Internet to its edges. Machine learning (ML) creates a secure and intelligent system and provides a robust security mechanism and dynamic for 5G networks. Therefore, this time will also present previous solutions with machine learning against the risks to IoT and 5G. This paper will present a set of previous studies related to insurance of risk management for the 5G-enabled IoT, Which aims to find previ

关键词： Adversarial machine learning

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iNUMAlloc: Towards Intelligent Memory Allocation for AI Accelerators with NUMA 21

iNUMAlloc: Towards Intelligent Memory Allocation for AI Acce...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud computing, 16th IEEE International Conference on Social computing and Networking and 13th International Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xu, Yuanchao Qian, Ruyi Wang, Yida Huo, Qirun Capital Normal University College of Information Engineering Beijing China Skl of Computer Architecture Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9798350329223

The amazing success of deep neural network benefits from the rise of big data. As deep learning models are becoming more scale than ever before, their requirements for memory bandwidth are growing at a tremendous pace. Some AI accelerators adopt non-uniform memory access (NUMA) architecture to mitigate this issue and hence complicate device memory allocation. Although extensive studies have been conducted on how to mitigate resource contention and reduce latency, almost all of them target on CPU-oriented NUMA systems but not on AI accelerators where memory allocation precedes task scheduling. The current memory allocator generally adopts an interleaved memory allocation strategy, which is very easy to implement but far from *** tackle this issue, this paper proposes iNUMAlloc, an intelligent memory allocator specialized for AI accelerators with NUMA architecture by combining program behavior and predictable hardware resources altogether. Preliminary evaluation shows that it can help to improve the accuracy and efficiency of memory allocation, thereby achieving stable execution time. © 2023 IEEE.

关键词： Deep neural networks

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EagerReuse: An Efficient Memory Reuse Approach for Complex Computational Graph 29

EagerReuse: An Efficient Memory Reuse Approach for Complex C...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Qian, Ruyi Cao, Bojun Gao, Mengjuan Shi, Qinwen Wang, Yida Xu, Yuanchao Huo, Qirun Qiu, Keni Capital Normal University College of Information Engineering Beijing China Institute of Computing Technology Cas Skl of Computer Architecture Beijing China

ISBN: (纸本)9798350330717

Memory reuse is a promising approach for deep neural network (DNN) to reduce memory consumption because it does not introduce any additional runtime overhead. We observe that existing memory reuse algorithms consider only the effect of an individual data feature (either tensor size or tensor lifetime) on memory reuse and ignore the relative position relationship (RPR) among tensors. As computational graphs grow slightly more complex, the mining of memory reuse becomes insufficient. To address this issue, we propose a new memory reuse algorithm - EagerReuse, which can exploit more memory reuse opportunities by analyzing RPR among tensors and reusing them as quickly as possible. We evaluated the algorithms with inference models in TensorFlow Model Garden, and the results show that the EagerReuse outperforms the state-of-the-art algorithms in three out of seven cases. For more complex computational graphs, EagerReuse can achieve better memory usage with slightly higher but acceptable overhead. © 2023 IEEE.

关键词： computational graph memory optimization memory reuse memory usage

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