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Intelligent Agriculture Robot for Tea Plantation Preservation - TeaBot 25

Intelligent Agriculture Robot for Tea Plantation Preservatio...

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25th International Multi Topic Conference, INMIC 2023

作者： Gunawardana, Imalka Perera, Varnavi Premathilake, Thulya Bamunusinghe, Gayan Lokuliyana, Shashika Gamage, Narmada Silva, Rajitha De Sliit Dept. of Software Engineering Malabe Sri Lanka Sliit Dept. of It Malabe Sri Lanka Sliit Dept. of Computer Systems Engineering Malabe Sri Lanka University of Lincoln Agri-Robotics Lincoln United Kingdom

ISBN: (纸本)9798350317701

Tea cultivation stands out as one of the most significant export products that contribute to the Gross Domestic Product (GDP). In the previous decades, the substantial work-force gradually transitioned to different occupations. Compared to other crops, tea cultivation demands costly and meticulous upkeep. With a shortage of labor, maintaining tea estates grew tougher, leading to decreased yields. In reaction, estate owners transitioned to cultivating low-maintenance crops. The TeaBot is an advanced robot designed to replace human labor for watering and fertilizing vast tea estates. The TeaBot distinguishes itself by operating in rough outdoor terrain and infrastructure-free navigation in real tea plantations. Given that tea plants demand continuous hydration and nutrients for optimal crop yield, TeaBot plays a pivotal role in enhancing efficiency while reducing water and fertilizer wastage. Mainly four motor-powered wheels move accurately by translating linear and angular velocities using a precise motor control algorithm. An autonomous navigation algorithm was developed using two distinct approaches, which include deep learning-based computer vision and classical computer vision. The selection of the classical computer vision method was predicated upon its notable attributes, including high precision, minimal resource utilization, and optimal efficiency. A deep learning-based stem identification model was trained based on MobileNetV2 architecture to detect where the plant stem meets the ground for efficient hydration of individual plants. This lightweight model achieved 90% detection accuracy. The precise results of stem detection have made the liquid fertilization process more efficient. © 2023 IEEE.

关键词： computer vision

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AYAT: A Lightweight and Efficient Code Clone Detection Technique 3

AYAT: A Lightweight and Efficient Code Clone Detection Techn...

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3rd Asia Conference on computers and Communications, ACCC 2022

作者： Giani, Yasir Ping, Luo Shah, Syed Asad Software School Tsinghua University Beijing China Bilkent University Dept.of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350332957

Code clones make software maintenance more challenging. Detecting bugs in large systems may significantly increase maintenance costs. Despite the fact that several techniques for clone identification have been proposed over the years, the accuracy and scalability of clone detection techniques remain hot research areas. Previously, Akram et al. proposed the DroidCC hybrid technique, where tokens were encoded into MD5 hash values by encoding them into 128-bit fingerprints, and clones were identified by matching identical hash values. Encoding tokens into MD5 hash values take more time due to the large fingerprint size of MD5 hash values. Due to the enormous chunk size, DroidCC cannot achieve higher accuracy. To overcome the weakness of the DroidCC technique, We proposed a novel AYAT a lightweight hybrid technique to detect clones at the fragment level. To speed up the detection process, we converted tokens into 32-bit polynomial values, and we set the chunk size to 5 lines per chunk to improve accuracy. We tested our technique on 10,968 java projects against 4.98 million lines of code. In comparison to the well-known DroidCC technique, it is significantly faster and more efficient. Our examination demonstrates that precision is significantly improved despite sacrificing scalability. AYAT code cloning detection technique has outscored DroidCC in every aspect. © 2022 IEEE.

关键词： computer software maintenance

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Decision Support System: Analysis and Design Methodology 2

Decision Support System: Analysis and Design Methodology

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2nd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2022

作者： Darbi, Huweida A. Saleh, Eiman M. University of Derna Dept. Computer Science Derna Libya Benghazi University Dept. Software Engineering Benghazi Libya

ISBN: (数字)9781665479189

ISBN: (纸本)9781665479189

The success of any organization depends on the quality of the decisions made, and good decisions can be made by analyzing all the available data and knowledge of the organization. Decision Support Systems (DSSs) are used to analyze this data and aid in decision-making in the formulation of well-studied decisions based on knowledge. However, the analysis and design of decision support systems (DSS) is a complex process that involves the use of appropriate methodologies, methods, and tools to model decision-making processes. This paper presents an architectural design of DSS framework. The proposed approach helps developers develop decision-making systems through models that detail the analysis and design. © 2022 IEEE.

关键词： Decision support systems

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Flexibility and Privacy: A Multi-Head Federated Continual Learning Framework for Dynamic Edge Environments 11

Flexibility and Privacy: A Multi-Head Federated Continual Le...

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11th International Symposium on Computing and Networking, CANDAR 2023

作者： Chen, Chunlu Wang, Kevin I-Kai Li, Peng Sakurai, Kouichi Kyushu University Graduate School of Information Science and Electrical Engineering Fukuoka Japan The University of Auckland Dept. of Electrical Computer and Software Engineering Auckland New Zealand The University of Aizu School of Computer Science and Engineering Aizuwakamatsu Japan Kyushu University Faculty of Information Science and Electrical Engineering Fukuoka Japan

ISBN: (纸本)9798350306705

Federated Learning (FL) and the Internet of Things (IoT) have revolutionized data processing and analysis, overcoming the traditional limitations of cloud computing. However, traditional machine learning strategies lead to increased costs and catastrophic forgetting due to model retraining with new datasets. Continual learning has been proposed to counter this, enabling models to adapt to new data while preserving previous knowledge, which is beneficial for dynamic edge environments. Despite these advantages, the retention of previous knowledge during the continual learning process may lead to the information leakage. To address the inherent challenges of multitask scenarios, we present a Federated Continual Learning (FCL) framework that integrates the privacy-preserving benefits of Federated Learning (FL) into a continual learning system, ensuring both continual learning and privacy preservation in edge computing data processing and analysis. Specifically, our architecture introduces dedicated fully-connected layers for each task. This architecture ensures that distinctive features pertinent to each task are not only captured but also preserved throughout the model's lifespan. Within our framework, data is processed via task-specific layers. Subsequently, the final label is determined by associating it with the paramount prediction value, thus capitalizing on the model's comprehensive knowledge reservoir to bolster prediction accuracy. We subjected our FCL framework to rigorous validation using two benchmark datasets: MNIST and CFAR-10. Experimental outcomes unequivocally substantiate the efficacy of our proposed methodology. © 2023 IEEE.

关键词： Learning systems

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Aerial Vehicle Detection and Classification Through Fusion of Multi-Domain Features of Acoustic Signals

Aerial Vehicle Detection and Classification Through Fusion o...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Sumble, Malaika Aziz, Sumair Khan, Muhammad Umar Iqtidar, Khushbakht Fernandez-Rojas, Raul University of Engg. and Tech. Taxila Department of Electronics Engineering Taxila Pakistan University of Canberra Faculty of Science and Technology CanberraACT Australia National University of Sciences & Tech. Dept. of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350349719

Detection of aerial vehicles is a challenging task. Over time, the significance of developing such vehicles has increased rapidly. However, an utmost concern arises from the potential misuse of these small-sized vehicles in illicit activities such as unauthorized surveillance, smuggling contraband, or disrupting critical infrastructure significant security threats. Recognizing the gravity of this issue, we have opted to introduce an innovative approach to automate the detection processes for aerial vehicles. This initiative aims to deploy effective drone detection systems to mitigate risks and safeguard against illegal activities. We put use to two datasets from GitHub depot. Cepstral, spectral, and time domain features were extracted from the data, followed by classification. We conducted two experiments, with the first (Drone, No-Drone) yielding 98.6% accuracy, using the Ensemble (Bagged Trees) classifier. The second set of experimentation addressed five classes: Background Noises, Bebop Drone, Drone, Helicopter, and Mambo Drone. Ensemble (Bagged trees) again outperformed all other classifiers and achieved 98.3% accuracy. The results highlight that our proposed framework gives effective results based on audio signals of different aerial vehicles. © 2024 IEEE.

关键词： Drones

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Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction 4

Reinforcement Learning for Language Grounding: Mapping Words...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Adapa, Srinivasa Rao Gupta, Himanshu Mukherjee, Susmoy Mallupalli, Praneeth Reddy Avhad, Priyanka Upreti, Kamal OpenLogix LLC Software Engineer MI United States Software Engineer Meta NJ United States VIT Institute Electrical and Electronics Engg. Tamil Nadu India Arcesium Hyderabad India Veermata Jijabai Technological Inst. Computer Engineering Mumbai India Christ University Dept. of Computer Science Delhi NCR Campus India

ISBN: (纸本)9798350353266

Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on instruction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute instructions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction. © 2024 IEEE.

关键词： Reinforcement learning

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RNN-based Model for an Optimal COVID-19 Cases Detection using Clinical Reports 9

RNN-based Model for an Optimal COVID-19 Cases Detection usin...

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9th International Conference on Optimization and Applications, ICOA 2023

作者： Khanday, Akib Bouktif, Salah Ouni, Ali Uae University Cit Dept. Computer Science and Software Engineering Al Ain United Arab Emirates University of Quebec Ets Montreal MontrealQC Canada

ISBN: (纸本)9798350312546

The global impact of COVID-19 has been significant due to its rapid spread and high transmission rate. A large number of individuals have been exposed to this dangerous virus, emphasizing the importance of early detection to potentially save numerous lives. This research paper introduces an enhanced method for COVID-19 detection, utilizing a Recurrent Neural Network (RNN) and leveraging early clinical reports. Specifically, a Long Short Term Memory (LSTM) model is trained using data obtained from the metadata of the available dataset published by the World Health organisation, specifically focusing on extracting clinical reports and labels. Several pre-processing techniques and word embedding are applied to train an LSTM-based classifier that accurately identifies positive COVID-19 cases. The implementation of this proposed approach yielded superior results compared to traditional machine learning algorithms. The model achieved a testing accuracy of 87%, and future works include expanding the dataset to enhance the efficiency of the model. © 2023 IEEE.

关键词： COVID-19

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Investigating Principle Component Analysis Impact on the Performance of Machine Learning Classifiers: A Health Informatics Application 25

Investigating Principle Component Analysis Impact on the Per...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Banimustafa, Ahmed Almatarneh, Sattam Bulkrock, Olla Alazaidah, Raed Almahasneh, Hossam Samara, Ghassan Higher Colleges of Technology Computer Information Science Dept. Abu Dhabi United Arab Emirates Zarqa University Data Science & Artificial Intelligence Dept. Zarqa Jordan University of Business and Technology College of Engineering Jeddah Saudi Arabia Princess Sumaya University for Technology Software Engineering Dept. Amman Jordan Zarqa University Computer Science Dept. Zarqa Jordan

ISBN: (纸本)9798331540012

Principal Components Analysis (PCA) is a popular dimensionality reduction and feature selection technique. It works by swiping non-informative features while retaining those that are still potentially useful. However, regardless of its wide applications in statistical analysis, the influence of PCA on the performance of classification algorithms has been rarely studied, particularly in the domain of medical image classification. In this paper, we present a thorough analysis of the influence of applying PCA on the classification of medical images. The dataset used in this study consists of thousands of images that represent four types of blood cells. These images are used to train three machine-learning classifier models after transforming images into numerical vectors using a CNN pre-trained embedding algorithm, where each image is encoded using one thousand numerical vectors. The study involves building three classification models using (1) K-nearest Neighbor (KNN), (2) Logistic Regression (LR), and (3) Random Forests (RF). Each model was built twice, once after the application of PCA to reduce the dimensionality of the embedded features and once with the original embedded features. The performance of each model is evaluated using Classification accuracy, F1, AUC, Recall, precision, and specificity metrics. The recorded performance is then compared to drawing a decisive conclusion. © 2024 IEEE.

关键词： Dimensionality reduction

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Automating the Diagnosis of Cucumber Plant Diseases Using Machine Learning 25

Automating the Diagnosis of Cucumber Plant Diseases Using Ma...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Banimustafa, Ahmed Qattouz, Hazem Almatarneh, Sattam Mansour, Khalid Alazaidah, Raed Bulkrock, Olla Higher Colleges of Technology Computer Information Science Dept Abu Dhabi United Arab Emirates Princess Sumaya University for Technology Software Engineering Dept Amman Jordan Zarqa University Data Science & Artificial Intelligence Dept Zarqa Jordan University of Business and Technology College of Engineering Jeddah Saudi Arabia Arab-Open University Bahrain Faculty of Computer Studies Bahrain

ISBN: (纸本)9798331540012

Farmers all over the world struggle to fight plant diseases. However, the incorrect use of pesticides can waste time, effort, and money and can also be very harmful to the environment and human health. In this work, we present an empirical study that involves identifying two pathogens related to Cucumbers plant diseases: Powdery Mildew and Alternaria Cucumerina. The study aims to develop a computer-based solution based on deep learning and machine learning algorithms to detect these diseases. To achieve this, a dataset of hundreds of samples was collected from ordinary farms in Beqaa, an agricultural region 15 KM north of Amman, Jordan. Three machine learning models were created in this study using Support Vector Machines (SVM), Logistic Regression (LR), and Multi-layer perceptron algorithms. The constructed models were applied to the dataset after embedding the dataset images using the Convolutional Neural Networks (CNN) InceptionV3 pre-trained model. The model was tested and evaluated using cross-evaluation and F1-Score, CA, AUC, Precision, Recall, and Specificity, which all showed excellent results, particularly using the SVM algorithm. The constructed methods are intended to be deployed to farms in their field condition environments using a CCTV system that can capture images of plants during growth, which can help identify plant diseases at that right stage, which would save time, effort, and money and reduce the impact of wrong and overuse of pesticides on consumers and the environment. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploring Relationships among Bitcoin's Market Price, Energy Consumption and Carbon Dioxide Emissions: A Machine Learning Approach 1

Exploring Relationships among Bitcoin's Market Price, Energy...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Jamali, Aadil Ali, Najma Imtiaz Brohi, Imtiaz Ali Kanasro, Nadeem Ahmed Murad, Muhammad Umar Jamali, Asad Ali University of Sindh IMCS Jamshoro Pakistan Government College University Dept. of Computer Science Hyderabad Pakistan Istanbul Aydin University Dept. of Software Engineering Istanbul Turkey University of Essex Dept. of Economics Colchester United Kingdom

ISBN: (纸本)9798350373042

The cryptocurrency boom has resulted in a substantial rise in energy usage and carbon emissions. The mining process of Bitcoin is notorious for its energy intensive process because it requires huge computational power. The carbon footprint of Bitcoin mining is a growing concern that's why we have to analyze the number of CO2 emissions resulting from the mining procedure. In this study, we have utilized machine learning techniques to examine the number of CO2 emissions generated by Bitcoin mining, analyzed the correlations between Bitcoin price, electricity consumption, and CO2 emissions, and pinpoint the factors during the process of Bitcoin mining that leads to CO2 emissions. We gathered global data set on Bitcoin electricity consumption, prices, and CO2 emissions. We trained supervised machine learning model (Regression) in Python to analyze and investigate the data. Our research findings have great implications for policymakers and researchers worried about the impact of the latest technologies on the environment, and in future Bitcoin policies and practices;they should prioritize sustainability and reduce the carbon footprint of mining operations. In the end, our research will provide valuable insights into the environmental impact of Bitcoin mining, highlight the factors that cause CO2 emissions and underscored the need for further research and action to reduce the carbon footprint of cryptocurrencies. © 2024 IEEE.

关键词： Carbon footprint

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