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Flower Based Plant Classification System

Flower Based Plant Classification System

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Muralitharan, Rasendram Inuvaisubramainam, Nilani Jayasinghe, Upul Ragel, Roshan G. Rajeevan, Aarumuham Niladhari, Gramma University of Peradeniya Faculty of Engineering Department of Computer Engineering Sri Lanka Office of the Deputy Chief Secretary Engineering Service Northern Province Sri Lanka Divisional Secretariat Uduvil Sri Lanka

ISBN: (纸本)9798350341072

Plants could be classified using plant flower information. Lot of existing flower-based plant classification systems are available. Some classification system uses DSP techniques to classify the plants. Some classification systems use machine learning techniques to classify the plants. Some classification system uses hybrid techniques to classify the plants. Some classification system uses other methods to classify the plants. Although we have lot of flower-based plant classification systems, the accuracy of the existing flower-based plant classification systems is low. We use machine learning techniques to classify the plants. Our system uses length, width, and mean RGB values of the flowers as the features of the machine learning models. We use K Nearest Neighbour, Support vector machine, Random Forest, and CNN algorithms to classify the plants. We collected the data from five different plants in the Jaffna district, in Sri Lanka. The plants are Nithiyakalyani, Nanthiyavattai, Nochi, Thiruvaththi, and Hibiscus. Each class has 50 samples. The length and width are measured using measuring tape. The RGB mean value is measured using digital camera. The local patterns of the plant species could be found in the first layer of the convolutional neural network models. © 2023 ieee.

关键词： Convolutional neural networks

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Frame Elimination for Recordings of Bat Flight Kinematics from a High-Speed Video Array

Frame Elimination for Recordings of Bat Flight Kinematics fr...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Jayathunga, D.P. Ramashini, M. Zaini, Juliana De Silva, Liyanage C. Müller, R. Uva Wellassa University of Sri Lanka Department of Computer Science and Informatics Sri Lanka Universiti Brunei Darussalam Faculty of Integrated Technologies Brunei School of Digital Science Faculty of Integrated Technologies Universiti Brunei Darussalam Brunei Virginia Tech Department of Mechanical Engineering BlacksburgVA24060 United States

ISBN: (纸本)9798350341072

Bat flight is complex because they have the ability to change the shape of their wings while in flight to perform manoeuvres. To accurately capture the subject's complexity, we have set up a synchronized array of 50high-speed cameras. This array is capable of capturing over 50,000 images per second. The video array is designed to cover a large imaging volume that allows bats to perform full-flight manoeuvres. As a result, many video frames do not show the entire bat. Hence, this study uses deep learning techniques to develop an accurate frame elimination process. Until the controlled environment is set up, we used simulated data created from the blender. For the frame elimination purpose, images needed to be classified based on the location of the bat, where it could be fully or partially presented within the frame or maybe not presented. We used different deep learning techniques, including the ordinary CNN, pretrained models, and object detection model - YOLOv5, to achieve this target. Among all the models, we noted that YOLOv5 gave the best accurate unwanted frame elimination results, where the mAP0.5 value is 0.99. © 2023 ieee.

关键词： Object detection

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Predicting Phosphoglycerylation with Transformer Features and Deep Learning

Predicting Phosphoglycerylation with Transformer Features an...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Chandra, Abel Sharma, Alok Dehzangi, Iman Tsunoda, Tatsuhiko Sattar, Abdul Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia Rutgers University Department of Computer Science CamdenNJ United States Center for Computational and Integrative Biology Rutgers University Camden United States Riken Center for Integrative Medical Sciences Laboratory for Medical Science Mathematics Yokohama Japan School of Science The University of Tokyo Laboratory for Medical Science Mathematics Department of Biological Sciences Tokyo Japan Graduate School of Frontier Sciences The University of Tokyo Laboratory for Medical Science Mathematics Department of Computational Biology and Medical Sciences Tokyo Japan

ISBN: (纸本)9798350341072

Understanding protein sequences can advance treatments for various diseases. However experimentally obtaining this information is laborious, time-consuming, and expensive. Traditional machine learning techniques, like support vector machine, random forest and logistic regression, offer potential to fast-track this process but are sometimes limited by data complexity. Deep learning algorithms, in contrast, tend to yield higher performance. In this study, we employed a convolutional neural network to predict protein phosphoglycerylation. Features were extracted from pre-trained transformer models and compared with conventional features, such as evolutionary information and physicochemical/biochemical properties. Our results indicate significant performance improvements across all feature types, with the combination of transformerbased features and the convolutional neural network being especially effective. This methodology holds potential for other protein property prediction tasks. Our software and datasets used in this study are publicly available at https://***/abelavit/***. © 2023 ieee.

关键词： Convolutional neural networks

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A Modified Feature Selection Algorithm for Intrusion Detection System Based on Student Psychology-Based Optimization with Explainable AI

A Modified Feature Selection Algorithm for Intrusion Detecti...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Evan, Md. Raisul Islam Motiur Rahman, Mohammad Tanzir Mehedi, Sk. Nazib, Abdullah Islam, Rafiqul Rahman, Ziaur Mawlana Bhashani Science and Technology University Computer Science and Engineering Tangail1902 Bangladesh Mawlana Bhashani Science and Technology University Information and Communication Technology Tangail1902 Bangladesh School of Electrical Engineering and Robotics Queensland University of Technology BrisbaneQLD Australia School of Computing Mathematics and Engineering Charles Sturt University Bathurst Australia School of Computer Science Queensland University of Technology BrisbaneQLD Australia

ISBN: (纸本)9798350341072

Intrusion Detection Systems (IDS) play a vital role in safeguarding network security by monitoring and identifying both normal and intrusive activities. However, constructing an effective IDS from extensive datasets poses a significant challenge. The process of feature selection from these datasets is crucial, as it not only enhances accuracy but also reduces the time required to build the model. In this paper, we employ a modified version of the Student Psychology-Based Optimization (SPBO) algorithm to identify the most relevant features set for IDS. Following feature selection, various machine learning algorithms are employed for multi-level classification. To evaluate our approach, we utilize the NLS-KDD dataset. Our proposed algorithm surpasses several state-of-the-art feature selection methods in terms of accuracy, precision, recall, Fl-score, and finally faster convergence. Specif-ically, our proposed bio-inspired SPBO algorithm combined with the Random Forest (RF) algorithm achieves a comparable accuracy rate of 99.81 %. To ensure better comprehensibility, we employ two widely used Explainable Artificial Intelligence (XAI) techniques: SHapley Additive exPlanations (SHAP) and Local In-terpretable Model-Agnostic Explanations (LIME). These methods provide insightful explanations for our model's decisions, further enhancing its interpretability. © 2023 ieee.

关键词： Feature Selection

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PRODG: Practical Overlapping Community Detection in Weighted Graphs using Deep GCNs

PRODG: Practical Overlapping Community Detection in Weighted...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Muttakin, Md Nurul Azim, Muhammad Anwarul Hossain, Md Iqbal Rahman, Md. Saidur Bangladesh University of Engineering and Technology Graph Drawing & Information Visualization Lab Department of Computer Science and Engineering Dhaka1205 Bangladesh The University of Arizona TucsonAZ United States

ISBN: (纸本)9798350341072

Graph-based neural networks, including transformers, are the most used neural networks. Recently, overlapping community detection problems have been tackled using graph convolutional networks (GCN), achieving superior performance than classical methods. However, some of these methods have limited practical use as they require ground truth information to optimize the hyper-parameters. In addition, most existing works, including GCN-based methods, either ignore the edge weight of weighted graphs or fail to leverage the edge weight information to achieve better performance. To this end, we propose PRODG as a practical overlapping community detection method in edge-weighted graphs using deep GCNs. Specifically, we design weighted dilated aggregation algorithms to incorporate the information of edge weights to achieve a deep-weighted residual GCN (WResGCN) model. Moreover, we perform a completely unsupervised modularity-based depth search for deep GCNs, making our method available for practical applications. We apply our model to real-world Facebook graphs with synthetic edge weights, having reliable ground truth information. Experimentation and evaluation show that our methods effectively leverage the edge-weight in weighted graphs. Our completely unsupervised depth search strategy in deep GCNs performs competitively or better than state-of-the-art methods, bolstering the practical use of deep GCNs in overlapping community detection. © 2023 ieee.

关键词： Graph neural networks

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Performance Analysis of Machine Learning Algorithms for Classification of Infection Severity Levels on Rubber Leaves

Performance Analysis of Machine Learning Algorithms for Clas...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Lazim, Siti Saripa Rabiah Mat Sulaiman, Zulkefly Nawi, Nazmi Mat Mustafah, Anas Mohd Institute of Plantation Studies Universiti Putra Malaysia Malaysia Unversiti Putra Malaysia Faculty of Agriculture Department of Plant Science Serdang43400 Malaysia Universiti Putra Malaysia Department of Biological and Agricultural Engineering Selangor Malaysia

ISBN: (纸本)9798350341072

White root disease (WRD) infection in rubber plantations which is caused by Rigidiporus micropores can lead to a significant yield loss. At the early infection stage, it is very difficult to diagnose the disease because infected trees do not exhibit any symptoms. Thus, this study was carried out to investigate the potential application of spectroscopic technology and machine learning algorithms to classify severity level of infected trees at early stage based on spectral data. A total of 50 leaf samples were used in this work, representing five severity levels;healthy, light, moderate, severe and very severe infection. A visible shortwave near-infrared (VSNIR) spectrometer was used to record the spectral data of the leaf samples. The chlorophyll content of each leaf was measured using SPAD meter. Four classification algorithms investigated in this study were artificial neural network (ANN), support vector machine (SVM), knearest neighbour (kNN) and random forest (RF). The result of the study demonstrates good classification accuracy of 90, 82, 78, and 72% for ANN, SVM, kNN and RF, respectively. This work shows that the spectroscopic measurement combined with classification techniques are promising strategy to classify severity level of WRD based on the spectral data of the rubber leaves. © 2023 ieee.

关键词： Rubber

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data and Information Security Issues and Challenges for Banking Sectors in Fiji

Data and Information Security Issues and Challenges for Bank...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Lal, Goel Aman Singh, Manpreet Bir, Jashvir School of Information Technology Engineering Mathematics & Physics The University of the South Pacific Suva Fiji Fiji National University National Training & ProductivityCentre Suva Fiji

ISBN: (纸本)9798350341072

The Internet has played a key role in changing how we interact with other people and how we do business today. As a result of the Internet, electronic commerce has emerged, allowing businesses to more effectively interact with their customers and other corporations inside and outside their industries. The internet can be seen as a truly global phenomenon that has made time and distance irrelevant to many transactions. One industry that is using this new communication channel to reach its customers is the banking industry. The transformation from traditional banking to e-banking has been a leap change. The electronic banking system addresses several emerging trends such as 'customers' demand for anytime, anywhere service, product time-to-market imperatives and increasingly complex back-office integration challenges. However, the increase in the use of ICT facilities results in the increase of criminal activities like spamming, credit card frauds, ATM frauds, phishing, identity theft, denial of service and other security bridges have lent credence to the view that ICT is contributing to crime in the banking sector. The challenges that oppose electronic banking are the concerns of security and privacy of information. This study discusses security issues associated with internet connectivity, focusing on three main areas: confidentiality, integrity, and availability of information. Vulnerabilities that companies are exposed to, such as a virus, malware, phishing, Trojan horse, exposes a' company's information assets to attacks. It also discusses preventive measures that organizations should implement in order to protect a company from such threats. Finally, recommendations are made for the decision-makers to consider when making decisions regarding data and information security on financial organizations. © 2023 ieee.

关键词： Decision making

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Plant Leaves Classification System

Plant Leaves Classification System

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Muralitharan, Rasendram Inuvaisubramaniam, Nilani Jayasinghe, Upul Ragel, Roshan G Rajeevan, Aarumuham Niladhari, Gramma University of Peradeniya Faculty of Engineering Department of Computer Engineering Sri Lanka Office of the Deputy Chief Secretary-Engineering Northern Province Sri Lanka Divisional Secretariat Uduvil Sri Lanka

ISBN: (纸本)9798350341072

Plant leaves could be classified using machine learning techniques. The plant leaves could be used to identify the different types of the plants. Lot of plant leaves classification systems are developed using machine learning techniques. Support Vector Machine, Random Forest, K Nearest Neighbour, and Convolutional Neural Network algorithms could be used to develop the plant leaves classification systems. Colour, Shape, Texture, and Vein details could be used as the features to classify the plant leaves. Though we have lot of plant leaves classification systems, the accuracy of the classification systems is low. We use the shape, and the colour of the plant leaves as features to classify the plant leaves. We have collected the length, width, and colour data from five different plants, which are Jack fruit, Lemon, Nochi, Guava, and Cinnamon. Nearly fifty samples are collected from each type of the plant. Our classification system uses machine learning algorithms to identify the plants. The system uses K Nearest Neighbour, Support Vector Machine, and Random Forest algorithms. The system could be used to classify plant leaves in the agricultural industry, and Ayurvedic medicine industry. The accuracy, precision, recall, and F1 score of the models are obtained. We have achieved the highest prediction accuracy of 99% in the Random Forest algorithm. The local pattern of the plant species could be found in the first layer of the convolutional neural network model. © 2023 ieee.

关键词： Color

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A Novel Smartphone Recommendation System Using Ensemble Machine Learning

A Novel Smartphone Recommendation System Using Ensemble Mach...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Almadhor, Ahmad Abbas, Sidra Sampedro, Gabriel Avelino Abisado, Mideth Gadekallu, Thippa Reddy College of Computer and Information Sciences Jouf University Sakaka72388 Saudi Arabia Comsats University Islamabad Department of Computer Science Islamabad Pakistan University of the Philippines Open University Faculty of Information and Communication Studies Los Baños4031 Philippines De la Salle University Center for Computational Imaging and Visual Innovations 2401 Taft Ave. Manila1004 Philippines College of Computing and Information Technologies National University Manila Philippines Zhongda Group Jiaxing City Zhejiang Province Haiyan County314312 China Lebanese American University Department of Electrical and Computer Engineering Byblos Lebanon School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu India College of Information Science and Engineering Jiaxing University Jiaxing314001 China Lovely Professional University Division of Research and Development Phagwara India

ISBN: (纸本)9798350341072

Due to the proliferation of internet evaluations brought on by the rising demand for smartphones, consumers find it challenging to make accurate selections when purchasing. In this paper, we offer ensemble voting methods based on TF-IDF (Term Frequency-Inverse Document Frequency) features for clas-sifying mobile phone ratings. We use a recently assembled dataset comprising over 13,000 smartphone reviews from the Flipkart website. The suggested approach includes feature extraction using the TF-IDF, data cleaning, balancing, and voting-based model prediction. To identify the recently created Flipkart dataset, the suggested method created an ensemble voting mechanism based on machine learning techniques. According to the experimental findings, the suggested method performs more accurately and efficiently than conventional machine learning techniques. At 98.0 %, the model achieved the greatest accuracy. The suggested method can be expanded to additional e-commerce platforms with sizable datasets of online evaluations and assist customers in making updated purchase preferences. © 2023 ieee.

关键词： Machine learning

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Time Series Forecasting of U.S. Housing Price Index Using Machine and Deep Learning Techniques

Time Series Forecasting of U.S. Housing Price Index Using Ma...

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2023 ieee asia-pacific conference on computer science and data engineering, csde 2023

作者： Nunna, Krishna Chaitanya Zhou, Zhaoxian Shakya, Sarbagya Ratna School of Computing Sciences and Computer Engineering University of Southern Mississippi Hattiesburg United States Eastern New Mexico University Department of Mathematical Sciences/Electronics Engineering Portales United States

ISBN: (纸本)9798350341072

The Housing price index (HPI) plays a key role in real estate dynamics, which majorly contributes to the United Sstates economy. Forecasting accurate housing price indices will be very beneficial for investors, policymakers, banks, and homeowners. There was a wide range of approaches implemented for forecasting HPI using microeconomic features with very little concentration on macroeconomic features such as economic indicators at a national level. HPI directly correlates with these macroeconomic features, which have a direct influence on overall real estate dynamics and the economy. In this paper, we applied various time series, machine learning [ML], and deep learning [DL] models to forecast HPI using macroeconomic features. Our goal is to get accurate predictions and get the relationships that directly impact the housing market. Macroeconomic features of the dataset were collected from economic and federal agencies with the aim of forecasting HPI values. We implemented the autoregressive model integrated moving average model [ARIMA] and Facebook Prophet [FB Prophet] to know the temporal dependencies. Next, we applied machine learning and deep learning models Random Forest, Extreme Gradient Boosting (XGBoost), Long Shorth term model (LSTM), Recurrent neural networks (RNN), and a hybrid approach, which is an ensemble of Extreme Gradient Boosting and LSTM. We studied the efficacy of implemented models and evaluated the performance of the models using the performance metrics Mean Absolute Error (MAE), and Root Mean square Error (RMSE), and R-squared. © 2023 ieee.

关键词： Forecasting

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