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An AI-based Framework for Olive Disease Identification Using SqueezeNet, Artificial Neural Networks, and Support Vector Machines 30

An AI-based Framework for Olive Disease Identification Using...

引用

30th IEEE International Conference on Telecommunications, ICT 2024

作者： Al-Khateeb, Shahed N Yarmouk University Faculty of Information Technology Computer Science Dept Irbid Jordan

ISBN: (纸本)9798350356694

Plant diseases pose a hazard to world food security. According to data from the Jordanian Department of Statistics, the olive crop was Jordan's greatest agricultural crop in terms of planted area and quantity produced in 2022. This study intends to use deep learning and machine learning approaches to identify olive crop illnesses. Image data was acquired from several sources and integrated into a single dataset. The data was split among 70% training and 30% testing. Deep learning SqueezeNet methods were used to extract features. Data training was conducted using both Artificial Neural Networks and Support Vector Machines models. In terms of accuracy, the Artificial Neural Network provided 92.9%, surpassing the Support Vector Machines model score of 85.5%. © 2024 IEEE.

关键词： Crops

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Sentiment Analysis of Libyan Middle Region Using Machine Learning with TF-IDF and N-grams 2nd

Sentiment Analysis of Libyan Middle Region Using Machine Lea...

引用

2nd International Libyan Conference on information and Communication Technologies, ILCICT 2023

作者： Habberrih, Abdullah Abuzaraida, Mustafa Ali Computer Science Department Faculty of Information Technology Misurata University Misurata Libya

ISBN: (纸本)9783031626234

Arabic dialects are commonly used on social media platforms by Arabic speakers to express their opinions and connect with each other. However, due to the lack of standardized rules or grammars, analyzing Arabic dialects with NLP tools can be more challenging than standard Arabic. Moreover, the poems domain within Arabic dialects is considered to be more challenging than other domains due to structural differences between poems and regular expressions. This study investigates the use of TF-IDF and N-grams with Lemmatization techniques to develop machine learning classifiers for sentiment analysis in the domain of poems within the Libyan dialect, specifically the Libyan Middle Region dialect. Three experiments were conducted using ML classifiers, namely, SVM, NB, and LR. The first experiment explored classifiers’ performance using .TF-IDF with Unigrams, whereas the second experiment investigated the use of TF-IDF with Trigrams, and the third experiment examined the impact of combining Unigrams and Trigrams on the classifiers’ performance. The experimental results indicate that utilizing Unigrams with TF-IDF can enhance classifier performance, whereas Trigrams with TF-IDF can have a negative effect on the classifiers’ performance. Notably, SVM achieved the highest accuracy of 69.04% in the first experiment, while LR achieved the highest accuracy of 58.60% in the second experiment, and SVM achieved an accuracy of 68.92% in the third experiment. Furthermore, in terms of precision, LR in the first experiment outperformed the other classifiers across all experiments with 70.49%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sentiment analysis

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Developing An Efficient Vibrotactile Stimuli In computer Game 8

Developing An Efficient Vibrotactile Stimuli In Computer Gam...

引用

8th International Conference on Electrical, Telecommunication and computer Engineering, ELTICOM 2024

作者： Tarigan, Jos Timanta Sikoko, Arivan Satya Selvida, Desilia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331531249

This research investigates the impact of haptic feedback on gaming experience using controllers. Haptic feedback is common in modern console gaming in the type of vibrotactile. Modern gamepads come with motor that can be triggered to simulate vibrotactile. Our objective is to study the use of vibrotactile in game development. In this research, we developed a third person action RPG game and implement several vibrotactile feature in the game. Our objective is to find the best way to implement vibrotactile based on the in-game event. To measure the effectiveness of the implementation, we invited 15 respondents to test the proposed solution. Based on the questionnaire filled in by the respondents, most of the test shows positive feedback. Our respondents agree that the implemented haptic feedback is a good addition to our game. Furthermore, most respondents agree that the vibration feature add a unique experience during the gameplay. © 2024 IEEE.

关键词： Game Development Haptic Feedback Human-computer Interaction Vibrotactile

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Reduction of Microarray Data Dimensions to Enhance Performance of Naïve Bayes in Classification 4

Reduction of Microarray Data Dimensions to Enhance Performan...

引用

4th International Conference of science and information technology in Smart Administration, ICSINTESA 2024

作者： Ramadhani, Putri Tsatsabila Nababan, Erna Budhiarti Efendi, Syahril Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798350376111

According to data Global Burden of Cancer Survey (Globocan) by World Health Organization (WHO) in 2018 there were 18.1 million new cases with a death rate of 9.6 million deaths, where 1 in 8 men and 1 in 11 women died of cancer. The death rate could be prevented with a comprehensive management system that starts with early detection, prevention, and treatment [2]. In the study of microarray data for cancer early detection, the properties of high-dimensional data, small sample sizes, and noise, must be treated seriously because they can affect the classifier's performance. This is critical for dealing with the curse of dimensionality and the inability to identify important information hidden in microarray data. Therefore, we propose PCA method for dimensionality reduction of microarray data to support Naive Bayes algorithm to complish classification tasks. Based on the studies we conducted, we discovered an increases of performance classification where the accuracy achieved is 9 2. 1 1%, precision 9 0. 2 0% and the f 1-score is 9 3. 8 8% for ovarian cancer and prostate cancer had the accuracy is 93.55%, precision and f 1 - score reached 9 2. 8 6%. © 2024 IEEE.

关键词： classification data science machine learning reduction dimension

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High-Dimensional Data Integration Model to Improve the Plants Quality

High-Dimensional Data Integration Model to Improve the Plant...

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2024 International Conference on Control and Automation, Electronics, Robotics, Internet of Things, and Artificial Intelligence, CERIA 2024

作者： Hasibuan, Nelly Astuti Sihombing, Poltak Nababan, Erna Budhiarti Amalia Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331511166

High Dimensional Data is data that has many dimensions or features with the aim of providing more complete and detailed information about an object or phenomenon. Plant images are objects that have data with many dimensions or features. Plant images can be analyzed to determine plant quality. For example, coffee plant images can be analyzed to determine the quality of coffee plants. Determination of coffee plant quality in previous studies only used one object such as leaves or stems and or fruit. In this study, the quality of coffee plants is measured using leaves, stems, fruit, and hypotheses from coffee plant experts. Analysis of coffee plant images can produce information in the form of size, color and health of plants. This information can be found by extracting image features. However, the large number of features is an obstacle in grouping. Clustering techniques are used to group the large amount of data in the dataset. Expert hypotheses will be used to validate the dataset, determine centroid values and determine weight values to determine plant quality. The obstacle in analyzing multidimensional data is the difficulty of visualizing the number of variables because the available methods generally only process low-dimensional data. The purpose of this study is to produce an integration model for analyzing plant quality datasets by optimizing machine learning to group high-dimensional data. © 2024 IEEE.

关键词： Data quality

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Crafting the Chance: Linear Congruential Generator in Randomization System in RPGs

Crafting the Chance: Linear Congruential Generator in Random...

引用

2024 International Conference on information technology Systems and Innovation, ICITSI 2024

作者： Tarigan, Jos Timanta Salim, Avin Chaili Nababan, Anandhini Medianty Wijaya, Brian Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798331511470

In role-playing games (RPGs), randomization plays an important role in designing the loot system. The unpredictability of rewards enhances player engagement and replayability. Thus, it is crucial for developers to build an algorithm to simulate an engaging randomization method. This research proposed the use of Linear Congruential Generator (LCG) based pseudo-random numbers generator in an RPG. To test the performance of the proposed method, we developed our own game titled "Rudantara", a third person roguelike game. The game employed LCG-based random generators to randomized loot drops, statistics, and upgrades. The objective of this research is to measure the performance of the proposed randomization method in our game and analyze its impact on gameplay, fairness and player satisfaction. © 2024 IEEE.

关键词： Game design

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Logistic Regression for Merchant Customer Churn Prediction: A Data-Driven Approach 24

Logistic Regression for Merchant Customer Churn Prediction: ...

引用

7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Nababan, Anandhini Medianty Purnamasari, Fanindia Elveny, Marischa Fransiska, Chatarina S. Zendrato, Niskarto Nasution, Umaya Ramadhani Putri Rahmat, Romi Fadillah Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (纸本)9798400717833

Fintech applications are currently one of the developments that make transactions easier for individuals. Therefore, many fintech companies are trying to attract public interest in using the products they provide. The competition to retain clients is significant. Monitoring the frequency of consumer transactions every month can overcome this problem. This research aims to estimate customer attrition at various merchants registered on fintech platforms using logistic regression techniques. Logistic regression techniques use transaction data to make predictions about target values. The research also uses the sigmoid function to differentiate between churn and no-churn target classes. This study extends the initial model by including regularization techniques (L1 and L2) to improve predictive performance. The findings show that applying regularization significantly improves the precision, recall, F1 score, and overall accuracy of the model. © 2024 Copyright held by the owner/author(s).

关键词： Sales

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Spatial Recognition of Citrus Trees, Seedlings, and Gaps: A Deep Learning and Geometric Approach on Satellite-Based RGB Imagery

Spatial Recognition of Citrus Trees, Seedlings, and Gaps: A ...

引用

2024 International Conference on Horizons of information technology and Engineering, HITE 2024

作者： Hussain, Muhammad Hamza Atif Mehdi, Syed Atif, Kanwal University of Central Punjab Faculty of Information Technology and Computer Science Lahore Pakistan

ISBN: (纸本)9798331516055

Global food deprivation is one of the key concerns in recent years leading to the development of several techniques to increase food production. A decisive factor in increasing the overall food production is the optimum use of agricultural land. When planting orchards, proper planning and monitoring is required. Failure to do so can lead to tree gaps or blank spots in the plantation either due to the planting irregularity, or natural reasons like weather, pests, and diseases. Research work has been done on the monitoring of trees and crops using remotely sensed data, but we find limited work on finding gaps in agricultural plantations such as fruit orchards. Contributing to this, we have proposed a deep-learning-based approach that uses satellite data to detect individual tree gaps in citrus orchards in Pakistan. Very High-resolution (VHR) satellite images were collected using the Google Earth application. A custom-trained convolutional neural network is applied to the images. A specialized algorithm based on geometric information computes the location and count of individual trees, seedlings, and tree gaps in the given satellite image. Our proposed technique can detect trees with an F-score of 0.997, seedlings with an F-score of 0.964, and tree gaps with an F-score of 0.951. © 2024 IEEE.

关键词： Satellite imagery

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Visual Programming Concept for Infineon XMC Series Microcontrollers 47

Visual Programming Concept for Infineon XMC Series Microcont...

引用

47th ICT and Electronics Convention, MIPRO 2024

作者： Vulić, Lukrecia Kaučić, Luka Aleksi, Ivan Matić, Tomislav Computer Science And Information Technology Osijek Faculty Of Electrical Engineering Osijek Croatia

ISBN: (纸本)9798350382495

In today's modern world, efficient programming is a necessity. To speed up code generation nowadays, programming code is generated using different graphical tools. Although efficient, this technology is scarcely used in embedded system development. In this paper, we present a procedure for block-based visual programming with applications in embedded systems development. We present several examples of blocks using the Blockly open-source visual programming tool and test the implementation on selected Infineon XMC-based boards. The presented programming solution shows successful implementation of block-based visual programming in embedded systems development. © 2024 IEEE.

关键词： Embedded systems

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Comparison of CURE Algorithms Before and After Attribute Deletion Using Forward Selection Method 24

Comparison of CURE Algorithms Before and After Attribute Del...

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7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Nasution, Umaya Ramadhani Putri Muchtar, Muhammad Anggia Purnamasari, Fanindia Hayatunnufus, Hayatunnufus Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

ISBN: (纸本)9798400717833

This study compares how well the CURE (Clustering Using Representatives) algorithm works in terms of speed and time efficiency before and after the forward selection method is used to get rid of attributes. Aiming to enhance computational speed without compromising the quality of clustering results, this study applies forward selection to identify and eliminate irrelevant attributes. The experimental results show that the removal of unimportant attributes significantly reduces the computation time of the CURE algorithm while maintaining or even improving the quality of clustering. The test results have shown that the time change of the clustering process reaches a representative point, i.e., before the attribute removal, the resulting time is 0.146 s, while after the attribute removal process, the resulting time is more efficient and reduced to 0.054 s. These findings highlight that forward selection can effectively enhance time efficiency in the clustering process, making it a highly useful method for high-dimensional dataset analysis. © 2024 Copyright held by the owner/author(s).

关键词： CURE

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