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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Sumesh, Nandana Navaneeth, R. Raj, Vimal Rajesh, Vismaya Ani, R. Department of Computer Science Amrita School of Computing Amritapuri India

ISBN: (纸本)9798331515720

In recent years, the agricultural sector has faced challenges in achieving optimal crop yields due to complex environmental factors and limited access to data-driven decision-making tools. An innovative IoT-based approach to predict crop yields is presented, aiming to improve crop productivity for farmers. The system utilizes a network of IoT sensors, including DHT22 and rainfall sensors, to gather real-Time data on temperature, humidity, and rainfall. Soil-integrated sensors are also used to monitor specific soil properties such as nitrogen, phosphorus, potassium, pH, electrical conductivity, humidity, and temperature. This data is sent to a web interface for live display and stored for predictive modeling. Several regression models-decision tree, random forest, ridge regression, and linear regression-were implemented to predict crop yields, with the random forest regression model achieving the highest accuracy and an error rate of 6.46 percent. The stacking model was also analyzed, but the random forest model's MAE and R-metrics proved superior, leading to its selection for deployment. A user-friendly web-based interface has been developed to enable farmers to interact with the system and predict crop yields effectively. This implementation smoothly integrates IoT technology with advanced data analytics, providing valuable insights to optimize agricultural practices, which in turn enhances food security and improves livelihoods. © 2024 IEEE.

关键词： Smart agriculture

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An Analysis of Hate Speech Detection Techniques 4

An Analysis of Hate Speech Detection Techniques

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4th International Conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Krisdianto, Ricky Apriani, Ivana Halim, Peter Miracle Anggreainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Departement Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Doctor of Computer Science Computer Science Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350318432

Hate speech is any act of provoking or insulting another person or group based on their ethnicity, religion, race, gender, sexual orientation, physical ability, or other characteristics. This can be done in a variety of ways. Because of this, a study was conducted that focused on ascertaining the most effective technique for identifying hate speech. It was discovered that LSTM, a form of recurrent neural network (RNN) that is particularly good at modeling sequential data, is the most successful technique to apply. Many natural language processing (NLP) activities may be performed with it, including hate speech detection, which makes LSTM have advantages over other techniques, both in terms of long-term memory mechanisms. LSTM, also able to handle long and complex text sequences, capable of recognizing more complex patterns, able to understand and consider the context in the text, to recognize the meaning of a word or phrase in a broader context. Then, based on the dataset obtained from the Kaggle website with a total of 24783 data, then the dataset is divided into training data and validation data, with a ratio of training data to validation data. of 80%: 20%. Then, the testing step results in an accuracy value of 87.10%. © 2023 IEEE.

关键词： Speech recognition

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Hybrid convolutional neural network approach for optimizing automatic identification of natural isotopes in gamma ray environmental sample spectra

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Neural Computing and Applications 2024年第31期36卷 19585-19595页

作者： Paleti, Bharathi Sastry, G Hanumat School of Computer Science UPES Dehradun India Department of CSE Anil Neerukonda Institute of Technology and Sciences Visakhapatnam India

Radioisotope identification presents challenges that can be effectively addressed through pattern recognition and machine learning (ML) techniques. However, further investigation is necessary to assess the accuracy of these algorithms in quantifying mixtures of radioisotopes. The novelty of the study focuses on a hybrid convolutional neural network (CNN) architecture, called Arch, which utilizes numerical values to predict the presence of radioisotopes based on their signals. The feature extraction methods are employed to analyze small-isotope libraries using area-of-interest techniques and low-resolution spectrometers, with fully calibrated detectors ensuring accurate identification complexity. Additionally, this study explores the use of two sets of machine learning approaches for the automated identification of radioisotopes, focusing specifically on the feature extraction method. The Hybrid CNN Arc model, as proposed, achieved a test data accuracy of 95%. Additionally, a recurrent neural network model achieved an accuracy of 92%, while a GBDT model achieved an accuracy of 86%. The precision, recall, and f1-score metrics have been computed for the Hybrid CNN Arch approach, yielding values of 95%, 95%, and 95%, respectively. Similarly, the RNN model achieved precision, recall, and f1-score scores of 89%, 82%, and 81.5%, respectively. Lastly, the GBDT model attained precision, recall, and f1-score values of 84%, 81%, and 74.6%, respectively. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Feature extraction

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Comparing Age Estimation with CNN and EfficientNetV2B1 8

Comparing Age Estimation with CNN and EfficientNetV2B1

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Christopher, Marco Vito Wahid, Abdul Nabiilah, Ghinaa Zain Rojali Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Age estimation from facial images is a challenging topic in computer vision since it can automatically label the human face with an exact age according to various physical or biological characteristics, such as facial structure, spots, and wrinkles. Additionally, it has substantial applications in many fields, including healthcare, security, entertainment, and education. There are a lot of techniques to estimate age, but the most popular one is the convolutional neural network (CNN), which offers high accuracy but needs a lot of training time and much more labeled data to achieve it. Another popular technique is transfer learning with feature extraction, which can provide higher accuracy with faster training time because it uses a pre-trained model rather than building one from scratch but has limited flexibility and a substantial risk of overfitting. Due to the advantages and disadvantages of both methods, this study analyzes and compares the effectiveness of transfer learning and CNN methods for age estimation from facial images. Using R-squared and RMSE as test metrics, the results show that the CNN method offers a better process and accuracy than the transfer learning method, with a higher R-squared value and a lower RMSE value. © 2023 The Authors. Published by Elsevier B.V.

关键词： age estimation convolutional neural network facial images transfer learning

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A Pattern Classification Model for Vowel data Using Fuzzy Nearest Neighbor

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Intelligent Automation & Soft Computing 2023年第3期35卷 3587-3598页

作者： Monika Khandelwal Ranjeet Kumar Rout Saiyed Umer Kshira Sagar Sahoo NZ Jhanjhi Mohammad Shorfuzzaman Mehedi Masud Department of Computer Science and Engineering National Institute of Technology SrinagarHazratbal190006Jammu and KashmirIndia Department of Computer Science and Engineering Aliah UniversityKolkataIndia Department of Computer Science and Engineering SRM UniversityAmaravati522240APIndia School of Computer Science SCS Taylor’s UniversitySubang Jaya47500Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia

Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problemsobserved in the fuzzification of an unknown pattern is that importance is givenonly to the known patterns but not to their features. In contrast, features of thepatterns play an essential role when their respective patterns overlap. In this paper,an optimal fuzzy nearest neighbor model has been introduced in which a fuzzifi-cation process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has beenformed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completelyllabelled Telugu vowel data set, and the accuracy is compared with the differentmodels and the fuzzy k nearest neighbor algorithm. The proposed model gives84.86% accuracy on 50% training data set and 89.35% accuracy on 80% trainingdata set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

关键词： Nearest neighbors fuzzy classification patterns recognition reasoning rule membership matrix

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Design and Characterization of High Quality-Factor Inductors for Wireless Systems Compatible With Flexible Large-Area Electronics

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2024年第6期3卷 234-241页

作者： Ma, Yue Wagner, Sigurd Verma, Naveen Sturm, James C. Princeton University School of Engineering and Applied Science Department of Electrical and Computer Engineering PrincetonNJ08544 United States

Resonant operation, exploiting high quality-factor planar inductors, has recently enabled gigahertz (GHz) applications for large-area electronics (LAE), providing a new technology platform for large-scale and flexible wireless systems. This work first presents the design, analysis, and characterization methodology of flex-compatible large-area planar inductors. Specifically, three distinct radio frequency (RF) inductor characterization methods are experimentally demonstrated and compared, with the most accurate method among them (i.e., S-parameters in a two-port configuration) demonstrating a record-high quality factor of up to 65in the 2.4-GHz frequency band. Enabled by accurate characterization, key inductor design considerations regarding the resistive loss due to inductor's metal traces are then discussed. Finally, a case study of the recently demonstrated LAE resonant switch shows the potential of these high-performance inductors towards large-area and conformal wireless systems for integrated Internet of Things (IoT) and 5G/6G applications. © 2022 IEEE.

关键词： Substrates

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Trajectory-Aware Task Coalition Assignment in Spatial Crowdsourcing (Extended Abstract) 40

Trajectory-Aware Task Coalition Assignment in Spatial Crowds...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Xie, Yuan Wu, Fan Zhou, Xu Luo, Wensheng Yin, Yifang Zimmermann, Roger Li, Keqin Li, Kenli College of Computer Science and Electronic Engineering Hunan University Hunan China Institute of Data Science National University of Singapore Singapore State University of New York Department of Computer Science New Paltz United States School of Data Science Chinese University of Hong Kong Hong Kong A STAR Singapore

ISBN: (纸本)9798350317152

With the popularity of GPS-equipped smart devices, spatial crowdsourcing (SC) techniques have attracted growing attention in both academia and industry. In existing trajectory-aware task assignment approaches, tasks assigned to a worker may be far apart from each other, resulting in a higher detour cost as the worker needs to deviate from the original trajectory more often than necessary. Motivated by the above observations, we investigate a trajectory-aware task coalition assignment (TCA) problem and prove it to be NP-hard. The goal is to maximize the number of assigned tasks by assigning task coalitions to workers based on their preferred trajectories. To tackle the TCA problem, we develop a batch-based three-stage framework consisting of task grouping, planning, and assignment. Extensive experiments on real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed algorithms. © 2024 IEEE.

关键词： Trajectories

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Detecting Potential Phishing Attacks on Twitter Using Transformers 3

Detecting Potential Phishing Attacks on Twitter Using Transf...

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3rd International Conference on Smart Cities, Automation and Intelligent Computing Systems, ICON-SONICS 2023

作者： Kustanto, Matthieau Vic Pratama, Hafizh Farhan Gumay, Aryaputra Ekalaya Pratama, Galih Dea Hasana, Silviya Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781509062805

Phishing is a social engineering attack where criminals use URLs to deceive users to obtain their login credentials, and personal data, and get unauthorized access to a user's accounts or gadgets. Therefore, checking the legitimacy of URLs can play a big role in detecting phishing attacks. There are many methods that can achieve accurate phishing detection;the most popular method being machine learning and black-listing or white-listing approaches;leaving other methods like semantic analysis or deep learning approaches unexplored. This paper aims to propose a novel method capable of doing real-time detection and an open-source library that is a curated collection of models that is already made by and is available for everyone in the community. The novel transformers method consists of pre-trained transformers and a black-listing approach which will be combined to increase the efficiency and performance of detection before it can be used properly to classify phishing URLs on Twitter in real-time. This approach reached 97.9% accuracy using a balanced dataset, suggesting that transformers can be a powerful tool in counteracting phishing attacks. © 2023 IEEE.

关键词： Semantics

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Tree Counting with Deep Learning Algorithm and Carbon Evaluation using Pleiades Satellite Imagery data in Kulon Progo, Yogyakarta, Indonesia

Tree Counting with Deep Learning Algorithm and Carbon Evalua...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Wijaya, David Irwansyah, Edy Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Trees are important living creatures on earth where their existence is very important for the health of the Earth's atmosphere, for the exchange of oxygen and carbon dioxide, etc. That's why the number of existing trees must be maintained as much as possible. In this case, there are many ways to maintain the existence of the tree. To maintain the existence of trees, there are several ways to maintain them such as counting or mapping the trees that exist in the area. Since it's too hard to count the trees by hand, luckily some algorithms can be used to count the trees themselves, which can help to count or map the trees, known as deep forest algorithms. The deep forest algorithm is a package in python that can be used to train, predict, and evaluate the individual tree crowns from aerial RGB imagery using a deep learning network for object detection. The reason for using the algorithm itself is that, as of 2019, there are not many tree counting experiments using this algorithm, only 14 experiments based on the official website. Not only tree counting as the goal of this experiment, evaluating carbon for every detected tree itself can also help so that carbon can be maintained and not disposed of as a hazardous substance. The main purpose of conserving the carbon itself is to protect the environment from sustainable development. As for the result of this experiment, the trees detected by deep learning deep forest algorithms successfully detected 613 trees from a total of 788 trees, making it an accuracy of 77.97% in the Kulon Progo area. As the carbon evaluation from counting all parts of an oil palm tree, know that for single biomass of oil palm tree is 354,8641 kg/ tree and it makes a total of 613 detected trees by Deep Forest Algorithms is 217531.6933 kg carbon in the study area. © 2023 IEEE.

关键词： Carbon dioxide

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An Enhanced Approach for Divorce Prediction: Implementing XGBoost Machine Learning Model with Particle Swarm Optimization on the 54 Divorce Predictors Scale dataset 6

An Enhanced Approach for Divorce Prediction: Implementing XG...

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6th International Conference on Information and Communications Technology, ICOIACT 2023

作者： Artanto, Brychan Chang, Frenrico Situmeang, Jason Made Meiliana Achmad, Said Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315639

Divorce is the final termination of the marital relationship which can be caused by many things and has been increasing over the past few decades. According to this case, our paper proposes a novel study about divorce prediction using the XGBoost algorithm optimized with Particle Swarm Optimization (PSO). With the aim to enhance the performance of the divorce prediction model and provide valuable insights for effective intervention and support. Our experiment was conducted on a comprehensive Gottman dataset that consists of 54 variables known as the Divorce Prediction Scale (DPS) based on Gottman Couple's Therapy and collected from 170 participants. With the tuned hyperparameter of XGBoost and PSO, our experiment shows the effectiveness of the divorce prediction model with the proposed method. Achieving an accuracy ranging from 98.823% to 99.411% and outperforming traditional methods also provides valuable insights into the divorce prediction area. This paper contributes to the field of divorce prediction by introducing a novel algorithm and the results show that DPS can be used to predict divorce. However, studies on this topic can be further developed by considering the amount of data and the country in which data were collected. © 2023 IEEE.

关键词： Forecasting

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