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Nowadays, social media has evolved into a powerful networked ecosystem in which governments and citizens publicly debate economic and political issues. This holds true for the pros and cons of Indonesia’s ore nickel export restriction to Europe, which we aim to investigate further in this paper. Using Twitter as a dependable channel for conducting sentiment analysis, we have gathered 7070 tweets data for further processing using two sentiment analysis approaches, namely Support Vector Machine (SVM) and Long Short Term Memory (LSTM). Model construction stage has shown that Bidirectional LSTM performed better than LSTM and SVM kernels, with accuracy of 91%. The LSTM comes second and The SVM Radial Basis Function comes third in terms of best model, with 88% and 83% accuracies, respectively. In terms of sentiments, most Indonesians believe that the nickel ore provision will have a positive impact on the mining industry in Indonesia. However, a small number of Indonesian citizens contradict this policy due to fears of a trade dispute that could potentially harm Indonesia’s bilateral relations with the EU. Hence, this study contributes to the advancement of measuring public opinions through big data tools by identifying Bidirectional LSTM as the optimal model for the dataset.

Recently, in computer vision behavior recognition is an active research area that plays a significant role in smart cities for crime prevention and urban safety. However, without base knowledge of Artificial Intelligence (AI) designing an efficient model is very difficult because we need data and programing skills for implementing the system. To tackle this problem, we designed and implemented a system that allows a user having no professional knowledge to easily and conveniently create a deep learning model. The interface of this system consists of Data Selection, Model Training and Testing, and Model Parameter values according to domains and categories. In addition, we designed a function to check the test results for the model selected by the user. This system allows users to quickly and easily create and test models.

Masonry structures account for a large proportion of the building stock worldwide. Presently, the structural conditions of such structures are mostly inspected manually, and which is expensive, laborious and subjective processes. As deep learning technique for computer vision advances, there is an opportunity to automate the visual inspection process using digital images. Several studies are in progress to automatically detect cracks in masonry structures using Deep Learning. However, it is important not only detecting a crack, but also measuring a length of the crack. This is because it is necessary to consider various factors required in the actual environment, such as calculating the cost of reinforcement work. In this paper, we propose the method that detects masonry cracks and measures the length of cracks with digital images. The aim of this study is to implement Deep Learning model for crack detection on masonry structure and to apply the method of crack length measurement additionally.

The escalating importance of online reviews in customers' purchasing behavior has led to growing concerns about the prevalence of manipulated reviews, causing confusion in their decision-making process. However, despite several efforts to develop models for manipulated review detection, a critical aspect that has not been addressed is examining the connection between manipulated review writer personality traits and manipulated review detection. In this study, we examine the role of reviewer personality traits among the factors related to review characteristics in manipulated review detection using deep learning and explainable AI. The purpose of this research is to answer the question are reviewer personality matter in manipulated review detection and what features have an impact on detection. To rich our purpose, we apply deep learning twice: 1) to infer reviewers' personality traits from review text (CNN), 2) to create manipulated review detection models (RNN and CNN). Furthermore, we equip manipulated reviews detection model with one of the XAI techniques - SHAP, that generates visual explanations of the model's decision-making process, thereby enhancing the model's interpretability and transparency. We conduct our experiment by utilizing real-world review data from Yelp.com.

Accurate classification of public policy is essential for effective policy analysis, design, comparison, and formulation across countries. However, manual classification by policy experts can lead to inconsistencies and human errors, highlighting the need for a more reliable and efficient approach. This study proposes a deep learning-based model to support policy classification using artificial intelligence. Leveraging Korean policy datasets, comprising administrative data (1988–2018), legislative data (1987–2018), and media data (1988–2020), previously curated by experts, we developed an AI model for automated policy classification based on the KoBERT language model. Designed as a supplementary tool for policy experts, this model enhances classification consistency, reduces decision-making time, and improves overall productivity. Moreover, the model enables the classification, comparison, and evaluation of diverse policies at both local and national levels, offering valuable support for strategic public policy development. The proposed model achieved a Top-1 accuracy of 62.4% and a Top-3 accuracy of 71.6%, outperforming traditional baselines and demonstrating its practical potential for real-world policy analysis.

자율 주행과 교통 감시의 가장 중요한 것은 차량 감지 기술이다. 또한, 포트홀과 같이 도로의 특정 상황 은 교통사고와 차량 파손의 원인이다. 본 논문에서는 도로의 포트홀을 자동으로 발견하기 위해서 딥 러닝 모델을 사용한다. 본 연구에서는 이미지에서 차량 및 포트홀을 감지 할 수 있도록 빠른 영역 기반 컨볼루션 신경망 (Faster R-CNN)과 인셉션 네트워크 V2 모델을 사용하여 모델을 활용하였다. 제안하는 연구를 검증하기 위해 Faster R-CNN, Single Shot Detector(SSD), YOLO 알고리즘과 성능, 정확도수, 검출 시간 및 장단점을 비교 하였다. 논문에서 제안하는 방법은 SSD 및 YOLO와 같은 기존 방법보다 좋은 성능을 보여주었다. 여기서 성능 평가의 척도는 정확도를 사용하였다. 제안된 방법은 SSD 및 YOLO와 같은 이전 방법에 비해 6%의 개선을 보여준다.

Vehicle detection is the most crucial component of automated driving and traffic monitoring. Additionally, pothole-caused bad road conditions are to blame for collisions and car damage. Deep learning models are used in the suggested work. In this study, a fast region-based convolutional neural network (Faster R-CNN) and an inception network V2 model were utilized to detect vehicles and potholes in images. To verify the proposed study, Faster R-CNN, Single Shot Detector (SSD), and YOLO algorithms were compared in performance, number of accuracy, detection time, and strengths and weaknesses. Accuracy serves as the benchmark for performance evaluation. When compared to the earlier approaches, such as SSD and YOLO, the suggested method exhibits a 6% improvement.

Comparison of different optimizer performance in photovoltaic power modeling using artificial neural deep learning techniques is described in this paper. Six different deep learning optimizers are tested for Long-Short-Term Memory networks in this study. The optimizers are namely Adam, Stochastic Gradient Descent, Root Mean Square Propagation, Adaptive Gradient, and some variants such as Adamax and Nadam. For comparing the optimization techniques, high and low fluctuated photovoltaic power output are examined and the power output is real data obtained from the site at Mokpo university. Using Python Keras version, we have developed the prediction program for the performance evaluation of the optimizations. The prediction error results of each optimizer in both high and low power cases shows that the Adam has better performance compared to the other optimizers.

In response to the demand for impartial and precise personality testing, this study presents a unique multi-modal method for predicting personality traits in collaborative settings. Conventional approaches that depend on surveys frequently create biases, which has led to the investigation of raw, subconscious open writing as a rich source of personality information. This study uses deep learning algorithms in conjunction with the stream of consciousness storytelling approach to uncover personality traits by utilizing both textual and gestural data. We use BERT word embedding to improve contextual understanding and convolutional networks for the textual component. Compared to earlier methods, this methodology offers a more dependable way for text-based personality evaluation. Moreover, we present facial recognition as an extra factor for personality evaluation, providing a whole framework with a wide range of uses. We conducted studies in a collaborative setting to assess the effectiveness of our strategy, and we obtained encouraging findings. This multimodal method changes the way people collaborate and opens doors to a wide range of applications, such as mental health diagnosis, job interviews, and forensic investigations. A thorough grasp of personality features is expected to improve personalization and cooperation, leading to more efficient collaboration and improved decision-making.

최근 디지털 콘텐츠 중 E-Book과 웹 소설이 증가하고 있다. 삽화는 텍스트 콘텐츠에서 독자의 이해를 도 울 수 있다. 따라서 텍스트 콘텐츠를 분석하여 자동으로 삽화를 생성하는 여러 접근방법이 등장했다. 먼저 규칙 기반 접근방법은 문장을 분석하는 규칙과 분석된 문장 구조를 삽화로 변환하는 규칙을 모두 활용하여 삽화를 생성한다. 그러나 정해진 규칙에서 벗어나면 삽화를 생성하지 못 할 수도 있다는 단점이 있다. 다음 으로 통계 기반 접근방법은 통계를 기반으로 후보 중 가장 가능성이 높은 삽화를 생성한다. 그러나 통계 기 반 접근방법은 사람이 추출한 정보에 강하게 의존한다는 단점이 있다. 본 연구는 사람이 아닌 시스템이 자동 으로 정보를 추출하는 딥러닝을 활용한 삽화 생성 시스템을 제안한다. 제안하는 시스템은 형태소 분석기, 개 체명 인식기, 의존 구문 구조 분석기로 이루어져있다. 동화 "빨간모자"를 통해 평가하였고, 총 99문장 중 삽 화 생성이 가능한 문장이 28문장, 삽화 생성이 불가능한 문장이 71문장이었다. 삽화 생성이 불가능한 문장의 경우 대사가 41문장, 불명확한 주어가 2문장, 그리고 표현이 불가능한 서술어가 28문장이었다. 제안하는 시스 템이 올바른 삽화를 생성한 문장은 23문장, 부적절한 삽화를 생성한 문장은 2문장, 삽화를 생성하지 않은 문 장은 74문장으로 나타났다.

Recently, e-books and web novels increase in digital contents. Illustrations can help a reader to understand the text contents. Therefore, some approaches have been researched to automatically generate the illustration by analyzing the text contents. First, the rule-based approaches generate the illustration with both the rules analyzing the text and the rules to convert the illustration from the analyzed text; however, they can fail to generate correct illustration without the correct rules. Second, the statistical approaches generate the illustration by choosing the most likely candidate based on the statistics; but they tend to strongly depend on the information extracted by the human. In this paper, we propose an illustration generation system using the deep learning, which the information depends on the deep learning system, rather than the human. The proposed system is composed of the morphological analyzer, the named-entity recognizer, and the dependency parser. It is evaluated on the fairy tale 'Little Red Riding Hood' with the total 99 sentences including both 28 sentences with the illustrations, and 71 sentences without illustration: 41 sentences corresponding to the dialogue, 2 sentences with the unspecified subject, and 28 sentences with the unexpressive predicate. The proposed system generates 23 correct illustrations and 2 incorrect illustrations, while it cannot generate the illustration corresponding to 74 sentences.

The integration of deep learning techniques in the field of audio signal processing has marked a significant leap forward in the capability to analyze and classify complex sounds, including the nuanced and information-rich cries of infants. Deep learning's promise in this domain lies in its potential to decipher the subtle cues contained within these cries, offering insights into an infant's health, emotional state, and developmental needs. This potential application stands at the intersection of technology and healthcare, promising to enhance our understanding and response to the needs of the youngest members of society. This study experimentally demonstrates that a convolutional neural network–based audio classification model effectively learns discriminative spectral and temporal features from audio signals. Experimental results show that the proposed convolutional neural networks architecture achieves significantly higher classification accuracy than traditional machine-learning baselines, particularly when trained on spectrogram-based representations. The findings confirm that deep learning models not only improve overall performance but also provide robust generalization across different audio classes and noisy conditions.

Sugarcane is an essential crop in the global agriculture industry. There are lot of diseases in plants of growing sugarcane typically involve in five classes. These diseases consist of Mosaic, Red rot, Yellow, Rust and Healthy. Therefore, this study used to train and testi a deep learning model comprising of 2521 Sugar cane image dataset of disease-infected leaves. This research provides a sequential model for the classification of sugar cane using convolutional neural network. This study used sequential network in which ten layers are adjusted for the classification of these Mosaic, Red rot, yellow, Rust and healthy diseases. The accuracy of the proposed method works better in comparison with the previously used techniques.

Social media has become one of the most popular channels to keep updated with daily news because it can quickly and easily access information. This advantage is used by malicious people to spread fake news widely. Since the COVID-19 pandemic, fake news has become a huge social problem, causing people to panic and misunderstand how to cure or protect themselves from the virus. So, the goal of this research is to use deep learning as the Recurrent Neural Network (RNN) model to find fake news about COVID-19 in the Thai language on social media and help filter information by classifying real and fake news.

Background/Objectives Deep learning which main technology in AI has high growth with being applied to field of speech recognition and Image classification. Especially, Deep learning technology in the field of Image classification is being applied as a core technology to Self-driving and crime prevention monitoring system that is recently emerging as the future industry. Methods/Statistical analysis: Various algorithm which is improved and developed CNN being able to do image process is suggested as Deep learning model in image recognition field. In this paper, we introduce various object detection algorithm including CNN. And explore most representative algorithms just R-CNN, Fast R-CNN, Faster R-CNN and difference between versions of YOLO devised to detect and track in real time. Findings: This paper evaluates deep learning algorithm’s performance by comparative analysis about mAP (mean average precision) and FPS (frames per second). In result of performance evaluation, YOLO algorithm is confirmed as that It shows excellent result in speed that detects and recognizes object and accuracy in real time system environment. Finally, we search cases in field of autonomous driving and access control system and home anti-crime system. Improvements/Applications: In this research, we can understand object detection algorithm among speech recognition technologies and proper field in each algorithm, apply security service based on image, recommend proper algorithm in various environment just like autonomous driving and security work, etc.

Germination of chili pepper seeds is critical for crop yield and resource utilization. A high germination rate increases yield and effectively reduces resource wastage. This study collected 450 macroscopic images of chili pepper seeds and constructed a dataset for deep learning training through standardized germination experiments. Six deep learning models were evaluated to improve the chili pepper seed classification accuracy and germination rate. After comparing the performance of the models, MobileNet_v2 performed the best, not only having the fewest number of parameters but also achieving a 98.89% accuracy and 97.82% F1 score. The model improved the original germination rate from 87.33% to 100% on the test set, significantly optimizing the seed selection process

Germination of chili pepper seeds is critical for crop yield and resource utilization. A high germination rate increases yield and effectively reduces resource wastage. This study collected 450 macroscopic images of chili pepper seeds and constructed a dataset for deep learning training through standardized germination experiments. Six deep learning models were evaluated to improve the chili pepper seed classification accuracy and germination rate. After comparing the performance of the models, MobileNet_v2 performed the best, not only having the fewest number of parameters but also achieving a 98.89% accuracy and 97.82% F1 score. The model improved the original germination rate from 87.33% to 100% on the test set, significantly optimizing the seed selection process

Among the major reasons for death in humans, brain tumors are the most prevalent type and it affects humans of all ages. Brain tumors are treatable if detected in early stages. The classification of Tumors is being done by biopsy. On the Other hand, Magnetic Resonance Imaging (MRI) is a routine technique for humans to investigate this disease (Brain Tumors). In contrast, avoiding the need for a Radiologist, the detection and classification method proposed by using the Deep Learning Technique in this paper would benefit to all doctors globally. This work focused on a new Sequential base Convolutional Neutral Network (CNN) Architecture to classify the Brain Tumor types such as Glioma-Tumors, Meningioma tumors, No-tumors, and Pituitary tumors using MRI images. The proposed method gives better results for classifying Brain Images from a given dataset of Brain tumors with around 3264 MRI images. The purpose of our work is to use the Sequential base CNN model to detect brain cancers. The accuracy of our model's performance will be assessed. Consequently, we may infer that the Sequential base CNN model produces results that are very adequate and have an increased accuracy. Finally, the proposed method improves the accuracy up to 82.66%.

With the availability of big customer data and advances in machine learning techniques, the prediction of customer behavior at the session-level has attracted considerable attention from marketing practitioners and scholars. This study aims to predict customer purchase conversion at the session-level by employing customer profile, transaction, and clickstream data. For this purpose, we develop a multimodal deep learning fusion model with dynamic and static features (i.e., DS-fusion). Specifically, we base page views within focal visist and recency, frequency, monetary value, and clumpiness (RFMC) for dynamic and static features, respectively, to comprehensively capture customer characteristics for buying behaviors. Our model with deep learning architectures combines these features for conversion prediction. We validate the proposed model using real-world e-commerce data. The experimental results reveal that our model outperforms unimodal classifiers with each feature and the classical machine learning models with dynamic and static features, including random forest and logistic regression. In this regard, this study sheds light on the promise of the machine learning approach with the complementary method for different modalities in predicting customer behaviors.

One of the emerging trends in the marketing field is digital video marketing. Online videos offer rich content typically containing more information than any other type of content (e.g., audible or textual content). Accordingly, previous researchers have examined factors influencing videos’ popularity. However, few studies have examined what causes a video to remain popular. Some videos achieve continuous, ongoing popularity, while others fade out quickly. For practitioners, videos at the recommendation slots may serve as strong communication channels, as many potential consumers are exposed to such videos. So,this study will provide practitioners important advice regarding how to choose videos that will survive as long-lasting favorites, allowing them to advertise in a cost-effective manner. Using deep learning techniques, this study extracts text from videos and measured the videos’ tones, including factual and emotional tones. Additionally, we measure the aesthetic score by analyzing the thumbnail images in the data. We then empirically show that the cognitive features of a video, such as the tone of a message and the aesthetic assessment of a thumbnail image, play an important role in determining videos’ long-term popularity. We believe that this is the first study of its kind to examine new factors that aid in ensuring a video remains popular using both deep learning and econometric methodologies.