Indonesian Journal of Electrical Engineering and Computer Science
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Predictive modeling for equity trading using sentiment analysis
Get PDFWarren Buffett’s investment philosophy highlights the importance of generating wealth through available capital, but investors require more advanced tools for informed decision-making. Current research is focused on developing a modeling technique that leverages computer algorithms, including sentiment analysis. This method evaluates public sentiment about companies through social media, aiding investors in identifying promising stocks and safeguarding their wealth against unfavorable market conditions. In India, the banking, real estate, and pharmaceutical sectors are among the most robust and rapidly growing industries; however, deciding to invest in these sectors remains debatable. To address this, the proposed study aims to develop a hybrid prediction model that combines sentiment and technical analysis to uncover short-term trading opportunities. This model utilizes a two-layer ensemble stacking technique, training three distinct machine learning algorithms in the first layer and aggregating their outputs in the second layer. The proposed model significantly outperforms traditional methods in terms of accuracy, enabling investors to make more confident and profitable decisions in the Indian stock market
Development of mobile-based Batak script recognition application using YOLOv8 algorithm
Get PDFThe Batak people are one of the ethnic groups that pass down many values and traditions to each generation, including the written tradition known as the Batak script. The Batak Toba people, in particular, have the Batak Toba script as part of their local wisdom that needs to be preserved and maintained. However, the use of the Batak script has significantly declined in the current era. To prevent the loss of this heritage, preservation through technology is necessary. This research utilizes a deep learning approach using the YOLOv8 algorithm to detect images of script objects, provide the coordinates of the script locations, and perform object recognition based on the dataset. The final result of this research is an Android-based application that can detect the Batak Toba script in real time and upload images. The research process involves experiments on several hyperparameters, such as epochs with a value of 200, confidence threshold, and IoU with a value of 0.5. The model evaluation shows excellent results, with a precision of 0.945, recall of 0.902, [email protected] of 0.954, and a high confidence score from the application's detection
Real-time recognition of Indonesian sign language SIBI using CNN-SVM model combination
Get PDFReal-time Sistem Isyarat Bahasa Indonesia (SIBI) sign language recognition plays a crucial role in improving accessibility for individuals with hearing and speech impairments. Despite advancements in SIBI recognition research, challenges remain in ensuring model stability and accuracy in realtime settings, particularly in handling gesture variations and classification inconsistencies. This study addresses these challenges by developing a convolutional neural network-support vector machine (CNN-SVM) combination model, integrating MediaPipe for hand coordinate extraction, CNN for feature extraction, and SVM for classification. To improve generalization and prevent overfitting, data augmentation is applied to expand the dataset. The model's performance is further enhanced through hyperparameter optimization (HPO) and post-processing techniques such as multi-window majority voting (MWMV) and SymSpell. Experimental results show that the CNN-SVM model trained on augmented data with HPO achieves 91% testing accuracy, outperforming both standalone CNN and SVM models. Furthermore, MWMV improves recognition stability, while SymSpell enhances spelling errors, ensuring more meaningful outputs. The system is integrated with OpenCV for real-time recognition, but current deployment remains limited to local execution. Future work will focus on developing lightweight models for web-based and mobile applications, making the system more accessible and scalable
Modern machine learning and deep learning algorithms for preventing credit card frauds
Get PDFCredit card fraud poses a significant threat to financial institutions and consumers, particularly in the context of online transactions. Conventional rule-based systems often struggle to keep pace with the evolving tactics of fraudsters. This research paper investigates the application of advanced machine learning and deep learning algorithms for credit card fraud detection. By reviewing existing methodologies and addressing the challenges associated with fraud detection, we explore the potential of stateof-the-art techniques in enhancing detection accuracy and efficiency. Key aspects such as transaction data analysis, feature engineering, model evaluation metrics, and practical implementations are discussed. The findings underscore the importance of leveraging advanced algorithms to combat fraudulent activities effectively, thereby safeguarding the integrity of online transactions
A novel multimodal model for detecting Vietnamese toxic news using PhoBERT and Swin Transformer V2
Get PDFNews articles with fake, toxic or reactionary content are currently posted and spreaded very strongly due to the popularity of the Internet and especially the explosion of social networks and online services in cyberspace. Toxic news, especially reactionary news aimed at Vietnam, such as online articles spreading false information, slandering leaders, inciting destruction of the great national unity bloc, have a great impact on social life because they can spread quickly and have many forms of expression, such as news in the forms of text, images, videos, or a combination of text and images. Due to the seriousness of articles posting fake, toxic or reactionary news in cyberspace, there have been a number of studies in Vietnam and abroad for detection and prevention. However, most of the proposals focus on handling fake and toxic news posted using the English language. Furthermore, due to a large number of online news are posted in the form of images, or text embedded in images and videos, it is very difficult to process these news, leading to a relatively low detection rate. This paper proposes a multimodal model based on the combination of PhoBERT and Swin Transformer V2 for detecting fake and toxic news in both forms of text and images. Comprehensive experiments conducted on a dataset of 8,000 text and image news articles demonstrate that the proposed multimodal model surpasses both individual models and previous approaches, achieving 95% accuracy and 95% F1-score
New technic of transfer learning for detecting epilepsy by EfficientNet and DarkNet models
Get PDFEpileptic seizures are one of the most prevalent brain disorders in the world. Electroencephalography (EEG) signal analysis is used to distinguish between normal and epileptic brain activity. To date, automatic diagnosis remains a highly relevant and significant research topic which can help in this task, especially considering that such diagnosis requires a significant amount of time to be carried out by an expert. As a result, the need for an effective seizure approach capable to classify the normal and epileptic brain signal automatically is crucial. In this perspective, this work proposes a deep neural network approach using transfer learning to classify spectrogram images that have been extracted from EEG signals. Initially, spectrogram images have been extracted and used as input to pre-trained models, and a second refinement is performed on certain feature extraction layers that were previously frozen. The EfficientNet and DarkNet networks are used. To overcome the lack of data, data augmentation was also carried out. The proposed work performed excellently, as assessed by multiple metrics, such as the 0.99 accuracy achieved with EfficientNet combined with a support vector machine (SVM) classifier
Optimizing photovoltaic system performance through MPPT synergetic adaptive control
Get PDFThis paper investigates enhancement of energy conversion through the implementation of new MPPT control strategy based on synergetic adaptive control (SAC) for a photovoltaic system. The architecture of this system encompasses a photovoltaic module, a DC-DC boost converter, a resistive load, and an MPPT controller. The controller amalgamates two distinct methodologies: the initial algorithm deduces the peak power current through a perturbation and observation (P&O) method, which serves as the reference point for the subsequent algorithm founded on synergetic adaptive control. The parameters for the latter are refined through the particle swarm optimization (PSO) technique This innovative method is employed to ascertain the optimal power output across varying weather conditions, aiming to enhance power transmission performance irrespective of meteorological variations. The efficacy of this strategy was affirmed through a comparative study with the conventional P&O method using MATLAB/Simulink simulations, which verified the superior performance of the proposed algorithm
Optimal land distribution for ambiguous profit vegetable crops using multi-objective fuzzy linear programming
Get PDFDecisions in agriculture had been driven by methodical planning to increase yields to cater to the needs of overwhelming populations while also allowing farmers to prosper. Allocating land to various crops by making use of limited resources is becoming a crucial challenge for achieving higher profits. To make cropping pattern decisions, farmers traditionally rely on experience, instinct, and comparisons with their neighbors. Since profit varies depending on many factors, intuition and experience usually cannot guarantee optimal (maximum) profits. A number of research studies on linear programming (LP) have shown optimum cropping patterns when crop prices (profits) are fixed. Vegetable crops, also known as cash crops, are subject to a high degree of price volatility owing to the fact that their production is costly and they carry a significant risk of not being profitable, despite the fact that they provide higher earnings than food crops. The net returns of crops in agriculture are greatly impacted by price uncertainty. With the use of the optimization tool TORA, a step-by-step process is shown in this paper to solve the model and manage the volatility in vegetable crop profitability using fuzzy multi-objective linear programming (FMOLP)
Security challenges and strategies for CNN-based intrusion detection model for IoT networks
Get PDFThe rapid proliferation of internet-of-things (IoT) networks has revolutionized various industries but has also exposed them to a myriad of security threats. These networks are particularly vulnerable to sophisticated cyber-attacks due to their distributed nature, resource constraints, and the diverse range of connected devices. To safeguard IoT systems, intrusion detection systems (IDS) have emerged as a critical security measure. Among these, convolutional neural network (CNN)-based models offer promising capabilities in recognizing and mitigating malicious activities within IoT environments. This paper addresses the security challenges specific to IoT networks and explores the critical aspects of identifying malicious packets that threaten their integrity. It also delves into the general challenges associated with implementing IDS in IoT settings, such as the need for real-time detection, resource efficiency, and adaptability to evolving threats. The discussion extends to potential strategies for enhancing CNN-based IDS. The paper concludes by summarizing the key findings and proposing directions for future research to overcome the identified challenges, ultimately contributing to the development of more robust and effective IDS solutions for securing IoT networks
A review on power transformer failures: analysis of failure types and causative factors
Get PDFThis article analyzes power transformers and their components, types of damage, factors causing them. The advantage of this review article is that it was initially conducted a theoretical analysis based on published articles on power transformer damage in recent years. Then a statistical analysis was carried out on damaged power transformers in real condition. In the theoretical analysis, the articles published in the databases in recent years were first identified by keywords, and then sorted according to their relevance to the topic. A statistical examination of the damaged power transformers was performed utilizing the theoretical approach. According to the results of the analysis, damage to power transformers in 6(10) kV networks occurs mainly in 100 kVA, 160 kVA and 250 kVA power transformers. One of the factors that cause the power transformer to fail is the irregular implementation of restrictions on the power supply to electrical consumers. And these failures mainly damage the windings of the power transformer. We hope that the materials in this analytical article will serve as a crucial resource