Enhanced mixup for improved time series analysis

Time series data analysis is crucial for real-world applications. While deep learning has advanced in this field, it still faces challenges, such as limited or poor-quality data. In areas like computer vision, data augmentation has been widely used and highly effective in addressing similar issues. However, these techniques are not as commonly explored or applied in the time series domain. This paper addresses the gap by evaluating basic data augmentation techniques using MLP, CNN, and Transformer architectures, prioritized for their alignment with state-of-the-art trends in time series analysis rather than traditional RNN-based methods. The goal is to expand the use of data augmentation in time series analysis. The paper proposed EMixup, which adapts the Mixup method from image processing to time series data. This adaptation involves mixing samples while aiming to maintain the data's temporal structure and integrating target contributions into the loss function. Empirical studies show that EMixup improves the performance of time series models across various architectures (improving 23/24 forecasting cases and 12/24 classification cases). It demonstrates broad applicability and strong results in tasks like forecasting and classification, highlighting its potential utility across diverse time series applications

Optimizing LPG distribution: A hybrid particle swarm optimization and genetic algorithm for efficient vehicle routing and cost minimization

This paper aims to develop an optimized solution for the Vehicle Routing Problem (VRP), tailored explicitly for Liquid Petroleum Gas (LPG) distribution, with a focus on minimizing transportation costs and enhancing delivery reliability. The critical role of LPG as an essential public infrastructure commodity, widely utilized for cooking and heating, makes its efficient and reliable distribution a significant logistical challenge due to the strict adherence to delivery time windows, heterogeneous fleets, multi-trip scenarios, and intricate loading and unloading requirements. To address these complexities, this study proposes a novel hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) that uniquely integrates multi-trip routing, time windows, and heterogeneous vehicle fleet management into a single optimization framework. The dual-phase optimization strategy leverages the exploratory capability of PSO and the solution-refining power of GA, resulting in high-quality, feasible solutions. Validation against real-world data involving VRP instances with 88 and 40 stations demonstrates the model’s practical impact, achieving reductions of up to 4.56% in transportation costs compared to existing operational routes. This research makes a significant contribution to interdisciplinary domains, including logistics optimization, sustainability, and energy distribution, by offering a robust and scalable model that comprehensively addresses complex, real-world VRP constraints

Machine learning-based B2C software project success prediction model in Indonesia

The success of a software project is a crucial factor in the information technology industry, but it is often difficult to predict due to its complexity and high dynamics. This research aims to develop a model for predicting the success of software projects, particularly B2C e-business software in Indonesia, utilizing a machine learning approach. This study involved 28 variables that affect the success of software projects obtained from previous research. The dataset was compiled from the historical records of software projects from various software development companies in Indonesia. The predictive model was developed using Support Vector Machine and Artificial Neural Network algorithms, with hyperparameter tuning performed via Grid Search. The modelling process includes the pre-processing stage of data, which involves synthetic data generation due to inadequate data collection, as well as the application of several dataset mining techniques (SMOTE, ADASYN, SMOTE Tomek Links, and ADASYN Tomek Links). Additionally, model training and performance evaluation are conducted using a confusion matrix. The search for important features using the Shapley Additive Explanations method is also conducted to develop an automated recommendation system based on key factors that require improvement. The results showed that the SVM model with Grid Search tuning of hyperparameters in the SMOTE Tomek Links data test yielded the best performance, with an accuracy of 87.8%, demonstrating the significant potential of machine learning in identifying project success factors from the early stages. This study contributes to the development of decision-support tools for B2C project managers in Indonesia by providing accurate early predictions and interpretable recommendations

Detection and classification of lung diseases in distributed environment

A significant increase in the size of the medical data, as well as the complexity of medical diagnosis, poses challenges to processing this data in a reasonable time. The use of big data is expected to have the upper hand in managing the large-scale datasets. This research presents the detection and prediction of lung diseases using big data and deep learning techniques. In this work, we train neural networks based on Faster R-CNN and RetinaNet with different backbones (ResNet, CheXNet, and Inception ResNet V2) for lung disease classification in a distributed and parallel processing environment. Moreover, we also experimented with three new network architectures on the medical image dataset: CTXNet, Big Transfer (BiT), and Swin Transformer, to evaluate their accuracy and training time in a distributed environment. We provide ten scenarios in two types of processing environments to compare and find the most promising scenarios that can be used for the detection of lung diseases on chest X-rays. The results show that the proposed method can accurately detect and classify lung lesions on chest X-rays with an accuracy of up to 96%. Additionally, we use Grad-CAM to highlight lung lesions, thus radiologists can clearly see the lesions’ location and size without much effort. The proposed method allows for reducing the costs of time, space, and computing resources. It will be of great significance to reduce workloads, increase the capacity of medical examinations, and improve health facilities

Classification of Bitter gourd leaf disease using deep learning architecture ResNet50

The primary goal of this research is to develop a feasible and efficient method for identifying the disease and to advocate for an appropriate system that provides an early and cost-effective solution to this problem. Due to their superior computational capabilities and accuracy, computer vision and machine learning methods and techniques have garnered significant attention in recent years for classifying various leaf diseases. As a result, Resnet50 and Resnet101 were proposed in this study for the classification of bitter gourd disease. The 2490 images of bitter gourd leaves are classified into three categories: Healthy leaf, Fusarium Wilt leaf, and Yellow Mosaic leaf. The proposed ResNet50 architecture accomplished 98% accuracy with the Adam optimizer. The ResNet101 architecture achieves an average accuracy of 94% with the Adam optimizer. As a result, the proposed model can differentiate between healthy and diseased bitter gourd leaves. This research contributes to the development of methods for detecting bitter melon leaf disease using computer vision and machine learning, achieving high accuracy and supporting automatic disease diagnosis. The results can help farmers quickly and cost-effectively detect diseases early, thereby increasing agricultural productivity

Optimization hybrid weighted switching filtering (OHWSF) using SVD and SVD++ for addressing data sparsity

Recommender systems are crucial for filtering vast amounts of digital content and providing personalized recommendations; however, their effectiveness is often hindered by data sparsity, where limited user-item interactions lead to reduced prediction accuracy. This study introduces a novel hybrid model, Optimization Hybrid Weighted Switching Filtering (OHWSF), to overcome this challenge by integrating two complementary strategies: Hybrid Weighted Filtering (HWF), which linearly combines predictions from SVD and SVD++ using a weighting parameter (α), and Hybrid Switching Filtering (HSF), which dynamically selects predictions based on a threshold rating (θ). The OHWSF framework introduces a tunable optimization mechanism governed by the parameter σ₁ to adaptively balance weighting and switching decisions based on actual rating deviations. Unlike existing static or manually tuned hybrid methods, the proposed model combines dynamic switching with weight optimization to minimize prediction error effectively. Extensive experiments on four benchmark datasets (ML-100K, ML-1M, Amazon Cell Phones Reviews, and GoodBooks-10K) demonstrate that OHWSF consistently outperforms traditional collaborative filtering (UBCF, IBCF), matrix factorization techniques (SVD, SVD++), and standalone hybrid models across all evaluation metrics (MAE, MSE, RMSE). The model achieves optimal performance within the range of α = 0.6–0.9 and θ = 1.0–1.5, demonstrating robustness across varying sparsity levels. Notably, OHWSF achieves up to 742.16% MAE improvement over the UBCF model, with significantly reduced training time compared to SVD++. These findings confirm that OHWSF significantly improves prediction accuracy, scalability, and adaptability in sparse data environments. This research contributes a flexible, interpretable, and efficient hybrid recommendation framework suitable for real-world applications

Privacy-Preserving U-Net Variants with pseudo-labeling for radiolucent lesion segmentation in dental CBCT

Accurate segmentation of radiolucent lesions in dental Cone-Beam Computed Tomography (CBCT) is vital for enhancing diagnostic reliability and reducing the burden on clinicians. This study proposes a privacy-preserving segmentation framework leveraging multiple U-Net variants—U-Net, DoubleU-Net, U2-Net, and Spatial Attention U-Net (SA-UNet)—to address challenges posed by limited labeled data and patient confidentiality concerns. To safeguard sensitive information, Differential Privacy Stochastic Gradient Descent (DP-SGD) is integrated using TensorFlow-Privacy, achieving a privacy budget of ε ≈ 1.5 with minimal performance degradation. Among the evaluated architectures, U2-Net demonstrates superior segmentation performance with a Dice coefficient of 0.833 and an Intersection over Union (IoU) of 0.881, showing less than 2% reduction under privacy constraints. To mitigate data annotation scarcity, a pseudo-labeling approach is implemented within an MLOps pipeline, enabling semi-supervised learning from unlabeled CBCT images. Over three iterative refinements, the pseudo-labeling strategy reduces validation loss by 14.4% and improves Dice score by 2.6%, demonstrating its effectiveness. Additionally, comparative evaluations reveal that SA-UNet offers competitive accuracy with faster inference time (22 ms per slice), making it suitable for low-resource deployments. The proposed approach presents a scalable and privacy-compliant framework for radiolucent lesion segmentation, supporting clinical decision-making in real-world dental imaging scenarios

An enhanced pivot-based neural machine translation for low-resource languages

This study examines the efficacy of employing Indonesian as an intermediary language to improve the quality of translations from Javanese to Madurese through a pivot-based approach utilizing neural machine translation (NMT). The principal objective of this research is to enhance translation precision and uniformity among these low-resource languages, hence advancing machine translation models for underrepresented languages. The data collecting approach entailed extracting parallel texts from internet sources, followed by pre-processing through tokenization, normalization, and stop-word elimination algorithms. The prepared datasets were utilized to train and assess the NMT models. An intermediary phase utilizing Indonesian is implemented in the translation process to enhance the accuracy and consistency of translations between Javanese and Madurese. Parallel text corpora were created by collecting and preprocessing data, thereafter, utilized to train and assess the NMT models. The pivot-based strategy regularly surpassed direct translation regarding BLEU scores for all n-grams (BLEU-1 to BLEU-4). The enhanced BLEU ratings signify increased precision in vocabulary selection, preservation of context, and overall comprehensibility. This study significantly enhances the current literature in machine translation and computational linguistics, especially for low-resource languages, by illustrating the practical effectiveness of a pivot-based method for augmenting translation precision. The method's dependability and efficacy in producing genuine translations were proved through numerous studies. The pivot-based technique enhances translation quality, although it possesses limitations, including the risk of error propagation and bias originating from the pivot language. Further research is necessary to examine the integration of named entity recognition (NER) to improve accuracy and optimize the intermediate translation process. This project advances the domains of machine translation and the preservation of low-resource languages, with practical implications for multilingual communities, language education resources, and cultural conservation

Advanced deep learning techniques for sentiment analysis: combining Bi-LSTM, CNN, and attention layers

Online platforms enhance customer engagement and provide businesses with valuable data for predictive analysis, critical for strategic sales forecasting and customer relationship management. This study explores in depth the potential of sentiment analysis (SA) to enhance sales forecasting and customer retention for small and large businesses. We collected a large dataset of product review tweets, representing a rich consumer sentiment source. We developed an artificial neural network based on a dataset of product review tweets that captures both positive and negative sentiments. The core of our model is Bi-LSTM (Bidirectional Long Short-Term Memory) architecture, enhanced by an attention mechanism to capture relationships between words and emphasize key terms. Then, a one-dimensional convolutional neural network with 64 filters of size 3x3 is applied, followed by Average_Max_Pooling to reduce the feature map. Finally, two dense layers classify the sentiment as positive or negative. This research provides significant benefits and contributions to sentiment analysis by accurately identifying consumer sentiment in product review tweets. The proposed model that integrated Bi-LSTM with attention mechanism and CNN detects negative sentiment with a precision of 0.97, recall of 0.98, and F1-score of 0.98, allowing companies to address customer concerns, improving satisfaction and brand loyalty proactively. In addition, the proposed model presents a better sentiment classification on average for both positive and negative sentiments, and accuracy (96%) compared to the other baselines. It ensures high-quality input data by reducing noise and inconsistencies in product review tweets. Moreover, the dataset collected in this study serves as a valuable benchmark for future research in sentiment analysis and predictive analytics

Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting Pneumonia

This study focused on algorithm performance and training/testing time, evaluating the most suitable chest X-ray image size for machine learning models to predict pneumonia infection. The neural network algorithm achieved an accuracy rate of 87.00% across different image sizes. While larger images generally yield better results, there is a decline in performance beyond a certain size. Lowering the image resolution to 32x32 pixels significantly reduces performance to 83.00% likely due to the loss of diagnostic features. Furthermore, this study emphasizes the relationship between image size and processing time, empirically revealing that both increasing and decreasing image size beyond the optimal point results in increased training and testing time. The performance was noted with 299x299 pixel images completing the process in seconds. Our results indicate a balance between efficiency, as larger images slightly improved accuracy but slowed down speed, while smaller images negatively impacted precision and effectiveness. These findings assist in optimizing chest X-ray image sizes for pneumonia prediction models by weighing diagnostic accuracy against computational resources