Detail Information Research Articles

Optimizing Online Channels: Exploring the Interplay of E-Commerce in Customer Journey of Smartphone & Gadget Retail

The rise of e-commerce has transformed the global retail industry, including the smartphone and gadget sector. In Indonesia, while online shopping has become more popular due to its convenience, smartphone brands still face challenges. Despite the growth of online platforms, many customers prefer shopping in physical stores, especially after the pandemic. This research aims to understand what drives consumers to shop online for smartphones and gadgets, and to offer insights and strategies for both smartphone manufacturers and e-commerce platforms to boost their online presence and attract more customers. The study addresses key questions about consumer behavior: What motivates people to buy smartphones and gadgets online rather than in-store? How do they perceive their online shopping experience, and what factors affect their satisfaction? What obstacles do they face when shopping online, and how do they overcome them? How do they react to personalized marketing and recommendations? And what drives their loyalty and repeat purchases in the online market? By using the Customer Decision Journey, Marketing Technology, and Porter’s Five Forces analysis, the research identifies important factors like delivery, promotions, store information, and ease of finding product details. The study will provide actionable business solutions and recommendations to help smartphone manufacturers and e-commerce platforms tackle challenges and improve their online market strategies.

Mapping burned areas in Thailand using Sentinel-2 imagery and OBIA techniques

Monitoring burned areas in Thailand and other tropical countries during the post-harvest season is becoming increasingly important. High-resolution remote sensing data from Sentinel-2 satellites, which have a short revisit time, is ideal for accurately and efficiently mapping burned regions. However, automating the mapping of agriculture residual on a national scale is challenging due to the volume of information and level of detail involved. In this study, a Sentinel-2A Level-1C Multispectral Instrument image (MSI) from February 27, 2018 was combined with object-based image analysis (OBIA) algorithms to identify burned areas in Mae Chaem, Chom Thong, Hod, Mae Sariang, and Mae La Noi Districts in Chiang Mai, Thailand. OBIA techniques were used to classify forest, agricultural, water bodies, newly burned, and old burned regions. The segmentation scale parameter value of 50 was obtained using only the original Sentinel-2A band in red, green, blue, near infrared (NIR), and Normalized Difference Vegetation Index (NDVI). The accuracy of the produced maps was assessed using an existing burned area dataset, and the burned area identified through OBIA was found to be 85.2% accurate compared to 500 random burned points from the dataset. These results suggest that the combination of OBIA and Sentinel-2A with a 10 m spatial resolution is very effective and promising for the process of burned area mapping.

Efficacy of dietary supplements on mortality and clinical outcomes in adults with sepsis and septic shock: A systematic review and network meta-analysis

The aim of this network meta-analysis (NMA) was to investigate the effects of different dietary supplements on the mortality and clinical status of adults with sepsis. We searched PubMed, EMBASE, and the Cochrane Library Central Register of Controlled Trials until February 2023. The inclusion criteria were: 1) randomized controlled trials (RCT)s; 2) adults suffering sepsis or septic shock; 3) evaluation of short- or long-mortality; and 4) publications between 1994 and 2023. The general information of studies and details of interventions were extracted. The primary outcome was short-term mortality (<90 days), and the secondary outcomes were long-term mortality (≥90 days), length of ICU and hospital stays, and duration of mechanical ventilation (MV). The risk of bias of RCTs was assessed using the Cochrane risk of bias tool 2 (ROB2). A random effect NMA was performed to rank the effect of each intervention using a frequentist approach. Finally, 56 RCTs with 5957 participants met the criteria. Approximately, one-third of RCTs were low risk of bias. NMA analysis revealed that there was no treatment more effective in short- or long-term mortality than control or other interventions, except for magnesium (RR: 0.33, 95% CI: 0.14, 0.79; GRADE=low) and vitamin C (RR: 0.81, 95% CI: 0.67, 0.99; low certainty evidence), which had beneficial effects on short-term mortality. Moreover, eicosapentaenoic acid, gamma-linolenic acid, and antioxidants (EPA+GLA+AOs) combination was the most effective, and magnesium, vitamin D and vitamin C were the other effective approaches in terms of duration of MV, and ICU length of stay. There was no beneficial dietary supplement for hospital stay in these patients. In septic patients, none of the dietary supplements had a substantial effect on mortality except for magnesium and vitamin C, which were linked to lower short-term mortality with low certainty of evidence. Further investigation into high-quality studies with the use of dietary supplements for sepsis should be highly discouraged.

An improved immersed boundary method with local flow pattern reconstruction and its validation

This study introduces an immersed boundary (IB) method based on coefficient array transformations of discrete equations for local cells and local flow pattern reconstruction, for the simulation of turbulent flow and combustion chemistry inside combustors with complex structure. This IB method is combined with a geometric scanning algorithm that traverses each fluid grid point in the vicinity of the wall, and based on the exact wall positions and normal vectors obtained from the scanning, the coefficient matrices of the individual grid points and their discrete forms of the governing equations are transformed, and the boundary conditions are added implicitly and exactly. The effectiveness of the method is validated through simulations of a cylinder, a gas turbine model combustor [Meier et al., “Spray and flame structure of a generic injector at aeroengine conditions,” in Proceedings of the ASME 2011 Turbo Expo: Power for Land, Sea, and Air (American Society of Mechanical Engineers, 2011), pp. 61–72 and Freitag et al., “Measurement of initial conditions of a kerosene spray from a generic aeroengine injector at elevated pressure,” Atomization Sprays 21, 521 (2011)], and a specific aero-engine combustor, demonstrating precision comparable to traditional body-fitted mesh approaches, especially for complex combustor structures. The simulation demonstrates that the IB method achieves accuracy comparable to a fitted grid when it provides boundary information of similar quality and detail for control equations. The locally reconstructed IB method introduced in this paper successfully delivers high-precision boundary conditions, making it valuable for practical engineering applications.

Bidirectional Feature Aggregation Network for Stereo Image Quality Assessment Considering Parallax Attention-Based Binocular Fusion

Inspired by the two-path visual information processing mechanism (i.e., a bottom-up path and a top-down path), we propose a bidirectional binocular feature aggregation based stereo image quality assessment (SIQA) network, which considers a two-path visual mechanism and realizes the binocular fusion based on parallax information. To better aggregate binocular features from different levels, a two-path feature aggregation structure, which simulates the bottom-up and top-down mechanism in human visual system (HVS), is proposed. It not only realizes the supplement of low-level detail information to high-level semantic in the bottom-up path, but also realizes the supplement of high-level semantic information to low-level detail in the top-down path. Simultaneously, because feature misalignment exists in binocular features of adjacent levels, a feature alignment module (FAM) based on deformable convolution is designed to integrate the binocular fusion features of adjacent levels. In addition, considering the importance role of parallax in guiding binocular fusion, a binocular fusion module (BFM) based on parallax attention mechanism, which is different with existing binocular fusion methods, is explicitly proposed to achieve the binocular fusion between the left and right view features. Extensive experiments are conducted on LIVE I, LIVE II, WIVC I and WIVC II databases to demonstrate the effectiveness of the proposed method.

Toward Pixel-Level Precision for Binary Super-Resolution With Mixed Binary Representation.

Binary neural network (BNN) is an effective method for reducing model computational and memory cost, which has achieved much progress in the super-resolution (SR) field. However, there is still a noticeable performance gap between a binary SR network and its full-precision counterpart. Considering that the information density in quantization features is far lower than full-precision features, we aim to improve the precision of quantization features to produce rich-enough output activations for SR task. First, we make several observations that a multibit value could be approximated by multiple 1-bit values, and the computation power of binary convolution could be improved by approximating the multibit convolution process. Then, we propose a mixed binary representation set to approximate multibit activations, which is effective in compensating the quantization precision loss. Finally, we present a new precision-driven binary convolution (PDBC) module, which increases the convolution precision and protects image detail information without extra computation. Compared with normal binary convolution, our method could largely reduce the information loss caused by binarization. In experiments, our methods consistently show superior performance over the baseline models and can surpass state-of-the-art methods in terms of peak signal to noise ratio (PSNR) and visual quality.

Study the Diversity of Nocturnal Moths in Agricultural Field of Western Maharashtra Region

Abstract: Study of moths is important as they are significant part of the ecosystem. A study on macro-moths was conducted at agricultural field of Western Maharashtra region from month of June 2022 to May 2023. The Main aim of study to acquire the detail information of moths found in agricultural filed of District Satara and Pune. During the study period, a total 2012 specimen’s moths were observed. In addition, the number of families recorded from agricultural field of Western Maharashtra is also high, 20 families and 57 species occurred in desired location. Agricultural field of Western Maharashtra shows moths diversity. So, moth diversity was studied from Western Maharashtra from two sites including Phaltan and Baramati taluka respectively. These two sites have a few species in common. In general, Arctiidae, Noctuidae, Pyralidae and Sphingidae dominated both the sites. The Moths were collected by using mercury light traps (160 W). The moth diversity has been studied for the first time from Agricultural field of Western Maharashtra region.

An efficient feature pyramid attention network for person re-identification

For person re-identification, occlusion, appearance similarity and background clutter have always been challenges. In order to effectively address the challenges, we propose an efficient feature pyramid attention network (FPA-Net), which combines visual features from different levels to focus on both detail features and information. Specifically, we embed a pair of attention mechanisms that complement each other in the backbone network to focus on the discriminant features of person areas. In addition, we designed a novel feature pyramid structure, which propagates the feature information from the cross-level through the top feature to the bottom feature and from the bottom feature to the top feature to supplement the detail information of the feature. Finally, we integrate features form different scales through a lightweight transition block to generate more discriminant features. Our method performed experimental analysis on four datasets: Market-1501, DukeMTMC-ReID, CUHK03(Detected) and MSMT17. A large number of experimental results prove that the performance of the method is significantly better than the existing state-of-the-art methods.

Mask wearing detection algorithm based on improved Yolov7

Manual inspection of the mask is too time-consuming and laborious. In order to detect whether a mask is worn in a crowded public place, a mask-wearing detection method based on improved YOLOV7 is proposed, which uses Depth wise separable convolution instead of conventional convolution, in order to integrate the local feature information and the whole image information deeply, Dilated Convolution was used to improve the Pyramid Pooling Module (DC-PPM) , at last, the loss function of target location is optimized, which makes it not only have the ability of feature extraction to fuse the whole and local information, but also have the ability of not losing the detail information. The experimental results show that the detection accuracy and speed of the algorithm are 95.07% and 79 frames/s respectively, which are 3.4% and 14 frames/s higher than the original YOLOV7 algorithm, very good to meet the actual application needs.

CN-BSRIQA: Cascaded network - blind super-resolution image quality assessment

High resolution (HR) images consist of higher quality and more detail information in comparison to low-resolution images. But obtaining HR images entails higher costs and requires a larger workforce. The solution is using super-resolution (SR) images. Single image SR is the process of creating HR images from a single low-resolution image. Obtaining SR images from low-resolution images is a well-known problem in the domains of computer vision and image processing. Advancement in technology has given rise to many SR algorithms. The perceptual quality assessment of super-resolved images can be used to benchmark the techniques employed for image SR. In this work, a cascaded network-blind super-resolution image quality assessment (CN-BSRIQA) methodology is proposed. The proposed approach works under the cascaded architecture where a convolutional neural network (CNN) is cascaded with a deep belief network (DBN). CNN is employed as a shallow network in the proposed methodology to extract low-level information after partitioning the input image into patches. To assess the visual quality of the SR images, the features retrieved from CNN are incorporated into a DBN. Three databases are used to evaluate the performance of proposed CN-BSRIQA i.e., SR Quality Database (SRQD), SR Image Quality Database (SRID), and visual quality evaluation for super-resolved images (QADS). When compared to other state-of-the-art methodologies for assessing the visual quality of SR images, CN-BSRIQA outperforms them. For the perceptual quality assessment of SR images, the experimental results reveal that CNN-based techniques outperform techniques based on hand-crafted features. Furthermore, the shallow CNN proposed in the CN-BSRIQA can extract features that are content-independent i.e., they show better performance over cross-database evaluation in comparison to existing state-of-the-art techniques.

Diverter transformer-based multi-encoder-multi-decoder network model for medical retinal blood vessel image segmentation

The retinal blood vessel is an essential part of the fundus structure. It is important to accurately analyze the structure and distribution of retinal vessels, which can help make accurate medical diagnoses. However, it is still challenging to extract detailed information due to the problems of fuzzy edges, low resolution, and lots of noise in retinal blood vessel medical images. To extract the image detail information effectively, we propose a new diverter transformer-based multi-encoder-multi-decoder network model in this paper. The network model consists of a feature encoder module and a feature decoder module. Among them, the feature encoding module consists of a diverter transformer with a diverter adaptive mechanism, three encoder units with a convolution layer and max-pooling layer, and the two decoder units in the feature decoding module consist of an inverse convolution layer and an up-sampling layer, respectively. The Local Context Module (LCNet Module) in the feature encoding module learns richer local context feature information layer by layer through changing the width of the network while downsampling; the Global Encoder Module1 (G-Encoder Module1) and the Global Encoder Module2 (G-Encoder Module2) extract the global feature representation of retinal blood vessel images by performing a max-pooling operation to transform the input data into a vector of fixed dimensions, thus helping the network model to better understand and extract the global feature representation of retinal blood vessel images. The two decoder units in the feature decoding module receive local and global feature information from three encoder units, LCNet Module, G-Encoder Module1 and G-Encoder Module2, respectively. Decoder Module1 generates segmentation prediction by layer-by-layer up-sampling operation, and Decoder Module2 recovers the feature information by downsampling and decoding operations and fuses the recovered feature information to output, obtaining the final segmentation of the retinal blood vessels. The proposed diverter transformer-based multi-encoder-multi-decoder network model is validated on the DRIVE and STARE datasets with other classical and state-of-the-art network models, and its segmentation accuracy is 97.25% and 97.93%, respectively. Compared with the classical U-Net model, the improvement is 2.24% and 1.42%, respectively. Compared with the state-of-the-art SPNet model, the accuracy is increased by 0.61% on DRIVE and 1.01% on STARE. It indicates that the network model proposed in this paper has a significant competitive advantage in improving the segmentation performance of retinal blood vessel images.

Underwater Degraded Image Restoration by Joint Evaluation and Polarization Partition Fusion

Images of underwater environments suffer from contrast degradation, reduced clarity, and information attenuation. The traditional method is the global estimate of polarization. However, targets in water often have complex polarization properties. For low polarization regions, since the polarization is similar to the polarization of background, it is difficult to distinguish between target and non-targeted regions when using traditional methods. Therefore, this paper proposes a joint evaluation and partition fusion method. First, we use histogram stretching methods for preprocessing two polarized orthogonal images, which increases the image contrast and enhances the image detail information. Then, the target is partitioned according to the values of each pixel point of the polarization image, and the low and high polarization target regions are extracted based on polarization values. To address the practical problem, the low polarization region is recovered using the polarization difference method, and the high polarization region is recovered using the joint estimation of multiple optimization metrics. Finally, the low polarization and the high polarization regions are fused. Subjectively, the experimental results as a whole have been fully restored, and the information has been retained completely. Our method can fully recover the low polarization region, effectively remove the scattering effect and increase an image’s contrast. Objectively, the results of the experimental evaluation indexes, EME, Entropy, and Contrast, show that our method performs significantly better than the other methods, which confirms the feasibility of this paper’s algorithm for application in specific underwater scenarios.

UPFormer: U-sharped Perception lightweight Transformer for segmentation of field grape leaf diseases

In the smart agriculture community, segmentation models are de-facto for the timely detection and identification of plant diseases. However, the complex background and the small diseases make it challenging to segment the grape leaf disease. The performance improvement trend of the existing models comes at the cost of model size and computational cost, which hinders deployment on resource-constrained hardware. Towards this end, we propose a tailored lightweight segmentation architecture referred to as the U-sharped Perception Transformer (UPFormer) for field grape leaf diseases, which achieves a better trade-off between performance and efficiency. Concretely, we leverage the U-shaped hierarchy to acquire small tokens with superior cost efficiency. The prototype perception and the pixel-aware broadcast are developed in parallel architectures to learn low-frequency global information and mine high-frequency local information for small diseases in complex environments. Besides, a fast token aggregation recipe is designed to compensate for the sacrificed detail information in an efficient aggregation paradigm without increasing the count of parameters. Extensive experiments have demonstrated that UPFormer remarkably outperforms existing CNNs, ViTs, and CNN-Transformer hybrid architectures on the datasets, including Field-PV, Syn-PV, and Plant Village.

FSKT‐GE: Feature maps similarity knowledge transfer for low‐resolution gaze estimation

AbstractThe limited of texture details information in low‐resolution facial or eye images presents a challenge for gaze estimation. To address this, FSKT‐GE (feature maps similarity knowledge transfer for low‐resolution gaze estimation) is proposed, a gaze estimation framework consisting of both a high resolution (HR) network and low resolution (LR) network with the identical structure. Rather than mere feature imitation, this issue is addressed by assessing the cosine similarity of feature layers, emphasizing the distribution similarity between the HR and LR networks. This enables the LR network to acquire richer knowledge. This framework utilizes a combination loss function, incorporating cosine similarity measurement, soft loss based on probability distribution difference and gaze direction output, along with a hard loss from the LR network output layer. This approach on low‐resolution datasets derived from Gaze360 and RT‐Gene datasets is validated, demonstrating excellent performance in low‐resolution gaze estimation. Evaluations on low‐resolution images obtained through 2×, 4×, and 8× down‐sampling are conducted on two datasets. On the Gaze360 dataset, the lowest mean angular errors of 10.97°, 11.22°, and 13.61° were achieved, while on the RT‐Gene dataset, the lowest mean angular errors of 6.73°, 6.83°, and 7.75° were obtained.

EAIS-Former: An efficient and accurate image segmentation method for fruit leaf diseases

Fruit leaf disease segmentation is an essential foundation for achieving accurate disease diagnosis and identification. However, shadows caused by folded leaves and serrations on leaves can lead to difficulty in extracting edge features, affecting the accuracy of leaf segmentation. In addition, the varying shapes and blurred boundaries of disease spots can further lead to poor segmentation performance of spots. To address the above problems, this work proposes a method called EAIS-Former by combining the advantages of global modeling of Transformer, local processing and positional coding of convolutional neural network (CNN) for accurate segmentation in fruit leaf disease images. Dual scale overlap (DSO) patch embedding is designed to effectively extract multi-scale disease features by dual paths to alleviate omission of lesions. Ultra large convolution (ULC) Transformer block is customized for performing positional encoding and global modeling to efficiently extract global and positional features of leaves and diseases. Skip convolutional local optimization (SCLO) module is proposed to optimize the local detail and edge information and improve the pixel classification ability of the model so that the segmentation results of leaves and spots can be finer and more tiny spots can be extracted. Double layer upsampling (DLU) decoder is built to efficiently fuse the detail information with the semantic information and output the accurate segmentation results of leaves and spots. The experimental results show that the proposed method reach 99.04%, 98.64%, 99.24%, 99.42%, 98.59% and 98.58% intersection over union (IoU) for leaf segmentation on apple rust, pomegranate cercospora spot, mango anthracnose, jamun fungal disease, apple alternaria blotch and apple gray spot datasets, respectively. The IoU of lesion segmentation achieve 94.47%, 94.54%, 83.83%, 86.60%, 89.59% and 88.76%, respectively. In contrast to DeepLabv3+, the accuracy of disease segmentation is raised by 5.25%, 5.15%, 5.55%, 7.64%, 7.04% and 9.35%, respectively. Compared with U-Net, the proposed method improves the accuracy of disease spot segmentation by 4.3%, 4.44%, 5.26%, 9.42%, 5.87% and 6.53% under the six fruit leaf test sets, respectively. In addition, total parameters and FLOPs of the proposed method are only 18.44% and 8.47% of U-Net, respectively. Therefore, this study can provide an efficient and accurate method for the task of fruit leaf disease spot segmentation, which provides a sufficient basis for the accurate analysis of fruit leaves and diseases.

ICEEMDAN/LOESS: An Improved Vibration-Signal Analysis Method for Marine Atomic Interferometric Gravimetry

The marine atomic interferometric gravimeter is a vital precision instrument for measuring marine geophysical information, which is widely used in mineral resources exploration, military applications, and missile launches. In practical measurements, vibration disturbance is an important factor that affects measurement accuracy. This paper proposes the combination of improved complete ensemble empirical mode decomposition with adaptive noise and locally weighted regression for vibration characterization of gravimeter vibration data. Firstly, the original signal is added into a pair of white noise for adaptive noise-complete ensemble empirical mode decomposition to obtain multiple intrinsic mode functions. The efficient IMF components and noise components are filtered out under the dual indicators of correlation coefficient and variance contribution ratio, and then the LOESS filtering method is used for noise reduction to obtain useful signal detail information; finally, the noise-containing components are reconstructed with the effective components after the noise-reduction process. The experimental results of both simulated and measured vibration signals show that the proposed method can effectively decompose the different high- and low-frequency bands contained in the vibration signal and remove the noise of the original signal.

Research on denoising and enhancing methods of medical images based on convolutional neural networks

SummaryIn the process of modern medical diagnosis, medical image‐assisted diagnosis plays a very important role. However, the process of medical image acquisition, will be affected by various types and degrees of noise, and there will be a certain probability of producing strip artifacts, which will interfere with the doctor's diagnosis, analysis, and treatment of diseases to a certain extent. However, the traditional medical image denoising method will cause problems such as image edge blurring and detail loss, and it is difficult to achieve the balance between noise removal and detail information retention. Therefore, denoising medical images and improving the accuracy of denoising as much as possible have very important scientific research significance and clinical application value. Based on this, this article proposes a medical image denoising method based on a double residual convolutional neural network and compares it with traditional medical images denoising methods such as K‐SVD, BM3D, and PNLM3. Experimental results show that the medical image denoising method based on the double residual convolutional neural network proposed in this article has excellent performance.

Radio News in the Slovak Republic in the 21st Century

The present scientific study focuses on the issue of radio news in the 21st century. The ambition is to evaluate, at the time of writing this study, the current situation concerning news coverage in the most listened radio entities in the Slovak Republic. In this context, the basic characteristics of a public radio broadcaster and a private radio entity are also described. The results focus on the current state of radio news and its diverse interpretation in selected radio entities in Slovakia based on quantitative content analysis. The study details information on the nature of broadcast news in our research sample of the main news programmes of a public and a private radio broadcaster in Slovakia. It also sets out research questions and hypotheses focusing on the representation of news with a domestic and foreign focus, as well as which foreign countries dominate the foreign news coverage, and the themes in the foreign news coverage. The study summarizes the results of the research, generalizes the data, and also formulates conclusions that define radio news in the 21st century. The research section also includes interviews with executives from two selected media entities to support the results of the quantitative content analysis.

A lightweight bladder tumor segmentation method based on attention mechanism.

In the endoscopic images of bladder, accurate segmentation of different grade bladder tumor from blurred boundary regions and highly variable shapes is of great significance for doctors' diagnosis and patients' later treatment. We propose a nested attentional feature fusion segmentation network (NAFF-Net) based on the encoder-decoder structure formed by the combination of weighted pyramid pooling module (WPPM) and nested attentional feature fusion (NAFF). Among them, WPPM applies the cascade of atrous convolution to enhance the overall perceptual field while introducing adaptive weights to optimize multi-scale feature extraction, NAFF integrates deep semantic information into shallow feature maps, effectively focusing on edge and detail information in bladder tumor images. Additionally, a weighted mixed loss function is constructed to alleviate the impact of imbalance between positive and negative sample distribution on segmentation accuracy. Experiments illustrate the proposed NAFF-Net achieves better segmentation results compared to other mainstream models, with a MIoU of 84.05%, MPrecision of 91.52%, MRecall of 90.81%, and F1-score of 91.16%, and also achieves good results on the public datasets Kvasir-SEG and CVC-ClinicDB. Compared to other models, NAFF-Net has a smaller number of parameters, which is a significant advantage in model deployment.

Avoiding food waste from restaurant tickets: a big data management tool

PurposeThis study aims to address the global food waste problem in restaurants by analyzing customer sales information provided by restaurant tickets to gain valuable insights into directing sales of perishable products and optimizing product purchases according to customer demand.Design/methodology/approachA system based on unsupervised machine learning (ML) data models was created to provide a simple and interpretable management tool. This system performs analysis based on two elements: first, it consolidates and visualizes mutual and nontrivial relationships between information features extracted from tickets using multicomponent analysis, bootstrap resampling and ML domain description. Second, it presents statistically relevant relationships in color-coded tables that provide food waste-related recommendations to restaurant managers.FindingsThe study identified relationships between products and customer sales in specific months. Other ticket elements have been related, such as products with days, hours or functional areas and products with products (cross-selling). Big data (BD) technology helped analyze restaurant tickets and obtain information on product sales behavior.Research limitations/implicationsThis study addresses food waste in restaurants using BD and unsupervised ML models. Despite limitations in ticket information and lack of product detail, it opens up research opportunities in relationship analysis, cross-selling, productivity and deep learning applications.Originality/valueThe value and originality of this work lie in the application of BD and unsupervised ML technologies to analyze restaurant tickets and obtain information on product sales behavior. Better sales projection can adjust product purchases to customer demand, reducing food waste and optimizing profits.