Nutthanon Leelathakul

Tomato is one of Thailand’s most significant economic crops, generating substantial export value and serving as a primary source of income for local farmers. However, the traditional manual grading process often fails to comply with the Thai Agricultural Standard TACFS 1503–2007, as grading decisions rely heavily on individual experience and subjective perception, resulting in inconsistent quality. Existing automated systems face the challenges of low accuracy, high costs, and complex hardware, while many are incompatible with Thailand’s grading standards. This study presents a multi-view tomato grading system (MVT-Grader), utilizing a dataset acquired from Doi Kham Food Products Co., Ltd. (Third Royal Factory, Tao Ngoi) under controlled lighting conditions. Subsequently, MVT-Grader is built on a custom-designed lightweight CNN architecture with an adjusted spatially aware loss function to enhance the model’s sensitivity in detecting subtle surface defects and color variations. The proposed model was trained using tomato images captured from two and three different viewpoints via a low-cost webcam setup and processed by a GPU-embedded system. Experiments conducted using stratified 5-fold cross-validation on a real-world industrial dataset demonstrate average grading accuracies of 99.43% (two-view) and 99.64% (three-view). Furthermore, the proposed Real-Time Lightweight CNN with Spatially Aware Loss Optimization achieves processing speeds of 87 ms and 114 ms per tomato for two- and three-view cases, respectively. Compared with MVCNN-Siamese, SDF-ConvNets, and Multi-View Spatial Network, the proposed system outperforms the others in both accuracy and speed, improving accuracy by 1.6–6.11% and reducing processing time by 39–49 ms.

Determining low-density polyethylene (LDPE) properties typically requires extensive laboratory testing, which is time-consuming and costly. For instance, the conditioning phase alone for measuring Vicat softening temperature requires a minimum of 40 hours [1]. Predictive modeling can reduce these costs. Ensuring accuracy that meets manufacturing standards, however, is challenging. This paper introduces TopSABoost, a hybrid method that combines topological sorting and boosting techniques to perform sequential predictions of LDPE properties and minimize the overall laboratory-testing cost. This approach reduces laboratory testing costs by predicting one property first and using it to predict another. The complexity analysis demonstrates that the proposed algorithm is ideal for non-real-time determination of sequential predictions, as it computes the model offline once for repeated use without requiring recalculations, aligning with manufacturing needs. The experimental results demonstrate that TopSABoost achieves a maximum error of just 0.11%, satisfying strict manufacturing constraints. TopSABoost identifies that prioritizing the prediction of L-value, followed by Density, offers the most cost-efficient sequence and significantly reduces reliance on direct laboratory testing while maintaining adherence to error thresholds.

Location-based recommender systems (LBRSs) have exhibited significant potential in providing personalized recommendations based on the user’s geographic location and contextual factors such as time, personal preference, and location categories. However, several challenges (such as data sparsity, the cold-start problem, and tedium problem) need to be addressed to develop more effective LBRSs. In this paper, we propose a novel POI recommendation system, called LACF-Rec3, which employs a hybrid approach of link analysis (HITS-3) and collaborative filtering (CF-3) based on three visiting behaviors: frequency, variety, and repetition. HITS-3 identifies distinctive POIs based on user- and POI-visit patterns, ranks them accordingly, and recommends them to cold-start users. For existing users, CF-3 utilizes collaborative filtering based on their previous check-in history and POI distinctive aspects. Our experimental results conducted on a Foursquare dataset demonstrate that LACF-Rec3 outperforms prior methods in terms of recommendation accuracy, ranking precision, and matching ratio. In addition, LACF-Rec3 effectively solves the challenges of data sparsity, the cold-start issue, and tedium problems for cold-start and existing users. These findings highlight the potential of LACF-Rec3 as a promising solution to the challenges encountered by LBRS.

Effective crossover methods are essential for genetic algorithms because they enhance population diversity, resulting in an increase in the search space to be explored and the mitigation of trapping at local optima. Ring-based crossover methods are designed to address the shortcomings of traditional crossover techniques; for example, the ends of binary-encoded chromosomes tend to remain unaltered, even when their fitness values are low. By conjoining the leftmost and rightmost parts of a chromosome to form a ring, the ring-based crossovers allow offspring bits to be inherited from parent bits at the front and rear positions. Such a ring-based technique helps increase population diversity while requiring a relatively low number of fitness evaluations. To the best of our knowledge, there has been no comparative study or comprehensive analysis of ring-based crossover techniques. In this paper, we study and compare the characteristics of various existing ring-based crossover techniques (i.e., circle-ring (CRC), annular (AC), ring (RC) and front-rear crossover (FRC)). Although they each have distinct characteristics, they share a common crossover concept: bits at different positions can be swapped. We propose a generalized ring-based crossover (GRC) as an umbrella of the ring-based crossovers, embodying all of their beneficial characteristics. Chromosome shifting, ring forming, chromosome exchange and offspring creation are integrated into the proposed model. The experimental results show that GRC outperforms the other ring-based crossover methods in all experiments, and these results are consistent with the results of behavioral analysis. In a trap problem, GRC outperformed the state-of-the-art BOA method and require 40-times fewer fitness evaluations. GRC tends to maintain a variety of candidates in populations and to preserve the building blocks of solution, and it requires 93% and 57% fewer fitness evaluations (on average) compared to traditional crossover and other ring-based crossover methods.

Mobile sinks (MSs) are capable of collecting data along specified paths in wireless sensor networks (WSNs). They are deployed as a popular alternative for data loggers, to which all nodes have to send sensory data. If MSs' paths (or cycles) are not well determined, it might take a relatively long time for the MSs to make a round trip. Recent research works have proposed methods to determine rendezvous points (RPs) that the MSs must pass by to collect data, with the aim of reducing the data-collection time. Determination of the number of RPs is important, and it is challenging to make ensure that there are sufficient RPs widely located throughout a sensor network, forming a circle along which the MSs can spend limited time traveling. This research presents a method for designing paths and pinpointing RPs for MSs to collect data, as well as determining the next hop to relay data for each sensor node. Instead of reducing the MSs' travel time, the focus of this research is to preserve the energy of all sensor nodes in WSNs. Our method determines the maximum number of RPs such that the MSs can run through each RP's communication range (within a time constraint) without depleting their own energy. The method comprises three main steps. First, we calculate the number of RPs and design the MS path. Second, the exact data-collection points are determined. The last step is to specify the path along which sensory data are relayed to the MS. In our experiments, we simulate two WSNs of different sizes. The results show that our method outperforms the others by 70%-80% in terms of the sensor node uptime, power consumption, MS traveling time and the number of RPs.