The identification and categorization of parasitic eggs are crucial for diagnosing parasitic infections, which pose a significant threat to global health. These methods also require specialized knowledge and can be expensive. In contrast, recent progress in artificial intelligence, particularly deep learning, has revolutionized this field by automating detection processes with high levels of accuracy and precision, reducing the need for specialized knowledge, and improving diagnostic speed. Transfer learning methods using convolutional neural networks (CNNs)-based models like AlexNet, ResNet, VGG16 and EfficientNet-B4 have yielded promising outcomes. Likewise, cutting-edge object detection models, such as the YOLO series (YOLOv5, YOLOv7, YOLOv8), Faster R-CNN, and TOOD have significantly enhanced detection efficiency. Moreover, innovative architectures like YAC-Net, which incorporate algorithmic modifications, have shown superior performance compared to traditional models like YOLO in addressing domain-specific challenges. Advanced models such as Vision Transformers, Cascade Mask R-CNN, Swin Transformers, and the DETR framework have demonstrated remarkable potential in object detection and classification tasks. DenseNet121, with its efficient feature extraction capabilities, and CoAtNet, a hybrid model leveraging the strengths of ConvNets, Vision Transformers and Ensemble learning have further enriched the field. This study examines these advancements, assesses their performance across key metrics, and explores their applicability in real-world clinical environments, offering insights into current limitations and future directions for improving parasitic egg detection and classification.

A geo-referenced time series database represents the data generated by a set of fixed locations (or spatial items) observing a particular phenomenon over time. This data hides valuable information that can help users progress in their social and economic lives. This paper presents a new model of Geo-referenced Periodic-Frequent Patterns (GPFPs) that might be in these databases. A GPFP is a set of frequently occurring items close to each other and seen in a database at regular intervals. Three constraints have been used to figure out how interesting a pattern is in a geo-referenced time series database: maximum distance (maxDist), minimum support (minSup), and maximum periodicity (maxPer). The maxDist controls how far apart the items in a pattern can be. The minimum number of times a pattern must appear in the data is controlled by the minSup. Lastly, the maxPer variable specifies how many times a pattern must repeat before it is considered periodic in the data. Each pattern that satisfies these three requirements will be returned. An effective method known as the Geo-referenced Periodic-Frequent Pattern-Miner (GPFP-Miner) has been proposed to discover all GPFPs included inside a geo-referenced time series database. GPFP-Miner uses an innovative, smart depth-first search approach to uncover required patterns efficiently. The findings of the experiments support the contention that the proposed algorithm is effective. In addition, we present two case studies in which we utilise our methodology to extract meaningful information from databases pertaining to air pollution and traffic congestion.

A geo-referenced time series database represents the data generated by a set of fixed locations (or items) observing a particular phenomenon over time. Useful information that can facilitate the users to achieve socio-economic development lies hidden in this data. This paper introduces a novel model of Fuzzy Geo-referenced Periodic-Frequent Patterns (FGPFPs) that may exist in these databases. An FGPFP represents a set of frequently occurring neighboring items observed at regular intervals in a database. For example, an FGPFP in a traffic congestion database represents a set of neighboring road segments where people have regularly faced congestion problems. A novel pruning technique has been presented to effectively reduce the search space and the computational cost of finding the desired patterns. We have also proposed an efficient depth-first search algorithm to find all the desired patterns. Experimental results demonstrate that the proposed algorithm is efficient. Finally, we demonstrate our model’s usefulness by performing traffic congestion analytics.

The development and operation of Online Judge System (OJS), which is used to evaluate the correctness of programs, is a nontrivial and difficult task due to the various functional and non-functional requirements. However, although many OJSs have been developed and operated, and their usefulness reported, the theory for constructing OJSs has not been sufficiently discussed. In this paper, we present the functional and nonfunctional requirements oriented to OJS as well as demonstrate the internal components and software architecture of an OJS, which has been in operation for over a decade and has evaluated over six million solutions. We also present real-world experiences and challenges encountered during this long journey of our OJS.

Time series classification is an important model in data mining. It involves assigning a class label to a test instance based on the training data with known class labels. Most previous studies developed time series classifiers by disregarding the fuzzy nature of events (i.e., events with similar values may belong to different classes) within the data. Consequently, these studies suffered from performance issues, including decreased accuracy and increased memory, runtime, and energy requirements. With this motivation, this paper proposes a novel fuzzy nearest neighbor classifier for time series data. The basic idea of our classifier is to transform the very large training data into a relatively small representative training data and use it to label a test instance by employing a new fuzzy distance measure known as Ravi. Experimental results on real world benchmark datasets demonstrate that the proposed classifier outperforms the current parameter-free time series classifiers and also the popular deep learning techniques.

Periodic-frequent pattern mining (PFPM) is an important data mining model having many real-world applications. However, this model's prosperous industrial use has been hindered by the problem of combinatorial explosion of patterns, which is the generation of too many redundant patterns, most of which may be useless to the user. We propose a novel model of closed periodic-frequent patterns that may exist in a temporal database to address this problem. Closed periodic-frequent patterns represent a concise lossless subset that uniquely preserves the complete information of all periodic-frequent patterns in a database. An efficient depth-first search algorithm, called Closed Periodic-Frequent Pattern Miner (CPFP-Miner), has been introduced to find all the database's desired patterns. Experimental results demonstrate that CPFP-Miner is not only memory, runtime, and energy-efficient, but also highly scalable. The usefulness of our model has also been shown with a case study on traffic congestion analytics.

Periodic-frequent pattern mining is a challenging problem of great importance in many applications. Most previous works focused on finding these patterns in binary temporal databases and did not take into account the quantities of items within the data. This paper proposes a novel model of fuzzy periodic-frequent pattern (FPFP) that may exist in a quantitative temporal database (QTD). Finding FPFPs in QTD is a non-trivial and challenging task due to its huge search space. A novel pruning technique, called improved maximum scalar cardinality, has been introduced to effectively reduce the search space and the computational cost of finding the desired itemsets. This technique facilitates the mining of FPFPs in real-world very large databases practicable. An efficient algorithm has also been presented to find all FPFPs in a QTD. Experimental results demonstrate that the proposed algorithm is efficient. We also present a case study in which we apply our model to find useful information in air pollution database.

Classification is one of the most popular techniques in the data mining area. In supervised learning, a new pattern is assigned a class label based on a training set whose class labels are already known. This paper proposes a novel classification algorithm for time series data. In our algorithm, we use four parameters and based on their significance on different benchmark datasets, we have assigned the weights using simulated annealing process. We have taken the combination of these parameters as a performance metric to find the accuracy and time complexity. We have experimented with 6 benchmark datasets and results shows that our novel algorithm is computationally fast and accurate in several cases when compared with 1NN classifier.

The problem of classification of time series data is an interesting problem in the field of data mining. Even though several algorithms have been proposed for the problem of time series classification we have developed an innovative algorithm which is computationally fast and accurate in several cases when compared with 1NN classifier. In our method we are calculating the fuzzy membership of each test pattern to be classified to each class. We have experimented with 6 benchmark datasets and compared our method with 1NN classifier.