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In recent years, deep convolutional networks (DCNN) have gained popularity for different classification (or recognition) tasks. In this paper, three well known DCNN structures were used, i.e., AlexNet, SqueezeNet and GoogLeNet, and their classification performances in recognizing handwritten Bangla isolated characters were compared. These networks have simpler structures compared to the recent DCNNs. Experiments on a standard Bangla database revealed that the overall performance of GoogLeNet is slightly better than the other two networks. Further analysis using saliency maps of the test samples revealed the important features that are learned by the networks for classifying characters. This information led us to understand why classification of some samples fail and how to rectify these. [ABSTRACT FROM AUTHOR]

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen–Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model's decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies. [ABSTRACT FROM AUTHOR]

Recently, Panama wilt disease that attacks banana leaves has caused enormous economic losses to farmers. Early detection of this disease and necessary preventive measures can avoid economic damage. This paper proposes an improved method to predict Panama wilt disease based on symptoms using an agro deep learning algorithm. The proposed deep learning model for detecting Panama wilts disease is essential because it can help accurately identify infected plants in a timely manner. It can be instrumental in large-scale agricultural operations where Panama wilts disease could spread quickly and cause significant crop loss. Additionally, deep learning models can be used to monitor the effectiveness of treatments and help farmers make informed decisions about how to manage the disease best. This method is designed to predict the severity of the disease and its consequences based on the arrangement of color and shape changes in banana leaves. The present proposed method is compared with its previous methods, and it achieved 91.56% accuracy, 91.61% precision, 88.56% recall and 81.56% F1-score. [ABSTRACT FROM AUTHOR]

This paper discusses the methods the course teacher used to teach the Information Processing and Retrieval course [Course Code: 19LISC302]. A few aspects, including Chain Indexing, KWIC, Boolean operators, queries, logical relations, and recall & precision, have been highlighted here for illustration. The course teacher was able to improve the students' knowledge of the mentioned areas by using teaching methods based on the Revised Bloom's Taxonomy. The methods also made it possible to follow the students' progress. Boolean operators, queries, logical relations, and recall & precision exercises are taken into consideration for the discussions here, but chain indexing and KWIC related classroom practice formats are only supplied at Annexure-i. The course teacher created the application that is connected to logical relations (Truth Table), and the students have used it in class to practice. Various multifaceted key phrases, a few queries, the Boolean operators AND, OR, and NOT, and the Truth Table are all included in the aforementioned programme. For students to successfully obtain pertinent data from any database, they must have a solid understanding of logical ideas. Meaningful results won't be produced by executing the queries and operators automatically or blindly. Additionally, it could need extra time from information seekers. Boolean operators are used in information retrieval systems to evaluate queries. The Boolean operators are typically linked to the classes in the queries. The evaluation for each class is either True or False. To effectively use the operators in search queries and extract the necessary data, learners need be familiar with the Truth Table-related concepts. In addition, for the aforementioned subjects other than Chain Indexing and KWIC, the Revised Bloom's Taxonomy based teaching and the students' evaluation techniques have also been presented here. Wherever needed, enough screenshots of the aforementioned programme are produced. Students used a learning approach based on Revised Bloom's Taxonomy to study the concepts linked to the aforementioned themes, and they have been duly documented below. With the use of the methodologies utilised in this study, the teacher was able to glean the essential information necessary for determining the pupils' degrees of learning ability and applying that information to the necessary charts. The charts show the students' competence levels according to themes, making it simple to see the students' skill status. Realizing their ability levels is aided by each level wise clearance and their overall clearance status. Chain indexing, KWIC, logical relations, queries with Boolean operators, database sample records format, RBT levels-based evaluation format, recall, and precision are the examples of exercise forms shown in Annexure-i. Further, overall progress charts, each level wise progress charts and Student performance evaluation report are given at Annexure-ii, Annexure-iii and Annexure-iv respectively. [ABSTRACT FROM AUTHOR]

Objective: This study compared the recall and precision of MeSH-term versus text-word searching to better understand psychosocial MeSH terms and to provide guidance on whether to include both strategies in an information literacy session or how much time should be spent on teaching each search strategy. Methods: Using the relevant recall method, a total of 3,162 resources were considered and evaluated to form a gold standard set of 1,521 relevant resources. We compared resources discussing psychosocial aspects of children and adolescents living with type 1 diabetes using two search strategies: text-word strategy versus MeSH-term strategy. The frequency of MeSH terms, the MeSH hierarchy, and elements of each search strategy were also examined. Results: Using the 1,521 relevant articles, we found that the text-word search strategy had 54% recall, while the MeSHterm strategy had 75% recall. Also, the precision of the text-word strategy was 34.4%, while the precision of the MeSHterm strategy was 47.7%. Therefore, the MeSH-term search strategy yielded both greater recall and greater precision. The MeSH strategy was also more complicated in design and usage than the text-word strategy. Conclusions: This study demonstrates the effectiveness of text-word and MeSH search strategies on precision and recall. The combination of text-word and MeSH strategies is recommended to achieve the most comprehensive results. These results support the idea that MeSH or a similar controlled vocabulary should be taught to experienced and knowledgeable students and practitioners who require a myriad of resources for their literature searches. [ABSTRACT FROM AUTHOR]

Introduction: The effective performance of an Information Retrieval System depends on the quality with which the organization of information is performed, which will imply a retrieval of the most relevant and pertinent information, since these procedures are conditioned to each other, creating a bridge between input and output of information. Objective: To evaluate the impact of the exhaustiveness and specificity variables and the recall and precision measures, as well as the concepts of relevance and pertinence, in Information Retrieval Systems. Methodology: It is characterized as a descriptive and exploratory study, based on a narrative literature review aiming to present the different concepts, their converging and divergent points. Results: As a contribution, we present a proposal for a flow for an Information Storage and Retrieval System, centered on the user, bringing together several aspects related to measures of recall and precision, of relevance and pertinence. Conclusion: It is considered as the final contribution of this study to highlight the importance of a systemic view, in which all elements of an Information Storage and Retrieval System are in interrelation, having the user as the main element; and present the fundamental activities that are important for the training of professionals able to build consistent Systems. [ABSTRACT FROM AUTHOR]

During a search, phrase-terms expressed in queries are presented to an information retrieval system (IRS) to find documents relevant to a topic. The IRS makes relevance judgements by attempting to match vocabulary in queries to documents. If there is a mismatch, the problem of vocabulary mismatch occurs. The aim is to examine ways of searching for documents more effectively, in order to minimise mismatches. A further aim is to understand the mechanisms of, and the differences between, human and machine-assisted, retrieval. The objective of this study was to determine whether IRS-H (an IRS using the hybrid indexing method) and human participants agree or disagree on relevancy judgments, and whether the problem of mismatching vocabulary can be solved. A collection of eighty research documents and sixty-five phrase-terms were presented to (i) IRS-H and four participants in Test 1, and (ii) IRS-H and one participant (aided by search software) in Test 2. Statistical analysis was performed using the Kappa coefficient. IRS-H and the four participants' judgements disagreed. IRS-H and the participant aided by search software judgments did agree. IRS-H solves the problem of mismatching vocabulary between a query and a document. [ABSTRACT FROM AUTHOR]

Although process model discovery has been largely investigated over the past two decades, existing process discovery methods are not yet considered fully satisfactory. A particular issue is the difficulty of discovering process models with a good balance among recall (or fitness), precision, generalization, and simplicity, notably for real-world event logs. This article revisits the genetic algorithms-based X-Processes process discovery method, discussing the results of using a fitness function calculated through a harmonic mean that considers the four main metrics of process model quality used in process mining — recall, precision, generalization and simplicity. Although genetic algorithms have been used to discover process models, such methods have limitations, as do other non-genetic algorithm-based methods. X-Processes was tested with two harmonic mean options — one based on recall and precision only and another based on the four quality metrics. X-Processes showed superior effectiveness when compared to state-of-the-art process discovery methods, including one also based on genetic algorithms. The experimental results obtained for both harmonic mean options, using 12 real-world event logs, show that the overall quality of all process models discovered by X-Processes is superior to that of the baseline methods. While its execution time is longer than the other compared process discovery methods, X-Processes emerges as a solution when the need for a higher quality process model outweighs the hunger for agility. [ABSTRACT FROM AUTHOR]

Process mining studies ways to improve real-world processes using historical event data generated by IT systems that support business processes of organisations. Given an event log of an IT system, process discovery algorithms construct a process model representing the processes recorded in the log, while conformance checking techniques quantify how well the discovered model achieves this objective. State-of-the-art discovery and conformance techniques either completely ignore or consider but hide from the users information about the likelihood of process behaviour. That is, the vast majority of the existing process discovery algorithms construct non-stochastic aware process models. Consequently, few conformance checking techniques can assess how well such discovered models describe the relative likelihoods of traces recorded in the log or how well they represent the likelihood of future traces generated by the same system. Note that this is necessary to support process simulation, prediction and recommendation. Furthermore, stochastic information can provide business analysts with further actionable insights on frequent and rare conformance issues. This article presents precision and recall measures based on the notion of entropy of stochastic automata, which are capable of quantifying and, hence, differentiating, between frequent and rare deviations of an event log and a process model that is enriched with the information on the relative likelihoods of traces it describes. An evaluation over several real-world datasets that uses our open-source implementation of the measures demonstrates the feasibility of using our precision and recall measures in industrial settings. Finally, we propose a range of intuitive desired properties that stochastic precision and recall measures should possess, and study our and other existing stochastic-aware conformance measures with respect to these properties. • It matters how often behaviour occurs in processes, e.g. for performance, simulation, prediction, etc. • Often, this stochastic perspective is not considered by conformance checking techniques. • In this paper, we introduce two sets of stochastic recall+precision measures. • The techniques are practically feasible, though are limited to deterministic stochastic behaviour. • 14 properties for stochastic conformance measures are proposed. • We analyse existing measures using these properties. [ABSTRACT FROM AUTHOR]