Fruit ripeness classification: A survey

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety. An important factor that determines fruit quality is its stage of ripening. This is usually manually classified by field experts, making it a labor-intensive and error-prone process. Thus, there is an arising need for automation in fruit ripeness classification. Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Furthermore, deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features, which are often crop-specific. In this survey, we review the latest methods proposed in the literature to automatize fruit ripeness classification, highlighting the most common feature descriptors they operate on

Hierarchical Text Classification: a review of current research

t is often the case that collections of documents are annotated with hierarchically-structured concepts. However, the benefits of this structure are rarely taken into account by commonly-used classification techniques. Conversely, Hierarchical Text Classification methods are devisedto take advantage of the labels’ organization to boost classification performance. With this work,we aim to deliver an updated overview of current research in this domain. We begin by definingthe task and framing it within the broader text classification area, examining important shared concepts such as text representation. Then, we dive into details regarding the specific task,providing a high-level description of its traditional approaches. We then summarize recentlyproposed methods, highlighting their main contributions. We additionally provide statisticsfor the most adopted datasets and describe the benefits of using evaluation metrics tailored to hierarchical settings. Finally, a selection of recent proposals is benchmarked against non-hierarchical baselines on five domain-specific datasets

Ticket Automation: an Insight into Current Research with Applications to Multi-level Classification Scenarios

odern service providers often have to deal with large amounts of customer requests, which they need to act upon in a swift and effective manner to ensure adequate support is provided. In this context, machine learning algorithms are fundamental in streamlining support ticket processing workflows. However, a large part of current approaches is still based on traditional Natural Language Processing approaches without fully exploiting the latest advancements in this field. In this work, we aim to provide an overview of support Ticket Automation, what recent proposals are being made in this field, and how well some of these methods can generalize to new scenarios and datasets. We list the most recent proposals for these tasks and examine in detail the ones related to Ticket Classification, the most prevalent of them. We analyze commonly utilized datasets and experiment on two of them, both characterized by a two-level hierarchy of labels, which are descriptive of the ticket’s topic at different levels of granularity. The first is a collection of 20,000 customer complaints, and the second comprises 35,000 issues crawled from a bug reporting website. Using this data, we focus on topically classifying tickets using a pre-trained BERT language model. The experimental section of this work has two objectives. First, we demonstrate the impact of different document representation strategies on classification performance. Secondly, we showcase an effective way to boost classification by injecting information from the hierarchical structure of the labels into the classifier. Our findings show that the choice of the embedding strategy for ticket embeddings considerably impacts classification metrics on our datasets: the best method improves by more than 28% in F1- score over the standard strategy. We also showcase the effectiveness of hierarchical information injection, which further improves the results. In the bugs dataset, one of our multi-level models (ML-BERT) outperforms the best baseline by up to 5.7% in F1-score and 5.4% in accuracy

A multi-level approach for hierarchical Ticket Classification

The automatic categorization of support tickets is a fundamental tool for modern businesses. Such requests are most commonly composed of concise textual descriptions that are noisy and filled with technical jargon. In this paper, we test the effectiveness of pre-trained LMs for the classification of issues related to software bugs. First, we test several strategies to produce single, ticket-wise representations starting from their BERT-generated word embeddings. Then, we showcase a simple yet effective way to build a multi-level classifier for the categorization of documents with two hierarchically dependent labels. We experiment on a public bugs dataset and compare our results with standard BERT-based and traditional SVM classifiers. Our findings suggest that both embedding strategies and hierarchical label dependencies considerably impact classification accuracy

Stop overkilling simple tasks with black-box models and use transparent models instead

In recent years, the employment of deep learning methods has led to several significant breakthroughs in artificial intelligence. Different from traditional machine learning models, deep learning-based approaches are able to extract features autonomously from raw data. This allows for bypassing the feature engineering process, which is generally considered to be both error-prone and tedious. Moreover, deep learning strategies often outperform traditional models in terms of accuracy.Comment: The experimental methodology is lacking. We plan to deeply revise the paper and submit a substantially different versio

A survey on text classification: Practical perspectives on the Italian language.

Text Classification methods have been improving at an unparalleled speed in the last decade thanks to the success brought about by deep learning. Historically, state-of-the-art approaches have been developed for and benchmarked against English datasets, while other languages have had to catch up and deal with inevitable linguistic challenges. This paper offers a survey with practical and linguistic connotations, showcasing the complications and challenges tied to the application of modern Text Classification algorithms to languages other than English. We engage this subject from the perspective of the Italian language, and we discuss in detail issues related to the scarcity of task-specific datasets, as well as the issues posed by the computational expensiveness of modern approaches. We substantiate this by providing an extensively researched list of available datasets in Italian, comparing it with a similarly sought list for French, which we use for comparison. In order to simulate a real-world practical scenario, we apply a number of representative methods to custom-tailored multilabel classification datasets in Italian, French, and English. We conclude by discussing results, future challenges, and research directions from a linguistically inclusive perspective

LncRNAs Associated with Neuronal Development and Oncogenesis Are Deregulated in SOD1-G93A Murine Model of Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease caused in 10% of cases by inherited mutations considered “familial”. An ever-increasing amount of evidence is showing a fundamental role for RNA metabolism in ALS pathogenesis, and long non-coding RNAs (lncRNAs) appear to play a role in ALS development. Here, we aim to investigate the expression of a panel of lncRNAs (linc-Enc1, linc–Brn1a, linc–Brn1b, linc-p21, Hottip, Tug1, Eldrr, and Fendrr) which could be implicated in early phases of ALS. Via Real-Time PCR, we assessed their expression in a murine familial model of ALS (SOD1-G93A mouse) in brain and spinal cord areas of SOD1-G93A mice in comparison with that of B6.SJL control mice, in asymptomatic (week 8) and late-stage disease (week 18). We highlighted a specific area and pathogenetic-stage deregulation in each lncRNA, with linc-p21 being deregulated in all analyzed tissues. Moreover, we analyzed the expression of their human homologues in SH-SY5Y-SOD1-WT and SH-SY5Y-SOD1-G93A, observing a profound alteration in their expression. Interestingly, the lncRNAs expression in our ALS models often resulted opposite to that observed for the lncRNAs in cancer. These evidences suggest that lncRNAs could be novel disease-modifying agents, biomarkers, or pathways affected by ALS neurodegeneration

Tailored resections in oral and oropharyngeal cancer using narrow band imaging

In a previous pilot study we observed that intra-operative narrow-band imaging (NBI) helps achieve clear superficial resection margins. The aim of this study was to verify if the use of intra-operative NBI can help to obtain tailored resections and if it is influenced by the lesion site, aspects not investigated in our previous study. MATERIALS AND METHODS: The resection margins of 39 oral and 22 oropharyngeal squamous cell carcinomas were first set at 1.5cm from the macroscopic lesion boundary (white light, WL, tattoo). Then, the superficial tumor extension was more precisely defined with NBI, giving rise to three possible situations: NBI tattoo larger than the WL tattoo, NBI tattoo coinciding with the WL tattoo, or NBI tattoo smaller than the WL tattoo. For each of these situations the space comprised between the NBI and WL tattoos was defined "NBI positive", "NBI null", and "NBI negative", respectively. Resections were performed following the outer tattoo. The number of clear superficial resection margins, and the pathological response on the "NBI-positive" and the "NBI-negative" areas were recorded. RESULTS: We obtained 80.3% negative superficial resection margins. NBI provided a more precise definition of superficial tumor extension in 43 patients. Sensitivity, specificity, positive and negative predictive values were 94.4%, 64%, 79.1% and 88.9%, respectively; a test of proportions demonstrated they were not influenced by tumor site. CONCLUSIONS: NBI could allow for real-time definition of superficial tumor extension with possible tailored resections and fewer positive superficial resection margins; it is not influenced by tumor site