Academic literature on the topic 'Term frequency weighting'

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Journal articles on the topic "Term frequency weighting"

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Mohammed, Mohannad T., and Omar Fitian Rashid. "Document retrieval using term term frequency inverse sentence frequency weighting scheme." Indonesian Journal of Electrical Engineering and Computer Science 31, no. 3 (2023): 1478. http://dx.doi.org/10.11591/ijeecs.v31.i3.pp1478-1485.

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The need for an efficient method to find the furthermost appropriate document corresponding to a particular search query has become crucial due to the exponential development in the number of papers that are now readily available to us on the web. The vector space model (VSM) a perfect model used in “information retrieval”, represents these words as a vector in space and gives them weights via a popular weighting method known as term frequency inverse document frequency (TF-IDF). In this research, work has been proposed to retrieve the most relevant document focused on representing documents and queries as vectors comprising average term term frequency inverse sentence frequency (TF-ISF) weights instead of representing them as vectors of term TF-IDF weight and two basic and effective similarity measures: Cosine and Jaccard were used. Using the MS MARCO dataset, this article analyzes and assesses the retrieval effectiveness of the TF-ISF weighting scheme. The result shows that the TF-ISF model with the Cosine similarity measure retrieves more relevant documents. The model was evaluated against the conventional TF-ISF technique and shows that it performs significantly better on MS MARCO data (Microsoft-curated data of Bing queries).
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Mohannad, T. Mohammed, and Fitian Rashid Omar. "Document retrieval using term frequency inverse sentence frequency weighting scheme." Document retrieval using term frequency inverse sentence frequency weighting scheme 31, no. 3 (2023): 1478–85. https://doi.org/10.11591/ijeecs.v31.i3.pp1478-1485.

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The need for an efficient method to find the furthermost appropriate document corresponding to a particular search query has become crucial due to the exponential development in the number of papers that are now readily available to us on the web. The vector space model (VSM) a perfect model used in “information retrieval”, represents these words as a vector in space and gives them weights via a popular weighting method known as term frequency inverse document frequency (TF-IDF). In this research, work has been proposed to retrieve the most relevant document focused on representing documents and queries as vectors comprising average term term frequency inverse sentence frequency (TF-ISF) weights instead of representing them as vectors of term TF-IDF weight and two basic and effective similarity measures: Cosine and Jaccard were used. Using the MS MARCO dataset, this article analyzes and assesses the retrieval effectiveness of the TF-ISF weighting scheme. The result shows that the TF-ISF model with the Cosine similarity measure retrieves more relevant documents. The model was evaluated against the conventional TF-ISF technique and shows that it performs significantly better on MS MARCO data (Microsoft-curated data of Bing queries).
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Nugroho, Kuncahyo Setyo, Fitra A. Bachtiar, and Wayan Firdaus Mahmudy. "Detecting Emotion in Indonesian Tweets: A Term-Weighting Scheme Study." Journal of Information Systems Engineering and Business Intelligence 8, no. 1 (2022): 61–70. http://dx.doi.org/10.20473/jisebi.8.1.61-70.

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Background: Term-weighting plays a key role in detecting emotion in texts. Studies in term-weighting schemes aim to improve short text classification by distinguishing terms accurately. Objective: This study aims to formulate the best term-weighting schemes and discover the relationship between n-gram combinations and different classification algorithms in detecting emotion in Twitter texts. Methods: The data used was the Indonesian Twitter Emotion Dataset, with features generated through different n-gram combinations. Two approaches assign weights to the features. Tests were carried out using ten-fold cross-validation on three classification algorithms. The performance of the model was measured using accuracy and F1 score. Results: The term-weighting schemes with the highest performance are Term Frequency-Inverse Category Frequency (TF-ICF) and Term Frequency-Relevance Frequency (TF-RF). The scheme with a supervised approach performed better than the unsupervised one. However, we did not find a consistent advantage as some of the experiments found that Term Frequency-Inverse Document Frequency (TF-IDF) also performed exceptionally well. The traditional TF-IDF method remains worth considering as a term-weighting scheme. Conclusion: This study provides recommendations for emotion detection in texts. Future studies can benefit from dealing with imbalances in the dataset to provide better performance. Keywords: Emotion Detection, Feature Engineering, Term-Weighting, Text Mining
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Ni'mah, Ana Tsalitsatun, and Agus Zainal Arifin. "Perbandingan Metode Term Weighting terhadap Hasil Klasifikasi Teks pada Dataset Terjemahan Kitab Hadis." Rekayasa 13, no. 2 (2020): 172–80. http://dx.doi.org/10.21107/rekayasa.v13i2.6412.

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Hadis adalah sumber rujukan agama Islam kedua setelah Al-Qur’an. Teks Hadis saat ini diteliti dalam bidang teknologi untuk dapat ditangkap nilai-nilai yang terkandung di dalamnya secara pegetahuan teknologi. Dengan adanya penelitian terhadap Kitab Hadis, pengambilan informasi dari Hadis tentunya membutuhkan representasi teks ke dalam vektor untuk mengoptimalkan klasifikasi otomatis. Klasifikasi Hadis diperlukan untuk dapat mengelompokkan isi Hadis menjadi beberapa kategori. Ada beberapa kategori dalam Kitab Hadis tertentu yang sama dengan Kitab Hadis lainnya. Ini menunjukkan bahwa ada beberapa dokumen Kitab Hadis tertentu yang memiliki topik yang sama dengan Kitab Hadis lain. Oleh karena itu, diperlukan metode term weighting yang dapat memilih kata mana yang harus memiliki bobot tinggi atau rendah dalam ruang Kitab Hadis untuk optimalisasi hasil klasifikasi dalam Kitab-kitab Hadis. Penelitian ini mengusulkan sebuah perbandingan beberapa metode term weighting, yaitu: Term Frequency Inverse Document Frequency (TF-IDF), Term Frequency Inverse Document Frequency Inverse Class Frequency (TF-IDF-ICF), Term Frequency Inverse Document Frequency Inverse Class Space Density Frequency (TF-IDF-ICSδF), dan Term Frequency Inverse Document Frequency Inverse Class Space Density Frequency Inverse Hadith Space Density Frequency (TF-IDF-ICSδF-IHSδF). Penelitian ini melakukan perbandingan hasil term weighting terhadap dataset Terjemahan 9 Kitab Hadis yang diterapkan pada mesin klasifikasi Naive Bayes dan SVM. 9 Kitab Hadis yang digunakan, yaitu: Sahih Bukhari, Sahih Muslim, Abu Dawud, at-Turmudzi, an-Nasa'i, Ibnu Majah, Ahmad, Malik, dan Darimi. Hasil uji coba menunjukkan bahwa hasil klasifikasi menggunakan metode term weighting TF-IDF-ICSδF-IHSδF mengungguli term weighting lainnya, yaitu mendapatkan Precission sebesar 90%, Recall sebesar 93%, F1-Score sebesar 92%, dan Accuracy sebesar 83%.Comparison of a term weighting method for the text classification in Indonesian hadithHadith is the second source of reference for Islam after the Qur’an. Currently, hadith text is researched in the field of technology for capturing the values of technology knowledge. With the research of the Book of Hadith, retrieval of information from the hadith certainly requires the representation of text into vectors to optimize automatic classification. The classification of the hadith is needed to be able to group the contents of the hadith into several categories. There are several categories in certain Hadiths that are the same as other Hadiths. Shows that there are certain documents of the hadith that have the same topic as other Hadiths. Therefore, a term weighting method is needed that can choose which words should have high or low weights in the Hadith Book space to optimize the classification results in the Hadith Books. This study proposes a comparison of several term weighting methods, namely: Term Frequency Inverse Document Frequency (TF-IDF), Term Frequency Inverse Document Frequency Inverse Class Frequency (TF-IDF-ICF), Term Frequency Inverse Document Frequency Inverse Class Space Density Frequency (TF-IDF-ICSδF) and Term Frequency Inverse Document Frequency Inverse Class Space Density Frequency Inverse Hadith Space Density Frequency (TF-IDF-ICSδF-IHSδF). This research compares the term weighting results to the 9 Hadith Book Translation dataset applied to the Naive Bayes classification engine and SVM. 9 Books of Hadith are used, namely: Sahih Bukhari, Sahih Muslim, Abu Dawud, at-Turmudzi, an-Nasa’i, Ibn Majah, Ahmad, Malik, and Darimi. The trial results show that the classification results using the TF-IDF-ICSδF-IHSδF term weighting method outperformed another term weighting, namely getting a Precession of 90%, Recall of 93%, F1-Score of 92%, and Accuracy of 83%.
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Tama, Fauzaan Rakan, and Yuliant Sibaroni. "Fake News (Hoaxes) Detection on Twitter Social Media Content through Convolutional Neural Network (CNN) Method." JINAV: Journal of Information and Visualization 4, no. 1 (2023): 70–78. http://dx.doi.org/10.35877/454ri.jinav1525.

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The use of social media is very influential for the community. Users can easily post various activities in the form of text, photos, and videos in social media. Information on social media contains fake news and hoaxes that will have an impact on society. One of the most social media used is Twitter. This study aims to detect fake news found on the Tweets using the Convolutional Neural Network (CNN) method by comparing the weighting features used of the Term Frequency Inverse Document Frequency (TF-IDF) and the Term Frequency-Relevance Frequency (TF-RF). The highest accuracy was obtained in the Term Frequency-Relevance Frequency (TF-RF) weighting feature with an accuracy of 84.11%, while in the Term Frequency Inverse Document Frequency (TF-IDF) weighting feature with an accuracy of 80.29%.
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Chen, Long, Liangxiao Jiang, and Chaoqun Li. "Using modified term frequency to improve term weighting for text classification." Engineering Applications of Artificial Intelligence 101 (May 2021): 104215. http://dx.doi.org/10.1016/j.engappai.2021.104215.

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Alshehri, Arwa, and Abdulmohsen Algarni. "TF-TDA: A Novel Supervised Term Weighting Scheme for Sentiment Analysis." Electronics 12, no. 7 (2023): 1632. http://dx.doi.org/10.3390/electronics12071632.

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In text classification tasks, such as sentiment analysis (SA), feature representation and weighting schemes play a crucial role in classification performance. Traditional term weighting schemes depend on the term frequency within the entire document collection; therefore, they are called unsupervised term weighting (UTW) schemes. One of the most popular UTW schemes is term frequency–inverse document frequency (TF-IDF); however, this is not sufficient for SA tasks. Newer weighting schemes have been developed to take advantage of the membership of documents in their categories. These are called supervised term weighting (STW) schemes; however, most of them weigh the extracted features without considering the characteristics of some noisy features and data imbalances. Therefore, in this study, a novel STW approach was proposed, known as term frequency–term discrimination ability (TF-TDA). TF-TDA mainly presents the extracted features with different degrees of discrimination by categorizing them into several groups. Subsequently, each group is weighted based on its contribution. The proposed method was examined over four SA datasets using naive Bayes (NB) and support vector machine (SVM) models. The experimental results proved the superiority of TF-TDA over two baseline term weighting approaches, with improvements ranging from 0.52% to 3.99% in the F1 score. The statistical test results verified the significant improvement obtained by TF-TDA in most cases, where the p-value ranged from 0.0000597 to 0.0455.
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Shehzad, Farhan, Abdur Rehman, Kashif Javed, Khalid A. Alnowibet, Haroon A. Babri, and Hafiz Tayyab Rauf. "Binned Term Count: An Alternative to Term Frequency for Text Categorization." Mathematics 10, no. 21 (2022): 4124. http://dx.doi.org/10.3390/math10214124.

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In text categorization, a well-known problem related to document length is that larger term counts in longer documents cause classification algorithms to become biased. The effect of document length can be eliminated by normalizing term counts, thus reducing the bias towards longer documents. This gives us term frequency (TF), which in conjunction with inverse document frequency (IDF) became the most commonly used term weighting scheme to capture the importance of a term in a document and corpus. However, normalization may cause term frequency of a term in a related document to become equal or smaller than its term frequency in an unrelated document, thus perturbing a term’s strength from its true worth. In this paper, we solve this problem by introducing a non-linear mapping of term frequency. This alternative to TF is called binned term count (BTC). The newly proposed term frequency factor trims large term counts before normalization, thus moderating the normalization effect on large documents. To investigate the effectiveness of BTC, we compare it against the original TF and its more recently proposed alternative named modified term frequency (MTF). In our experiments, each of these term frequency factors (BTC, TF, and MTF) is combined with four well-known collection frequency factors (IDF), RF, IGM, and MONO and the performance of each of the resulting term weighting schemes is evaluated on three standard datasets (Reuters (R8-21578), 20-Newsgroups, and WebKB) using support vector machines and K-nearest neighbor classifiers. To determine whether BTC is statistically better than TF and MTF, we have applied the paired two-sided t-test on the macro F1 results. Overall, BTC is found to be 52% statistically significant than TF and MTF. Furthermore, the highest macro F1 value on the three datasets was achieved by BTC-based term weighting schemes.
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Santhanakumar, M., C. Christopher Columbus, and K. Jayapriya. "Multi term based co-term frequency method for term weighting in information retrieval." International Journal of Business Information Systems 28, no. 1 (2018): 79. http://dx.doi.org/10.1504/ijbis.2018.091164.

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Santhanakumar, M., C. Christopher Columbus, and K. Jayapriya. "Multi term based co-term frequency method for term weighting in information retrieval." International Journal of Business Information Systems 28, no. 1 (2018): 79. http://dx.doi.org/10.1504/ijbis.2018.10012193.

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Dissertations / Theses on the topic "Term frequency weighting"

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Ma, Qing. "Feature selection and term weighting beyond word frequency for calls for tenders documents." Thèse, 2006. http://hdl.handle.net/1866/17866.

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Book chapters on the topic "Term frequency weighting"

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Sabbah, Thabit, and Ali Selamat. "Modified Frequency-Based Term Weighting Scheme for Accurate Dark Web Content Classification." In Information Retrieval Technology. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12844-3_16.

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Tardelli Adalberto O., Anção Meide S., Packer Abel L., and Sigulem Daniel. "An implementation of the Trigram Phrase Matching method for text similarity problems." In Studies in Health Technology and Informatics. IOS Press, 2004. https://doi.org/10.3233/978-1-60750-946-2-43.

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The representation of texts by term vectors with element values calculated by a TFIDF method yields to significant results in text similarity problems, such as finding related documents in bibliographic or full-text databases and identifying MeSH concepts from medical texts by lexical approach and also harmonizing journal citation in ISI/SciELO references and normalizing author's affiliation in MEDLINE. Our work considered “trigrams” as the terms (elements) of a term vector representing a text, according to the Trigram Phrase Matching published by the NLM's Indexing Initiative and its logarithmic Term Frequency – Inverse Document Frequency method for term weighting. Trigrams are overlapping 3-char strings from a text, extracted by a couple of rules, and a trigram matching method may improve the probability of identifying synonym phrases or similar texts. The matching process was implemented as a simple algorithm, and requires a certain amount of computer resources. An efficiency-focused C-programming was adopted. In addition, some heuristic rules improved the efficiency of the method and made it feasible a regular “find your scientific production in SciELO collection” information service. We describe an implementation of the Trigram Matching method, the software tool we developed and a set of experimental parameters for the above results.
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Al-Thuhli, Amjed, Mohammed Al-Badawi, Youcef Baghdadi, and Abdullah Al-Hamdani. "A Framework for Interfacing Unstructured Data Into Business Process From Enterprise Social Networks." In Social Entrepreneurship. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8182-6.ch035.

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The increased number of Enterprise Social Networks (ESN) business applications has had a major impact on organizations' business processes improvements by allowing the involvement of human interactions to these process. However, these applications generate unstructured data which create barriers and challenges to offering the data in the form of web services in a SOA environment, which again impacts negatively the business process. In this context, the authors propose a framework to interface ESN unstructured data into BP using text mining techniques. The Term frequency-inverse document frequency is used as a weighting schema in this framework. After that, the cosine similarity and k-mean are utilized to find similar values from different documents and cluster documents into groups respectively. The result of the evaluation of the framework shows promising results for retrieving social unstructured data. These results can be published into the SOA enterprise service bus using the RESTful web services.
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Selvi, D. Thamarai, S. Kalaiselvi, V. Anitha, S. Santhi, V. Gomathi, and Sathish Kumar Sekar. "A Sentimental Analysis of Legal Documents Using Mask Attention BERT Networks." In Advances in Web Technologies and Engineering. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7868-7.ch010.

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Legal systems can function more efficiently by processing cases faster and having a higher case clearance rate when complex legal texts are automatically analysed for logical patterns. The most crucial task in doing this is classifying sentences in legal documents automatically based on their content. This chapter suggests a deep learning model for sentiment analysis-based legal text analysis and judgment generation. The transformer model is an innovative encoder-decoder that uses self-awareness to analyse speech patterns which runs noticeably quicker and allows for parallel processing. In this work, the glove embedding and the BERT algorithm—bidirectional encoder representation for transformer model—are utilised to construct sentiment analysis for text categorization. Prior to extracting valuable data from textual input, a preprocessor is used to enhance the quality of the data. Next, pre-trained glove word embedding methods and term frequency-inverse document frequency (TF-IDF) feature weighting are used.
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Maheswari, Bentham Science Publisher. "Bag of Visual Words Model - A Mathematical Approach." In Advanced Mathematical Applications in Data Science. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815124842123010007.

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Information extraction from images is now incredibly valuable for many new inventions. Even though there are several simple methods for extracting information from the images, feasibility and accuracy are critical. One of the simplest and most significant processes is feature extraction from the images. Many scientific approaches are derived by the experts based on the extracted features for a better conclusion of their work. Mathematical procedures, like Scientific methods, play an important role in image analysis. The Bag of Visual Words (BoVW) [1, 2, 3] is one of them, and it is helpful to figure out how similar a group of images is. A set of visual words characterises the images in the Bag of Visual Words model, which are subsequently aggregated in a histogram per image [4]. The histogram difference depicts the similarities among the images. The reweighting methodology known as Term Frequency – Inverse Document Frequency (TF-IDF) [5] refines this procedure. The overall weighting [6] for all words in each histogram is calculated before reweighting. As per the traditional way, the images are transformed into the matrix called as Cost matrix. It is constructed through two mathematical: Euclidean distances and Cosine distances. The main purpose of finding these distances is to detect similarity between the histograms. Further the histograms are normalized and both distances are calculated. The visual representation is also generated. The two mathematical methods are compared to see which one is appropriate for checking resemblance. The strategy identified as the optimum solution based on the findings aids in fraud detection in digital signature, Image Processing, and classification of images.
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Bouarara, Hadj Ahmed, Reda Mohamed Hamou, and Abdelmalek Amine. "A New Swarm Intelligence Technique of Artificial Haemostasis System for Suspicious Person Detection with Visual Result Mining." In Biometrics. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0983-7.ch076.

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In the last few years, the video surveillance system is ubiquitous and can be found in many sectors (banking, transport, industry) or living areas (cities, office building, and store). Unfortunately, this technology has several drawbacks such as the violation of individual freedom and the inability to prevent malicious acts (stealing, crime, and terrorist attack ... etc.). The authors' work deals on the development of a new video surveillance system to detect suspicious person based on their gestures instead of their faces, using a new artificial haemostasis system composed of four steps: pre-processing (pre-haemostasis) for digitalization of images using a novel technique of representation called n-gram pixel, and the weighting normalized term frequency; Each image vector passes through three filters: primary detection (primary haemostasis), secondary detection (secondary haemostasis) and the final detection (fibrinolysis), with an identification step (plasminogen activation) to evaluate the malicious degree of the person presents in this image; the results obtained by their system are promising compared to the performance of classical machine learning algorithms (C4.5 and KNN). The authors' system is composed of a visualization tool in order to see the results with more realism using the functionality of zooming and rotating. Their objectives are to help the justice in its investigations and ensure the safety of people and nation.
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Mindell, David P. "Aligning DNA Sequences: Homology and Phylogenetic Weighting." In Phylogenetic analysis Of Dna sequences. Oxford University PressNew York, NY, 1991. http://dx.doi.org/10.1093/oso/9780195066982.003.0005.

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Abstract Ever since the term homology was defined as “the same organ in different animals under every variety of form and function” by Owen (1848), it has frequently been both used and debated. Homology is a concept representing an hypothesis of correspondence between features, and is used at different levels of biological organization (e.g., phenotypic, genotypic).
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Luque González, Arturo. "Implications and Asymmetries of the Knowledge Society." In COVID-19 Pandemic Impact on New Economy Development and Societal Change. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3374-4.ch008.

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The term knowledge society brings together many of the transformations that are taking place in today's society, and its definition serves as an indicator of these changes. The related concentrations or asymmetries that arise from the phenomenon are also the subject of analysis and dispute. Its development and scope have been uneven, constantly incorporating new meanings to the existing terminology, hence the need to analyze 82 concepts of the knowledge society through a frequency count in Google Scholar, with a subsequent categorization saturating in six dimensions, in order to analyze their framing. The methodology used a higher-order association, establishing the most significant combinations and weightings. From these results, the concept of the knowledge society is defined by the dual economic-social category, according to its frequency of use in Google. This shows economic influences as a determining factor in the knowledge society, engendering processes far from the common good or the general interest.
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Frase, Richard S., and Julian V. Roberts. "Problematic Components Found in Many Criminal History Formulas." In Paying for the Past. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190254001.003.0011.

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The focus of this chapter is on five components of criminal history scores that lack strong justification from the perspective of recidivism risk, retribution, or both rationales. These components are: juvenile court adjudications, misdemeanor convictions, the offender’s “custody status” when committing the offense being sentenced (whether he was incarcerated or on some form of criminal justice release), weighting prior felony convictions according to their severity ranking or other seriousness indicator, and the policy in some jurisdictions of according extra weight to prior offenses that were similar to the offense being sentenced (“patterning” premiums). The chapter then presents data from Minnesota, showing how the inclusion of the first four of these score components greatly increases the frequency and duration of recommended and imposed prison terms. The chapter concludes that criminal history scores should not routinely include any of these five problematic components, although judges might consider them as potential aggravating factors.
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Clary, D. C. "Torsional diffusion Monte Carlo: A method for quantum simulations of proteins." In Quantum Monte Carlo. Oxford University PressNew York, NY, 2007. http://dx.doi.org/10.1093/oso/9780195310108.003.00132.

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Abstract Before this paper studies of the molecular dynamics of proteins had heen carried out primarily with classical dynamics. This paper shows that quantum-dynamical effects are important in determining the energies and geometries of typical proteins. The examples in this study are the proteins gelsolin (with 356 atoms and 142 torsions) and gp41-HIV (1101 atoms and 452 torsions), which were treated with all-atom force fields in diffusion QMC with restriction to torsional motions only. These motions have a much lower frequency than other motions for proteins, and they are the essential motions determining the dynamics of protein folding. The authors were able to assemble a general code for torsional diffusion QMC calculations to be carried out automatically for any protein with known connectivity or atom coordinates converted to internal coordinates of fixed-bond angles, fixed bond lengths, and variable torsion angles. All-atom potential energies were obtained from standard force-field programs. The torsional diffusion calculation is essentially identical to one-dimensional translational motion calculations, except that angles replace distances, and the tt2/2m is replaced by n,2 /21 in the diffusion terms. The results obtained from the calculations are the zero point energies and, with use of descendent weighting methods, distributions of structures or simply vibrationally averaged structures. The calculations show structures significantly changed from minimum-energy structures. It is noted that binding energies for drugs that might inhibit functions of such proteins are likely to be affected and that QMC calculations of this type may be important in drug discovery.
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Conference papers on the topic "Term frequency weighting"

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Umemura, Kyoji, and Kenneth W. Church. "Empirical term weighting and expansion frequency." In the 2000 Joint SIGDAT conference. Association for Computational Linguistics, 2000. http://dx.doi.org/10.3115/1117794.1117809.

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"Full-text Retrieval Model based on Term Frequency and Position Weighting." In 2017 8th International Computer Systems and Education Management Conference. Francis Academic Press, 2017. http://dx.doi.org/10.25236/icsemc.2017.06.

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Kim, Yoon, and Owen Zhang. "Credibility Adjusted Term Frequency: A Supervised Term Weighting Scheme for Sentiment Analysis and Text Classification." In Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis. Association for Computational Linguistics, 2014. http://dx.doi.org/10.3115/v1/w14-2614.

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Chirawichitchai, Nivet, Parinya Sa-nguansat, and Phayung Meesad. "Developing an effective Thai Document Categorization Framework base on term relevance frequency weighting." In Knowledge Engineering 2010). IEEE, 2010. http://dx.doi.org/10.1109/ictke.2010.5692907.

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Plansangket, Suthira, and John Q. Gan. "A new term weighting scheme based on class specific document frequency for document representation and classification." In 2015 7th Computer Science and Electronic Engineering (CEEC). IEEE, 2015. http://dx.doi.org/10.1109/ceec.2015.7332690.

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Abbasian, L., O. M. Akindele, A. N. A. Abugharara, and S. D. Butt. "A Comparative Study of Preparation Methods, Weighting Agents, and Temperature on Quality of E-M Compatible Borehole Imaging Fluid." In 58th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2024. http://dx.doi.org/10.56952/arma-2024-0360.

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ABSTRACT: Imaging fluids plays a crucial role in mitigating the impact of borehole groundwater on borehole E-M imaging results. Such fluid must possess specific characteristics such as low conductivity to minimize electromagnetic wave attenuation, appropriate dielectric permittivity to prevent signal ringing at the fluid-wellbore boundary, higher density than water for settlement at the bottom hole, and long-term stability throughout the imaging process. This study meticulously examines the influence of agitation and temperature on the quality of oil-based imaging fluids, comparing two major preparation methods with distinct grain sizes of the weighting agent. One is the conventional method involving a critical heating step of the emulsifier in 20% of the imaging fluid liquid base, while the second involves dissolving the emulsifier in the liquid base by agitation. Evaluation of the produced fluids encompasses considerations of their stability over time, settlement in various water temperatures, and rheological properties. The results of these experiments reveal the pros and cons of the agitation process compared to the conventional method, weighting agent grain size, and temperature on the overall quality of the produced imaging fluid. 1. INTRODUCTION High frequency electromagnetic waves are used as the source of Ground Penetrating Radar (GPR) to image and map subsurface geological formations and structures (Jol, 2008). Borehole GPR utilizes high frequency electromagnetic waves for mapping out the downhole subsurface geology. This GPR data quality can be influenced by the presence of borehole water or the media between the antenna and the wellbore Li (2023). Hence, the selection of a proper borehole fluid will help overcome the impact of ground water on imaging data quality. Borehole water should be replaced by an imaging fluid at the bottom hole and cover the E-M antennas while imaging. The ideal borehole fluid (imaging fluid) should have specific properties such as low conductivity (low EM wave attenuation), appropriate dielectric permittivity close to that of the host rock to avoid signal ringing between the fluid-wellbore boundary, higher density than water to enable it settle at the bottom hole, and stability such that it does not discompose while imaging. Following the above characteristics, the imaging fluid is made up of a liquid base with low dielectric permittivity and conductivity, a weighting agent to increase its density than water to about Specific Gravity (SG) of 1.2 and an emulsifier to ensure its stability by preventing the separation and settlement of suspended solids.
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Xiao, Bing, Alejandro J. Rivas-Guerra, and Marc P. Mignolet. "Maximum Amplification of Blade Response Due to Mistuning in Multi-Degree-of-Freedom Blade Models." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-54030.

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This paper focuses on the determination and study of the maximum amplification of the steady state forced response of bladed disks due to mistuning. A general multi-degree-of-freedom dynamic model is adopted for each blade/disk sector and optimization techniques are used to maximize a weighted quadratic norm of the response of the degrees-of-freedom of blade 1 (overall response of blade 1). First, a mathematical optimization effort is conducted in which the resonant mistuned mode shape(s) (1 for engine orders 0 and N/2 where N is the number of blades, 2 otherwise) is selected to maximize the overall response of blade 1. The form of these optimum mode shapes is derived for all weighting matrices. The specific mode shapes are also derived for two particular weights the first one of which depends on the tuned bladed disk mass matrix and for which the overall response is akin to the kinetic energy. A closed form solution is also derived when the analysis focuses solely on the response of a specific degree-of-freedom or a specific stress component. In these cases, the ratio of the corresponding overall response to its tuned counterpart, i.e. the amplification factor, is found to be the product of two terms. The first one is an amplification obtained by tuned variations of the blade properties/mode shapes and thus is referred to as the modal amplification factor. The second term is an amplification obtained by proper mistuning. Interestingly, the modal amplification factor may take on very large values while a representative value of the largest mistuned factor is often the Whitehead limit of (1+N)/2 as in the single-degree-of-freedom per blade model. The above formulation and results are readily extended to the optimization of the blade alone response (as opposed to blade and disk sector). Numerical optimization efforts were also undertaken on both a two-degree-of-freedom per blade disk model and a 24-blade blisk reduced order model. The results of these computational efforts not only confirm the assumptions and findings of the theoretical developments but also demonstrate that substantially larger amplification factors can be obtained with a general natural frequency mistuning as opposed to Young’s modulus mistuning. Finally, an amplification due to mistuning (no tuned amplification) slightly larger than the Whitehead limit was obtained with relative variations in blade alone frequencies less than 0.5%.
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8

Mottershead, John E. "On the Optimal Correction of Structural Dynamic Models." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0368.

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Abstract It is well known that a discrete analytical model cannot accurately represent the dynamics of a continuous physical system over the frequency range of the model. Methods have been presented with the intention of improving analytical models (finite element models in particular) by the use of either (a) measured modal data or (b) measured frequency responses. In this paper we consider in particular the selection of weighting terms and the physical significance of the estimates. In both cases the discussion is illustrated by means of example problems.
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9

Ståhlsten, Torbjörn, Hans C. Strifors, and Guillermo C. Gaunaurd. "Signature Features in Returned Echoes From Submerged Targets Insonified by Short, Broadband Pulses: Comparison of Experiments and Theory." In ASME 1995 Design Engineering Technical Conferences collocated with the ASME 1995 15th International Computers in Engineering Conference and the ASME 1995 9th Annual Engineering Database Symposium. American Society of Mechanical Engineers, 1995. http://dx.doi.org/10.1115/detc1995-0421.

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Abstract Backscattered echoes are studied from submerged elastic targets in the frequency domain and combined time-frequency domain when the targets are insonified by short, broadband sound pulses. The targets are either an air-filled spherical shell or various solid brass or steel spheres. The incident waveform is generated by weighting a sinusoidal signal with a Blackman time-window of a few cycles width. The spectrum is computed from each recorded set of experimental data and the result is shown to agree well with the theoretical prediction for the corresponding target and interrogating waveform. An advantage of the time-frequency approach is that target signatures can show the time evolution of the resonance features that identify each target. Experimentally obtained data are processed in the time-frequency domain using a pseudo-Wigner distribution (PWD). The associated time-window is Gaussian, and its width is adjusted to suppress the interference of cross-terms in the PWD, yet retaining the desired property of time-frequency concentrating the extracted features.
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Polyansky, K. V., and I. V. Kovalev. "Personalization of the adaptive learning method of L.A. Rastrigin based on a frequency dictionary using cluster analysis of text corpora." In V ALL-RUSSIAN (NATIONAL) SCIENTIFIC CONFERENCE SCIENCE, TECHNOLOGY, SOCIETY: ENVIRONMENTAL ENGINEERING IN THE INTERESTS OF SUSTAINABLE DEVELOPMENT OF TERRITORIES. Krasoyarsk Science & Technology City Hall, 2024. http://dx.doi.org/10.47813/nto.5.2024.5005.

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The application of the adaptive learning method by L.A. Rastrigin to a frequency multilingual dictionary on system analysis is considered. The disadvantages of this method are revealed, such as the lack of personalization in relation to the student when issuing portions of training information. As a solution, it is proposed to modify the criterion of the quality of training. Its formula includes coefficients of significance obtained from data based on cluster analysis of the training collection of text corpora. For this purpose, TF-IDF weighting of terms from the collection was carried out and a correlation matrix was compiled based on the cosine distances of their vectors. As a result, a new criterion of the quality of training was obtained, taking into account the terminological preferences of the student and based on the significance of terms in the training collection of text corpora. The modified adaptive learning method personalizes the learning process, making it more flexible and modern.
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