Evaluation of information retrieval: precision and recall

Arora, Monika; Kanjilal, Uma; Varshney, Dinesh

doi:10.1504/ijicbm.2016.074482

Cited by 35 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Efektivitas sistem temu kembali informasi dinilai berdasarkan teori dari Lancaster dalam Pendit (2008) dan Arora (2016) yaitu relevan dan tidak relevan. Dalam teori tersebut juga dijelaskan bahwa efektivitas sistem temu kembali informasi dikategorikan menjadi dua yaitu efektif jika nilainya di atas 50% dan tidak efektif jika nilainya di bawah 50%.…”

Section: Frekuensi Recall Melalui Opacunclassified

Analisis Artikel Ilmiah Bidang Ilmu Maritim Dalam Mendukung Repositori Nasional

Royani¹,

Margono²,

Sulaiman³

2019

baca.j.dok.inf.

View full text Add to dashboard Cite

The aim of this research are to find out: (1) data proceed from database with the topic maritime and marine that can access by the user through the OPAC: (2) precision of the data in database: (3) the precision between data proceed by the standard tabulation of literature (keyword) and title in database; (4) the precision between data was inputed with content and metadata field. Result of the extract data from ISJD is descriptor (keyword) Maritime Field (25 pieces keyword from 609 article title) tabulated and made the matrix by excel office. Data that have been collected will be validated base on the recall result, and the precision it will be analyzed descriptively. It found that in Maritime field the descriptor used as keyword was 25 pieces. The keywords were: marine alga as food, ocean mining, marine park and reserves, marine chemical ecology, marine biodiversity, marine law, maritime policy, maritime industry, marine mineral resources, maritime safety, marine biological diversity, loading and unloading, marine ecosystem, marine resources, marine pharmacology, marine resources conservation, marine ecology, marine tourism, marine fisheries, marine environmental, ports, marine shipping, marine pollution, maritime law, and marine transportation. The number of article is found based on keywords. ABSTRAK Penelitian ini bertujuan untuk mengetahui jumlah: (1) data terolah dalam database dengan topik kemaritiman dan kelautan yang bisa diakses oleh pengguna melalui OPAC; (2) cantuman dengan ketepatan data dalam database; (3) ketepatan antara data terolah dengan standar pengolahan literatur (keyword) dengan judul yang terinput di database; (4) ketepatan antara konten dengan field metadata yang terinput. Data hasil ekstrak dari database ISJD berupa deskriptor (kata kunci) bidang Maritim (25 buah kata kunci dari 609 judul artikel) ditabulasi dan dibuat matriks dengan format excel. Data yang terkumpul selanjutnya divalidasi berdasarkan hasil recall dan precision yang kemudian dianalisis secara deskriptif. Penentuan descriptor bidang Maritim yang dipakai sebagai kata kunci dalam penelusuran berjumlah 25 buah. Kata kunci tersebut yaitu marine alga as food, ocean mining, marine park and reserves, marine chemical ecology, marine biodiversity, marine law, maritime policy, maritime industry, marine mineral resources, maritime safety, marine biological diversity, loading and unloading, marine ecosystem, marine resources, marine pharmacology, marine resources conservation, marine ecology, marine tourism, marine fisheries, marine environmental, ports, marine shipping, marine pollution, maritime law, dan marine transportation. Jumlah artikel yang ditemukan berdasarkan kata kunci.

show abstract

Section: Frekuensi Recall Melalui Opacunclassified

Analisis Artikel Ilmiah Bidang Ilmu Maritim Dalam Mendukung Repositori Nasional

Royani¹,

Margono²,

Sulaiman³

2019

baca.j.dok.inf.

View full text Add to dashboard Cite

show abstract

“…The terms true positive refers to the results containing the data present in the actual document and the predicted document. The False-positive refers to the results which contain the data which is not present in the actual document but is predicted by the experimentation (Arora, Kanjilal & Varshney, 2016). The recall is the ratio of true positives over predicted results and can be determined by the following formula given in the Eq.…”

Section: Resultsmentioning

confidence: 99%

FNG-IE: an improved graph-based method for keyword extraction from scholarly big-data

Tahir

Asif

Ahmad

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

Keyword extraction is essential in determining influenced keywords from huge documents as the research repositories are becoming massive in volume day by day. The research community is drowning in data and starving for information. The keywords are the words that describe the theme of the whole document in a precise way by consisting of just a few words. Furthermore, many state-of-the-art approaches are available for keyword extraction from a huge collection of documents and are classified into three types, the statistical approaches, machine learning, and graph-based methods. The machine learning approaches require a large training dataset that needs to be developed manually by domain experts, which sometimes is difficult to produce while determining influenced keywords. However, this research focused on enhancing state-of-the-art graph-based methods to extract keywords when the training dataset is unavailable. This research first converted the handcrafted dataset, collected from impact factor journals into n-grams combinations, ranging from unigram to pentagram and also enhanced traditional graph-based approaches. The experiment was conducted on a handcrafted dataset, and all methods were applied on it. Domain experts performed the user study to evaluate the results. The results were observed from every method and were evaluated with the user study using precision, recall and f-measure as evaluation matrices. The results showed that the proposed method (FNG-IE) performed well and scored near the machine learning approaches score.

show abstract

“…It is difficult to have objectivity to select the known IoT device types when configuring device list because device types are subjectively divided by device manufacturers. The types of IoT devices to be classified in this study were combined with those mentioned in the published papers and ArXiv document [4,5,8,11,12] do not support SSDP or have incomplete functions due to abusing cases (such as DDoS attacks) via SSDP. Hence, when a response is not received or device identification is unsuccessful, a scan method using the MDNS, NBNS protocols borrowed from a number of devices is used as alternative classification methods.…”

Section: Problem Definitionmentioning

confidence: 99%

“…We use precision and recall to evaluate the each methodology and compare the distinguishment accuracy between Nmap and UAIS. We choose precision and recall indicators because they are the popular indicators used to evaluate the performance of classification studies in the information retrieval academic field [12] Precision and recall are calculated by the following formulas. Table 4 shows the results of Nmap and UAIS distinguishment.…”

Section: Accuracy Of Distinguishmentmentioning

confidence: 99%

UDP-Based Active Scan for IoT Security (UAIS)

2021

KSII TIIS

View full text Add to dashboard Cite

Today, IoT devices are flooding, and traffic is increasing rapidly. The Internet of Things creates a variety of added value through connections between devices, while many devices are easily targeted by attackers due to security vulnerabilities. In the IoT environment, security diagnosis has problems such as having to provide different solutions for different types of devices in network situations where various types of devices are interlocked, personal leakage of security solutions themselves, and high cost, etc. To avoid such problems, a TCP-based active scan was presented. However, the TCP-based active scan has limitations that it is difficult to be applied to real-time systems due to long detection times. To complement this, this study uses UDP-based approaches. Specifically, a lightweight active scan algorithm that effectively identifies devices using UPnP protocols (SSDP, MDNS, and MBNS) that are most commonly used by manufacturers is proposed. The experimental results of this study have shown that devices can be distinguished by more than twice the true positive and recall at an average time of 1524 times faster than Nmap, which has a firm position in the field.

show abstract

Evaluation of information retrieval: precision and recall

Cited by 35 publications

References 0 publications

Analisis Artikel Ilmiah Bidang Ilmu Maritim Dalam Mendukung Repositori Nasional

Analisis Artikel Ilmiah Bidang Ilmu Maritim Dalam Mendukung Repositori Nasional

FNG-IE: an improved graph-based method for keyword extraction from scholarly big-data

UDP-Based Active Scan for IoT Security (UAIS)

Contact Info

Product

Resources

About