Summarization of Text and Image Captioning in Information Retrieval Using Deep Learning Techniques

Mahalakshmi, P.; Fatima, Mehwish

doi:10.1109/access.2022.3150414

Cited by 22 publications

(4 citation statements)

References 22 publications

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“…Clustering will be further discussed in Section 2.3.2. Alongside clustering, IR systems have been shown to be helpful in a variety of tasks, one such being text summarization [38],…”

Section: Importance Of Information Retrieval Systemsmentioning

confidence: 99%

Discovering Related Terms and Detecting Trends in Software Engineering Using Word Embeddings

Baskararajah

2024

Preprint

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<p>The Software Engineering (SE) community is prolific, making it challenging for experts to keep up with the flood of new papers and for neophytes to enter the field. One solution that has been proposed to ease the burden of entry on the community would be automatic summarization of papers. While there exist term and trend summarization and analysis tools, the unique language utilized in SE requires bespoke solutions. Therefore, we posit that the community may benefit from a tool extracting terms and their interrelations from the SE community's text corpus and showing terms' trends. In this paper, we build a prototyping tool using the word embedding technique. We train the embeddings on the SE Body of Knowledge handbook and 15,233 research papers' titles and abstracts. We create test cases necessary for validation of the training of the embeddings. Upon gathering the trends of interrelated SE terms, we also use cluster analysis to investigate the trends, to help discover underlying patterns in the way trends in SE rise and fall in popularity. We provide representative examples showing that the embeddings may aid in summarizing terms and uncovering trends in the knowledge base, as well as showing examples of patterns that may lie underneath trends in interrelated terms in software engineering.</p>

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“…Clustering will be further discussed in Section 2.3.2. Alongside clustering, IR systems have been shown to be helpful in a variety of tasks, one such being text summarization [38],…”

Section: Importance Of Information Retrieval Systemsmentioning

confidence: 99%

Discovering Related Terms and Detecting Trends in Software Engineering Using Word Embeddings

Baskararajah

2024

Preprint

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“…The representation and organization of information items should be such that users can easily access the desired information [21]. Recently, text mining and clustering techniques have been used to extract unstructured data such as job profiles [6], text summarization [22], text analysis [23], question answering [24], and topic interest [25]. Later, research on job selection and recommendations was inseparable from the Artificial Intelligence (AI) technology approach, as in clustering, classification, text mining, and NLP [26]- [28].…”

Section: Related Workmentioning

confidence: 99%

Information Technology Job Profile Using Average-Linkage Hierarchical Clustering Analysis

Siswipraptini,

Warnars,

Ramadhan

et al. 2023

IEEE Access

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The growth in Information Technology (IT) jobs is predicted to reach 15 percent between 2021 and 2031. The growth of IT jobs has resulted in a remarkable change in all infrastructure, such as information, skills, and domains covered in IT job profiles. Unfortunately, job roles and skills in this field remain undefined. The gap between the supply and demand needs in the IT workforce must be filled immediately with an appropriate strategy. To fulfill industry needs, an in-depth analysis of IT job profiles is important. Therefore, it is important for educational programs to identify the competencies needed by the industry to update their output. This study aims to identify the job profiles required for IT job specialists by analyzing real-world job posts published online to identify hidden meanings from a textual database. A systematic semantic methodology was proposed using an average-linkage hierarchical clustering analysis. It resembles a tree structure technique to discover relevant phrases, relationships, and hidden meanings through semantic analysis. Occurrences of the most frequent words and phrases were extracted to reveal the domain knowledge of each IT job cluster. The result is a systematic semantic analysis of the IT job profile comprising the programming language, specialized type, duty, database, tools, and frameworks. The justification for each job profile was validated by 10 IT professionals from various private and government companies in Indonesia through Focus Group Discussions (FGD).INDEX TERMS Information technology job profile, skills, Average-Linkage Hierarchical Clustering analysis, most frequent word, most frequent phrase.

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“…DBN have many advantages, such as the ability to encode deeper, higher-order network topological structures, and the ability to prevent over-fitting and falling into local minima through special, inappropriate pre-training phases [26], the ability of unsupervised learning, fast inference, and multilayer structures [24]. Currently, DBN has already been applied to text detection [27][28][29], face and expression recognition [30,31], and hyperspectral image classification [32,33]. DBN has also been combined with AE.…”

Section: Introductionmentioning

confidence: 99%

An acoustic emission identification model for train axle fatigue cracks based on deep belief network

Lin,

Tang,

Zhu

et al. 2024

Meas. Sci. Technol.

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Aiming to effectively identify train axle fatigue cracks, some scholars are now trying to consider introducing acoustic emission detection technology into axle health monitoring and combining it with intelligent neural networks to achieve better monitoring results. In the field of axle fatigue crack acoustic emission identification, the commonly used methods include parameter analysis, wavelet analysis, and traditional artificial neural networks. Though these methods did work in the field of axle fatigue crack identification, they still have two deficiencies. （1）The features are manually extracted depending on large artificial experience. (2) The methods mentioned above have shallow architectures, which cannot handle some complex fatigue crack linear relationships. In this paper, an acoustic emission signal analysis method for axle fatigue cracks based on deep belief networks is proposed to overcome the aforementioned deficiencies. The main purpose of this paper is to emphasize the feasibility of the deep belief network to intelligently identify fatigue crack signals from different types of acoustic emission signals of axles and to classify crack signals at different stages. The research method is to use acoustic emission (AE) technology to obtain the axle fatigue crack AE signal, pretreat the collected time-domain signal, identify whether cracks appear, and classify the stages of crack emergence by deep belief network (DBN) so as to find the axle crack in advance. The experimental analysis results show that the method of identification of axle fatigue cracks based on DBN not only can identify fatigue crack signals but also has a very high identification rate of fatigue cracks at different stages. In the axle fatigue crack acoustic emission identification field, it can be seen that the proposed method in this paper will be a promising approach.

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Summarization of Text and Image Captioning in Information Retrieval Using Deep Learning Techniques

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 22 publications

References 22 publications

Discovering Related Terms and Detecting Trends in Software Engineering Using Word Embeddings

Discovering Related Terms and Detecting Trends in Software Engineering Using Word Embeddings

Information Technology Job Profile Using Average-Linkage Hierarchical Clustering Analysis

An acoustic emission identification model for train axle fatigue cracks based on deep belief network

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