Neural Network Based Integrated Model for Information Retrieval

Nimmani, Pranaya; Vodithala, Swathy; Polepally, Vijayakumar

doi:10.1109/iciccs51141.2021.9432241

Cited by 8 publications

(6 citation statements)

References 1 publication

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“…The stochastic gradient descent algorithm RMSprop (Root Mean Square Propagation) modifies the learning rate for each weight based on the average magnitude of recent gradients for that weight [28] [29]. It uses an exponentially decaying average to calculate this magnitude, giving more weight to recent gradients than older ones.…”

Section: E Rmsprop Optimizationmentioning

confidence: 99%

Comparison of Adam Optimization and RMS prop in Minangkabau-Indonesian Bidirectional Translation with Neural Machine Translation

Ahda,

Wibawa,

Dwi Prasetya

et al. 2024

JOIV : Int. J. Inform. Visualization

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Language is a tool humans use to establish communication. Still, the language used is one language and between regions or nations with their languages. Indonesia is a country that has a diversity of second languages and is the fourth most populous country in the world. It is recorded that Indonesia has nearly 800 regional languages, but research activities in natural language processing are still lacking. Minangkabau is an endangered language spoken by the Minangkabau people in Indonesia's West Sumatra province. According to UNESCO, the Minangkabau language is listed as a language that is "definitely endangered," with only around 5 million speakers worldwide. This study uses neural machine translation (NMT) to create a formula based on this information. Neural machine translation, in contrast to conventional statistical machine translation, intends to build a single neural network that can be built up to achieve the best performance. Because it can simultaneously hold memory for a long time, comprehend complicated relationships in data, and provide information that is very important in determining the outcome of translation, LSTM is one of the most powerful machine-learning techniques for translating languages. The BLUE score is utilized in the NMT evaluation. The test results use 520 Minangkabau sentences, conducting tests based on the number of epochs ranging from 100-1000, resulting in optimization using Adam being better than optimization RMSprop. This is evidenced by the results of the best BLUE-1 score of 0.997816 using 1000 epochs.

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Section: E Rmsprop Optimizationmentioning

confidence: 99%

Comparison of Adam Optimization and RMS prop in Minangkabau-Indonesian Bidirectional Translation with Neural Machine Translation

Ahda,

Wibawa,

Dwi Prasetya

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…Information retrieval sees multiple different approaches using semantic matching, term matching and word embedding, where distinct chunks of text are matched through similar meanings. In [17], Nimmani et al applied IR to the domain of software engineering, and more specifically, to aid in Change Impact Analysis (CIA). The authors combined the Bag of Words (BoW) method and Long-Short Term Memory (LSTM) networks, achieving better accuracy than current methods.…”

Section: Related Workmentioning

confidence: 99%

Machine Reading at Scale: A Search Engine for Scientific and Academic Research

Sousa¹,

Oliveira²,

Praça³

2022

Systems

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The Internet, much like our universe, is ever-expanding. Information, in the most varied formats, is continuously added to the point of information overload. Consequently, the ability to navigate this ocean of data is crucial in our day-to-day lives, with familiar tools such as search engines carving a path through this unknown. In the research world, articles on a myriad of topics with distinct complexity levels are published daily, requiring specialized tools to facilitate the access and assessment of the information within. Recent endeavors in artificial intelligence, and in natural language processing in particular, can be seen as potential solutions for breaking information overload and provide enhanced search mechanisms by means of advanced algorithms. As the advent of transformer-based language models contributed to a more comprehensive analysis of both text-encoded intents and true document semantic meaning, there is simultaneously a need for additional computational resources. Information retrieval methods can act as low-complexity, yet reliable, filters to feed heavier algorithms, thus reducing computational requirements substantially. In this work, a new search engine is proposed, addressing machine reading at scale in the context of scientific and academic research. It combines state-of-the-art algorithms for information retrieval and reading comprehension tasks to extract meaningful answers from a corpus of scientific documents. The solution is then tested on two current and relevant topics, cybersecurity and energy, proving that the system is able to perform under distinct knowledge domains while achieving competent performance.

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“…Ransomware is considered as major threat which is utilized by most of the hackers to receive money. Furthermore, the expertise which produced Ransomware still prolong which in turn has made it more complex to protect 14 . Ransomware is a kind of malware which deliberately encrypts the significant files of user's and thus needs the sufferer to disburse money for performing the decryption.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the expertise which produced Ransomware still prolong which in turn has made it more complex to protect. 14 Ransomware is a kind of malware which deliberately encrypts the significant files of user's and thus needs the sufferer to disburse money for performing the decryption. Ransomware has become widespread method in cybercrime.…”

Section: Introductionmentioning

confidence: 99%

Ransomware recognition in blockchain network using water moth flame optimization‐aware DRNN

Nalinipriya

Maram

Priya

et al. 2022

Concurrency and Computation

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The emergence of networking systems and quick deployment of applications cause huge increase in cybercrimes which involves various applications like phishing, hacking, and malware propagation. However, the Ransomware techniques utilize certain device which may lead to undesirable properties which might shrink the paying-victim pool. This paper devises a new method, namely Water Moth Flame optimization (WMFO) and deep recurrent neural network (Deep RNN) for determining Ransomware. Here, Deep RNN training is done with WMFO, and is developed by combining Moth Flame optimization (MFO) and Water wave optimization (WWO). Moreover, features are mined with opcodes and by finding term frequency-inverse document frequency (TF-IDF) amongst individual features. Moreover, Probabilistic Principal Component Analysis (PPCA) is adapted to choose significant features. These features are adapted in Deep RNN for classification, wherein the proposed WMFO is employed to produce optimum weights. The WMFO offered enhanced performance with elevated accuracy of 95.025%, sensitivity of 95%, and specificity of 96%.

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Neural Network Based Integrated Model for Information Retrieval

Cited by 8 publications

References 1 publication

Comparison of Adam Optimization and RMS prop in Minangkabau-Indonesian Bidirectional Translation with Neural Machine Translation

Comparison of Adam Optimization and RMS prop in Minangkabau-Indonesian Bidirectional Translation with Neural Machine Translation

Machine Reading at Scale: A Search Engine for Scientific and Academic Research

Ransomware recognition in blockchain network using water moth flame optimization‐aware DRNN

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