This work deals with spontaneous music genrefication through computational models which in the recent times has been gaining importance rapidly. Through these hybrid computational models implemented users get an enhan...
This work deals with spontaneous music genrefication through computational models which in the recent times has been gaining importance rapidly. Through these hybrid computational models implemented users get an enhanced level of satisfaction when their choice of music files genre with least latency is gained. The paper includes the use of LPCC attributes to obtain the features of the music files along with robust classification models such as neural networks. For this purpose, one thousand audio files are taken as the sample set. The input audio file is pre-processed and suitable LPCC features are computed and stored into final vector. The pool of vectors of numerous audio samples are finally utilized to train a neural model. The proposed work trains the model for ten very distinct categories of music such as hip-hop, jazz, metal, pop, blues, classical, country, disco, reggae, and rock. Further, a comparison is also made between all the classifiers such as SVM, ANN and random forest (RF). Comparatively the best accuracy rate of 85.53\% has been achieved for the proposed work that validates its effectiveness.
The Internet of Vehicles (IoV) necessitates efficient resource management to meet the growing demands for high data rates, low latency, and real-time communication in Intelligent Transportation Systems (ITS). This pap...
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This paper proposes a novel approach to enhance supply chain (SC) visibility, cooperation, and performance during inventory management while effectively mitigating the risk of information leakage by leveraging machine...
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In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of *** model has obtained state-of-the-art performance for...
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In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of *** model has obtained state-of-the-art performance for several language ***,there has been little work exploring useful architectures for Urdu-to-English machine *** conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and *** results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen *** trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,*** a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more *** attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear ***,we considered refining the attention-based models by defining an additional attention-based dropout *** dropout fixes alignment errors and minimizes translation errors at the word *** empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation *** ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as *** empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed.
Nowadays, wireless sensor networks (WSNs) are often used in industrial settings to gather data regarding the different machines in use. There are numerous sensors such as pressure gauges, temperature sensors, dust sen...
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Area version goals bridge the space between supply and target domain names by remodeling statistics from the source domain to the target area without providing categorized goal facts. Medical image domain version is a...
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Attackers commonly employ malicious URLs and harmful QR codes to spread malware and phishing scams; therefore, they need to be classified. Malicious URLs that lead visitors to phishing or malware-infected websites tha...
Attackers commonly employ malicious URLs and harmful QR codes to spread malware and phishing scams; therefore, they need to be classified. Malicious URLs that lead visitors to phishing or malware-infected websites that steal personal data can be included in emails, social media posts, or website content. Rogue QR codes can be used to propagate malware, steal data, or direct users there, much like malicious websites can. In this paper, the classification of malicious content is provided using two approaches:(1) Malware URL categorization based on ML and malware QR codes (2) classification of malicious QR codes based on deep learning. The first technique classifies URLs as dangerous or benign using machine learning models that are trained on features derived from the URLs. Several ML techniques, such as Random Forest, Naive Bayes, and Support Vector Machine, are used to evaluate the model's performance. The second option focuses on classifying harmful QR codes using deep learning techniques. The classification of QR codes as malicious or benign is done using CNN and well-known transfer learning models like RESNET. In general, the offered approaches provide effective techniques to classify unsafe material, which can be utilized to enhance security.
The World Health Organization (WHO) reported in 2021 that more over 700,000 people had committed suicide. Suicide can be stopped, but most efforts have so far been ineffective. However, the application of machine lear...
The World Health Organization (WHO) reported in 2021 that more over 700,000 people had committed suicide. Suicide can be stopped, but most efforts have so far been ineffective. However, the application of machine learning presents fresh chances to improve prediction accuracy and advance the cause of suicide prevention. This study uses data-driven methodologies to analyze and predict suicide, and presents the same in this report. Machine learning algorithms and PySpark programs are used to analyze a dataset of demographic, socioeconomic, and psychological characteristics related to suicide instances from a Kaggle dataset comprising of 237519 rows and 7 columns. The study creates prediction models like linear regression, decision tree, decision tree regressor and bagging regressor. The results support efforts to prevent suicide by assisting in the prioritization of resources and focused interventions.
To address the safety hazards and inefficiency of traditional manual grain depot management, the application of granary vehicles has emerged as a promising solution. However, the absence of GPS signals indoors and the...
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作者：
Wang, ShuyaoSui, YongduoWang, ChaoXiong, HuiSchool of Data Science
University of Science and Technology of China China
Hong Kong
The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Guangzhou Hkust Fok Ying Tung Research Institute Hong Kong
Knowledge graph (KG) demonstrates substantial potential for enhancing the performance of recommender systems. Due to its rich semantic content and associations among interactive entities, it can effectively alleviate ...
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