Financial markets depend heavily on stock price prediction, which affects both market behaviour and investment decisions. Given the complexity and volatility of financial markets, traditional methods of stock price fo...
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ISBN:
(数字)9798350377002
ISBN:
(纸本)9798350377019
Financial markets depend heavily on stock price prediction, which affects both market behaviour and investment decisions. Given the complexity and volatility of financial markets, traditional methods of stock price forecasting sometimes encounter difficulties. Due to their capacity to extract complex patterns from large datasets, deep learning techniques have become more and more popular in recent years. The use of deep learning models to forecast stock values is examined in this research study, and their effectiveness is compared among various deep learning models. The study employs a diverse dataset encompassing historical stock prices. LSTM based models such as Long-Short Term Memory (LSTM), Bidirectional Long Short-Term Memory(Bidirectional-LSTM), Stacked Long Short-Term Memory(Stacked-LSTM) and Tree based models such as Random Forest, ADA Boost, XG Boost are employed to predict future prices. Using historical stock price data, the models are trained and verified, and a variety of performance indicators are used to evaluate how accurate are the predictions. In this research study, we found that stacked LSTM performs better among various other models.
In the era of high-speed internet access, a surge in redundant data generation is observed across different media sources and devices, posing challenges in computational and storage efficiency during data outsourcing ...
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Early identification of skin cancer is mandatory to minimize the worldwide death rate as this disease is covering more than 30% of mortality rates in young and adults. Researchers are in the move of proposing advanced...
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Nowadays,commercial transactions and customer reviews are part of human life and various business *** technologies create a great impact on online user reviews and activities,affecting the business *** reviews and rat...
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Nowadays,commercial transactions and customer reviews are part of human life and various business *** technologies create a great impact on online user reviews and activities,affecting the business *** reviews and ratings are more helpful to the new customer to purchase the product,but the fake reviews completely affect the *** traditional systems consume maximum time and create complexity while analyzing a large volume of customer ***,in this work optimized recommendation system is developed for analyzing customer reviews with minimum ***,Amazon Product Kaggle dataset information is utilized for investigating the customer *** collected information is analyzed and processed by batch normalized capsule networks(NCN).The network explores the user reviews according to product details,time,price purchasing factors,etc.,ensuring product quality and *** effective recommendation system is developed using a butterfly optimized matrix factorizationfiltering *** the system’s efficiency is evaluated using the Rand Index,Dunn index,accuracy,and error rate.
Products generated by agricultural industries are bought and sold in the Agricultural Auction System. An online auction is a procedure where buyers and sellers may interact on a single platform and place bids within a...
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We experimentally analyze the effect of the optical power on the time delay signature identification and the random bit generation in chaotic semiconductor laser with optical *** to the inevitable noise during the pho...
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We experimentally analyze the effect of the optical power on the time delay signature identification and the random bit generation in chaotic semiconductor laser with optical *** to the inevitable noise during the photoelectric detection and analog-digital conversion,the varying of output optical power would change the signal to noise ratio,then impact time delay signature identification and the random bit *** results show that,when the optical power is less than-14 dBm,with the decreasing of the optical power,the actual identified time delay signature degrades and the entropy of the chaotic signal ***,the extracted random bit sequence with lower optical power is more easily pass through the randomness testing.
In recent years, significant progress has been made in knowledge graph representation learning, which has shown promising results in knowledge computing applications such as relation extraction and knowledge reasoning...
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The intend of this literature survey is to lessen the problems faced by dentists in the field of maxillary sinus diagnosis in image processing and to serve as a valuable reference to the literature related application...
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ISBN:
(纸本)9798350372748
The intend of this literature survey is to lessen the problems faced by dentists in the field of maxillary sinus diagnosis in image processing and to serve as a valuable reference to the literature related application. The odontogenic diseases may be diagnosed with atypical symptoms or it might mimic other conditions. This can create difficulty to disembark an accurate diagnosis. Sinusitis or temporomandibular joint disorder possibly is a symptom that resemble an odontogenic infection. Some odontogenic diseases may have overlapping symptoms, making it difficult to differentiate between them when based solely on clinical presentation. For instance, both a periapical abscess and a periodontal abscess can cause localized pain, swelling, and sensitivity. Diagnosing maxillary sinus issues through digital imaging, such as panoramic dental Xray, Cone Beam Computed Tomography (CBCT) and Computed Tomography (CT) scans, can be challenging due to the complex anatomy and the potential for overlapping structures. Radiologists utilize assorted computerized methods for maxillary sinus disease detection. CT scan analysis uses algorithms for segmentation and feature extraction, aiding machine learning algorithms in pattern recognition. CBCT provides detailed three-dimensional images, enabling comprehensive assessments of maxillary sinus anatomy and pathology. MRI utilizes signal intensity variations and texture analysis to identify potential diseases. Moreover, the integration of ultrasound, analysis of endoscopic video, and reporting of automated systems utilizing techniques of deep learning such as Convolutional Neural Networks and Recurrent Neural Networks, enhances precise detection by combining information from various imaging modalities. Interpreting dental radiographs can be complex, and certain conditions may not be clearly visible or may appear differently on different imaging modalities. It requires expertise and experience to accurately interpret radiographic findings and
A smart agricultural informatics platform integrated with Internet of Things (IoT) aims to revolutionize farming practices through a decentralized communication framework, the primary goal is to establish a knowledge-...
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In the contemporary era of technological advancement,smartphones have become an indispensable part of individuals’daily lives,exerting a pervasive *** paper presents an innovative approach to passenger countingonbuse...
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In the contemporary era of technological advancement,smartphones have become an indispensable part of individuals’daily lives,exerting a pervasive *** paper presents an innovative approach to passenger countingonbuses throughthe analysis ofWi-Fi signals emanating frompassengers’mobile *** study seeks to scrutinize the reliability of digital Wi-Fi environments in predicting bus occupancy levels,thereby addressing a crucial aspect of public *** proposed system comprises three crucial elements:Signal capture,data filtration,and the calculation and estimation of passenger *** pivotal findings reveal that the system demonstrates commendable accuracy in estimating passenger counts undermoderate-crowding conditions,with an average deviation of 20%from the ground truth and an accuracy rate ranging from 90%to 100%.This underscores its efficacy in scenarios characterized by moderate levels of ***,in densely crowded conditions,the system exhibits a tendency to overestimate passenger numbers,occasionally doubling the actual *** acknowledging the need for further research to enhance accuracy in crowded conditions,this study presents a pioneering avenue to address a significant concern in public *** implications of the findings are poised to contribute substantially to the enhancement of bus operations and service quality.
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