Stock market prediction has become a challenging task in today’s world of valuable and best investment. Simple models cannot do stock market prediction to predict future values with high accuracy. The stock market ha...
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ISBN:
(数字)9798350356816
ISBN:
(纸本)9798350356823
Stock market prediction has become a challenging task in today’s world of valuable and best investment. Simple models cannot do stock market prediction to predict future values with high accuracy. The stock market has becoming more complex and volatile, necessitating accurate models that understand its complex, nonlinear dynamics. This paper creates a model to forecast future price of stock market combining Convolutional Neural Network (CNN) and Long-Short Term Memory model (LSTM). Both CNN and LSTM are used for detecting spatial and temporal patterns. Tesla Datasets is used for model implementation. This paper uses Mean Squared Error (MSE) measures as metrics to demonstrate the model's enhanced performance. displays the expected value, compares it to the actual values, and illustrates the potential improvement of the epochs on this model.
Crop yield forecasting involves predicting the amount of crops that a farmer can expect from their field. This prediction is based on several factors, including soil type and environmental conditions. It is a crucial ...
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ISBN:
(数字)9798350359299
ISBN:
(纸本)9798350359305
Crop yield forecasting involves predicting the amount of crops that a farmer can expect from their field. This prediction is based on several factors, including soil type and environmental conditions. It is a crucial issue for the farming industry, which is the backbone of any nation’s economy. Thanks to advancements in Artificial Intelligence, farmers can benefit from accurate crop yield estimations. This research study presents a Feed Forward Neural Network architecture that enables farmers to get accurate crop yield estimates and finally discusses about the implementation of the procedure and its outcome.
In Middle Eastern countries, Dubas insects pose a significant threat, causing plant death by draining sap from date palm leaflets and fronds. This study presents a novel method for detecting Dubas insects at different...
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ISBN:
(数字)9798350359688
ISBN:
(纸本)9798350359695
In Middle Eastern countries, Dubas insects pose a significant threat, causing plant death by draining sap from date palm leaflets and fronds. This study presents a novel method for detecting Dubas insects at different stages of their growth. Existing models perform ineffectively and have large trained weights, making integration with edge devices difficult. To address these issues, we propose a pruned YOLOv7 model which is lighter and has fewer parameters, resulting in 75% lower FLOPs. Pruned Yolov7 outperforms baseline models with a mAP@0.5 of 80.1%. Our approach enables real-time detection and seamless integration into edge devices, resulting in practical solutions to combat Dubas insect infestations.
Identification of cognates across related languages is one of the primary problems in historical linguistics. Automated cognate identification is helpful for several downstream tasks including identifying sound corres...
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Liver diseases are common, thereby posing a significant quality-of-life impairment. They have required accurate and prompt assessments to be managed. Typically, conventional methods of evaluation often include subject...
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ISBN:
(数字)9798350390179
ISBN:
(纸本)9798350390186
Liver diseases are common, thereby posing a significant quality-of-life impairment. They have required accurate and prompt assessments to be managed. Typically, conventional methods of evaluation often include subjectivity and laborious amounts of work. It introduces a DNN-based model with L2 regularization to predict risk categorization of liver diseases based on five categories of categorizations: Healthy_Liver, Low_Risk, Moderate_Risk, High_Risk, and Severe_Liver_Disease. The model includes the hidden layers with rectified linear unit (ReLU) activation functions, batch normalization, and dropout for regularizing to achieve robust learning. The DNN was trained in a very comprehensive Kaggle dataset with many physiological and biochemical features and has achieved an impressive 94.07% classification accuracy. Healthcare professionals will be equipped with a significant resource to develop personalized treatment strategies facilitated by this investigation, which highlights the capabilities of deep learning methodologies in the automation of liver disease assessment. Subsequent research will focus on integrating additional data sources to improve the model’s scalability and predictive precision, thus promoting the advancement of automated liver disease evaluation in clinical settings.
To date, there are a number of online social networks dedicated to musicians. However, these do not truly leverage and capitalize on the musical diversity, i.e., the variability that exists across musicians, their ins...
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ISBN:
(数字)9798350366525
ISBN:
(纸本)9798350366532
To date, there are a number of online social networks dedicated to musicians. However, these do not truly leverage and capitalize on the musical diversity, i.e., the variability that exists across musicians, their instruments, musical activities and social relations. For instance, existing social networks are not designed to search and find for musicians with specific characteristics related to their profile and, above all, the actual particuliarities of their playing style. More importantly, their integration with the Internet of Musical Things (IoMusT) has been largely overlooked thus far. To bridge these gaps, in this paper we propose MusicoNet, a social network for musicians based on IoMusT technologies. The network leverages Semantic Web methods and is made accessible through an app for smartphones and tablet devices, which can wirelessly interact with smart musical instruments (or, through a laptop, with conventional instruments). MusicoNet was not conceived to exchange topic-oriented communication through textual and photographic posts as it occurs in popular social networks, but to support the search for and connectivity among musicians having given diversity factors. Such a search is not simply based on sole textual queries as it occurs in conventional social networks, but also on content-based queries which can be performed via musical instruments. We describe the technical implementation of MusicoNet, the IoMusT ecosystem it enables, and a preliminary technical validation. We then discuss the lessons learned and future avenues for the proposed technology, which represents the first instance of the recently proposed Internet of Musical Things and People paradigm.
This research paper investigates the effectiveness of deep learning models for gait recognition using a variety of data, including gait phase data and sensor data. This study evaluates the performance of convolutional...
This research paper investigates the effectiveness of deep learning models for gait recognition using a variety of data, including gait phase data and sensor data. This study evaluates the performance of convolutional neural networks (CNN) and long-term memory networks (LSTM), focusing on comparative analysis across multiple datasets to gain insight and contribute to different data analyses. The gait level dataset contains important movement patterns as a main area of research. Additionally, sensor-based data identifies various information needs, and complete analysis, for greater understanding and use. These comprehensive experimental results provide several observations on the variability and performance of CNNs and LSTMs across various datasets. The applicability of these models in recognizing activities in different products is presented, and their advantages and limitations in model recognition, display, and feature extraction are seen. This study distinguishes between different types of deep learning models and demonstrates their suitability for handling different types of data. These results lead to decisions regarding model selection and application, contributing to advances in analytics, biometrics, and human recognition applications.
This study analyzes air pollution in Asian cities using the Global Air Pollution Data, consisting of 6,196 entries from 31 countries. Our primary goal is to identify pollution patterns through multivariate analysis an...
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ISBN:
(数字)9798350391213
ISBN:
(纸本)9798350391220
This study analyzes air pollution in Asian cities using the Global Air Pollution Data, consisting of 6,196 entries from 31 countries. Our primary goal is to identify pollution patterns through multivariate analysis and evaluate the effectiveness of six clustering algorithms: K-Means, Hierarchical Clustering, DBSCAN, Gaussian Mixture Models (GMM), Agglomerative Clustering, and Spectral Clustering. Performance was assessed using Silhouette Score, Davies-Bouldin Index, Calinski-Harabasz Index, WCSS, Cohesion, and Separation. The novelty of this work lies in the comparative analysis of these clustering methods on air pollution data, providing new insights into pollution dynamics across Asian cities. The analysis identified four distinct clusters- ‘High Pollution’, ‘Moderate Pollution’, ‘Ozone-Dominated Pollution’, and ‘Low Pollution’- with K-Means proving to be the most effective. Significant disparities were found, particularly in South and East Asia, where countries like India, China, and Pakistan exhibited the highest pollution levels. Additionally, an examination of capital cities revealed specific pollution patterns and the primary pollutants-PM2.5, NO2, CO, and Ozone-aiding in identifying sources and affected regions. These findings underscore the need for targeted regional pollution control strategies.
One of the most prevalent illnesses, typhoid causes a large number of fatalities each year, primarily in Africa. A quick and accurate diagnosis is essential in the medical sector. Self-medication, delayed diagnosis, a...
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Formal methods are crucial for ensuring higher integrity levels for safety-critical systems. However, teaching these methods can be quite challenging. Students often show low motivation and are primarily focused on pa...
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ISBN:
(数字)9798331542788
ISBN:
(纸本)9798331542795
Formal methods are crucial for ensuring higher integrity levels for safety-critical systems. However, teaching these methods can be quite challenging. Students often show low motivation and are primarily focused on passing formal methods courses with minimal effort. Performance in compulsory formal methods courses is usually below average, with students perceiving the subject as overly mathematical and lacking practical relevance. To address these challenges and enrich the learning experience, we have integrated mandatory group homework assignments into our teaching framework. Students are required to work collaboratively on case studies and present their solutions during class. This work-in-progress paper provides an experience report on enhancing the learning possibilities of master’s students in a model checking course at the Frankfurt University of Applied sciences (FRA-UAS).
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