Titlebook: Machine Learning and Metaheuristics Algorithms, and Applications; Second Symposium, So Sabu M. Thampi,Selwyn Piramuthu,Dhananjay Singh Conf

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Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models,stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. Researchers have also worked on technical analysis of sto

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DAMP

Big Data: Does BIG Matter for Your Business?, “big” really matter? We attempt to explain when and why big does not matter in many business cases. In those occasions where “Big” does matter, we outline the data strategy framework that differentiates the degree of big data requirements. In conclusion, we offer practical advice on strategic usage

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Machine Learning and Soft Computing Techniques for Combustion System Diagnostics and Monitoring: A used in. Combustion control and optimization techniques are essential for efficient and reliable monitoring of the combustion process. This paper presents a comprehensive review of combustion monitoring diagnostics and prognostics which have been researched thoroughly using various soft-computing te

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Traffic Sign Classification Using ODENet,uch likely that use-cases have constraints to be respected, especially on embedded devices, i.e, low powered, memory-constrained systems. Finding a suitable model under constraints is repeated trial-and-error to find optimal trade-off. A novel technique known as Neural Ordinary Differential Equation

paleolithic

Analysis of UNSW-NB15 Dataset Using Machine Learning Classifiers,ween normal and the real-time network traffic. Behind every evaluation and establishment of attack detection, such datasets are the cornerstone deployed by research community. Creating our own dataset is a herculean task. Hence analyzing the subsisting datasets aids to provide a thorough clarity on

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Concept Drift Detection in Phishing Using Autoencoders,e underlying distribution of the data. A common solution is to retrain the machine learning model which can be expensive, both in obtaining new labeled data and in compute time. Traditionally many approaches to concept drift detection operate upon streaming data. However drift is also prevalent in s

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CybSecMLC: A Comparative Analysis on Cyber Security Intrusion Detection Using Machine Learning Clasreased. The attackers always use communication channels to violate security features. The fast-growing of security attacks and malicious activities create a lot of damage to society. The network administrators and intrusion detection systems (IDS) were also unable to identify the possibility of netw