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Algorithms are ubiquitous in the contemporary technological world, and they ultimately consist of finite sequences of instructions used to accomplish tasks with necessary input values. This book analyzes the top-performing algorithms in areas as diverse as Big Data, Artificial Intelligence, Optimization Techniques, and Cloud &amp Cyber Security Systems to explore their power and limitations.
Algorithms play a vital role in all sciences, especially in Computer Science. We are always in search of efficient algorithms that give the results in less amount of time and that consume less space for large input sizes. Cybersecurity and Big Data are two prominent areas of Computer Science, where the role of algorithms is vital. Algorithms provide the best security mechanism to protect the data from all types of attacks. There are several types of attacks such as threats to data safety, data security, and the mechanisms to store and retrieve the data.
Cybercrime is a hard reality nowadays, and to deal with that we require full proof of algorithms that provide the best data security, data safety, and protection from all kinds of attacks and crimes, which are changing their shapes, nature, and methodology with each passing moment. Big Data is complicated scattered huge data that increases in size with time. Data processing, data safety, data operations, data information extraction, and so on are all equally important. Big Data is the area that deals with huge amounts of data that needs sound high-performance algorithms. To extract the relevant information, data manipulation, access, and retrieval are big challenges.
Depending on intellect, environment, and mindset, humans can effectively identify sarcasm, hatred, abuse, praise, denial, neutrality, and any emotion behind the speech. But for a machine, it is merely a combination of ASCII characters. The machines are unable to identify the sentiments behind a sentence. Thus, to impart such knowledge to machines, several machine learning, deep learning, or natural language processing (NLP) algorithms are utilized. Traditionally, machine learning used several supervised classifiers for detecting hate speech, such as naive Bayes (NB), support vector machines (SVMs), extreme gradient boosting (XGBoost), logistic regression (LR), random forest (RF), k-nearest neighbors (kNNs), and decision tree (DT), which, later on, is advanced with usage of multilayer perceptron (MLP), long short-term memory (LSTM) networks, Bi-LSTM, convolutional neural networks (CNNs), and NLP techniques.