Enhancing cloud security through the integration of deep learning and data mining techniques: A comprehensive review

Cloud computing is crucial in all areas of data storage and online service delivery. It adds various benefits to the conventional storage and sharing system, such as simple access, on-demand storage, scalability, and cost savings. The employment of its rapidly expanding technologies may give several benefits in protecting the Internet of Things (IoT) and physical cyber systems (CPS) from various cyber threats, with IoT and CPS providing facilities for people in their everyday lives. Because malware (malware) is on the rise and there is no well-known strategy for malware detection, leveraging the cloud environment to identify malware might be a viable way forward. To avoid detection, a new kind of malware employs complex jamming and packing methods. Because of this, it is very hard to identify sophisticated malware using typical detection methods. The article presents a detailed assessment of cloud-based malware detection technologies, as well as insight into understanding the cloud's use in protecting the Internet of Things and critical infrastructure from intrusions. This study examines the benefits and drawbacks of cloud environments in malware detection, as well as presents a methodology for detecting cloud-based malware using deep learning and data extraction and highlights new research on the issues of propagating existing malware. Finally, similarities and variations across detection approaches will be exposed, as well as detection technique flaws. The findings of this work may be utilized to highlight the current issue being tackled in malware research in the future

Customer Behavior Analysis for Social Media

It is essential for a business organization to get the customer feedback in order to grow as a company. Business organizations are collecting customer feedback using various methods. But the question is ‘are they efficient and effective?' In the current context, there is more of a customer oriented market and all the business organizations are competing to achieve customer delight through their products and services. Social Media plays a huge role in one's life. Customers tend to reveal their true opinion about certain brands on social media rather than giving routine feedback to the producers or sellers. Because of this reason, it is identified that social media can be used as a tool to analyze customer behavior. If relevant data can be gathered from the customers' social media feeds and if these data are analyzed properly, a clear idea to the companies what customers really think about their brand can be provided

Detecting Well-being in Digital Communities: An Interdisciplinary Engineering Approach for its Indicators

In this thesis, the challenges of defining, refining, and applying well-being as a progressive management indicator are addressed. This work\u27s implications and contributions are highly relevant for service research as it advances the integration of consumer well-being and the service value chain. It also provides a substantial contribution to policy and strategic management by integrating constituents\u27 values and experiences with recommendations for progressive community management

Sparse precision matrix estimation in phenotypic trait evolution models

Phylogenetic trait evolution models allow for the estimation of evolutionary correlations between a set of traits observed in a sample of related organisms. By directly modeling the evolution of the traits along an estimable phylogenetic tree, the model's structure effectively controls for shared evolutionary history. In these models, relevant correlations are usually assessed through the high posterior density interval of their marginal distributions. However, the selected correlations alone may not provide the full picture regarding trait relationships. Their association structure, expressed through a graph that encodes partial correlations, can in contrast highlight sparsity patterns featuring direct associations between traits. In order to develop a model-based method to identify this association structure we explore the use of Gaussian graphical models (GGM) for covariance selection. We model the precision matrix with a G-Wishart conjugate prior, which results in sparse precision estimates. Furthermore the model naturally allows for Bayes Factor tests of association between the traits, with no additional computation required. We evaluate our approach through Monte Carlo simulations and applications that examine the association structure and evolutionary correlations of phenotypic traits in Darwin's finches and genomic and phenotypic traits in prokaryotes. Our approach provides accurate graph estimates and lower errors for the precision and correlation parameter estimates, particularly for conditionally independent traits, which are the target for sparsity in GGMs.Comment: 24 pages, 4 figure

Statistical Computational Topology and Geometry for Understanding Data

Here we describe three projects involving data analysis which focus on engaging statistics with the geometry and/or topology of the data. The first project involves the development and implementation of kernel density estimation for persistence diagrams. These kernel densities consider neighborhoods for every feature in the center diagram and gives to each feature an independent, orthogonal direction. The creation of kernel densities in this realm yields a (previously unavailable) full characterization of the (random) geometry of a dataspace or data distribution. In the second project, cohomology is used to guide a search for kidney exchange cycles within a kidney paired donation pool. The same technique also produces a score function that helps to predict a patient-donor pair\u27s a priori advantage within a donation pool. The resulting allocation of cycles is determined to be equitable according to a strict analysis of the allocation distribution. In the last project, a previously formulated metric between surfaces called continuous Procrustes distance (CPD) is applied to species discrimination in fossils. This project involves both the application and a rigorous comparison of the metric with its primary competitor, discrete Procrustes distance. Besides comparing the separation power of discrete and continuous Procrustes distances, the effect of surface resolution on CPD is investigated in this study

A Review on the Applications of Machine Learning for Tinnitus Diagnosis Using EEG Signals

Tinnitus is a prevalent hearing disorder that can be caused by various factors such as age, hearing loss, exposure to loud noises, ear infections or tumors, certain medications, head or neck injuries, and psychological conditions like anxiety and depression. While not every patient requires medical attention, about 20% of sufferers seek clinical intervention. Early diagnosis is crucial for effective treatment. New developments have been made in tinnitus detection to aid in early detection of this illness. Over the past few years, there has been a notable growth in the usage of electroencephalography (EEG) to study variations in oscillatory brain activity related to tinnitus. However, the results obtained from numerous studies vary greatly, leading to conflicting conclusions. Currently, clinicians rely solely on their expertise to identify individuals with tinnitus. Researchers in this field have incorporated various data modalities and machine-learning techniques to aid clinicians in identifying tinnitus characteristics and classifying people with tinnitus. The purpose of writing this article is to review articles that focus on using machine learning (ML) to identify or predict tinnitus patients using EEG signals as input data. We have evaluated 11 articles published between 2016 and 2023 using a systematic literature review (SLR) method. This article arranges perfect summaries of all the research reviewed and compares the significant aspects of each. Additionally, we performed statistical analyses to gain a deeper comprehension of the most recent research in this area. Almost all of the reviewed articles followed a five-step procedure to achieve the goal of tinnitus. Disclosure. Finally, we discuss the open affairs and challenges in this method of tinnitus recognition or prediction and suggest future directions for research