In today's world, technology has become an inevitable part of human life. In fact, during the Covid-19 pandemic, everything from the corporate world to educational institutes has shifted from offline to online. It leads to exponential increase in intrusions and attacks over the Internet-based technologies. One of the lethal threat surfacing is the Distributed Denial of Service (DDoS) attack that can cripple down Internet-based services and applications in no time. The attackers are updating their skill strategies continuously and hence elude the existing detection mechanisms. Since the volume of data generated and stored has increased manifolds, the traditional detection mechanisms are not appropriate for detecting novel DDoS attacks. This paper systematically reviews the prominent literature specifically in deep learning to detect DDoS. The authors have explored four extensively used digital libraries (IEEE, ACM, ScienceDirect, Springer) and one scholarly search engine (Google scholar) for searching the recent literature. We have analyzed the relevant studies and the results of the SLR are categorized into five main research areas: (i) the different types of DDoS attack detection deep learning approaches, (ii) the methodologies, strengths, and weaknesses of existing deep learning approaches for DDoS attacks detection (iii) benchmarked datasets and classes of attacks in datasets used in the existing literature, and (iv) the preprocessing strategies, hyperparameter values, experimental setups, and performance metrics used in the existing literature (v) the research gaps, and future directions. 

https://link.springer.com/content/pdf/10.1007/s00500-021-06608-1.pdf

Deep learning approaches for detecting DDoS attacks: a systematic review

Rapid urbanization, industrial development, and climate change have resulted in water pollution and in the quality deterioration of surface and groundwater at an alarming rate, deeming its quick, accurate, and inexpensive detection imperative. Despite the latest developments in sensor technologies, real-time determination of certain parameters is not easy or uneconomical. In such cases, the use of data-derived virtual sensors can be an effective alternative. In this paper, the feasibility of virtual sensing for water quality assessment is reviewed. The review focuses on the overview of key water quality parameters for a particular use case and the development of the corresponding cost estimates for their monitoring. The review further evaluates the current state-of-the-art in terms of the modeling approaches used, parameters studied, and whether the inputs were pre-processed by interrogating relevant literature published between 2001 and 2021. The review identified artificial neural networks, random forest, and multiple linear regression as dominant machine learning techniques used for developing inferential models. The survey also highlights the need for a comprehensive virtual sensing system in an internet of things environment. Thus, the review formulates the specification book for the advanced water quality assessment process (that involves a virtual sensing module) that can enable near real-time monitoring of water quality.

From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art.

Oral cancer is a dangerous and extensive cancer with a high death ratio. Oral cancer is the most usual cancer in the world, with more than 300,335 deaths every year. The cancerous tumor appears in the neck, oral glands, face, and mouth. To overcome this dangerous cancer, there are many ways to detect like a biopsy, in which small chunks of tissues are taken from the mouth and tested under a secure and hygienic microscope. However, microscope results of tissues to detect oral cancer are not up to the mark, a microscope cannot easily identify the cancerous cells and normal cells. Detection of cancerous cells using microscopic biopsy images helps in allaying and predicting the issues and gives better results if biologically approaches apply accurately for the prediction of cancerous cells, but during the physical examinations microscopic biopsy images for cancer detection there are major chances for human error and mistake. So, with the development of technology deep learning algorithms plays a major role in medical image diagnosing. Deep learning algorithms are efficiently developed to predict breast cancer, oral cancer, lung cancer, or any other type of medical image. In this study, the proposed model of transfer learning model using AlexNet in the convolutional neural network to extract rank features from oral squamous cell carcinoma (OSCC) biopsy images to train the model. Simulation results have shown that the proposed model achieved higher classification accuracy 97.66% and 90.06% of training and testing, respectively.

/pdf/histopathologic-oral-cancer-prediction-using-oral-squamous-1jbeof34.pdf

Histopathologic Oral Cancer Prediction Using Oral Squamous Cell Carcinoma Biopsy Empowered with Transfer Learning

Optimal breast tumor diagnosis using discrete wavelet transform and deep belief network based on improved sunﬂower optimization method

In the analysis of medical images, one of the challenging tasks is the recognition of brain tumours via medical resonance images (MRIs) The diagnosis process is still tedious due to its complexity and considerable variety in tissues of tumor perception Therefore, the necessities of tumor identification techniques are improving nowadays for medical applications In the past decades, different approaches in the segmentation of various precisions and complexity degree have been accomplished, which depends on the simplicity and the benchmark of the technique An overview of this analysis is to give out the summary of the semi-automatic techniques for brain tumor segmentation and classification utilizing MRI An enormous amount of MRI based image data is accomplished using deep learning approaches There are several works, dealing on the conventional approaches for MRI-based segmentation of brain tumor Alternatively, in this review, we revealed the latest trends in the methods of deep learning Initially, we explain the several threads in MRI pre-processing, including registration of image, rectification of bias field, and non-brain tissue dismissal And terminally, the present state evaluation of algorithm is offered and forecasting the growths to systematise the MRI-based brain tumor into a regular cyclic routine in the clinical field are focussed

A comprehensive review on brain tumor segmentation and classification of MRI images

Biomedical engineering is the concept of applying fundamental theories and analytical practices to medicine and biology. It can be profitable in the field of healthcare from implementation of medical devices to diagnostic expert systems. These devices and expert systems produce high-dimensional and irregular data. Employing deep learning (DL) algorithms in these devices will be effective for signal analyzing and identification of diseases. DL is a subset of machine learning that employs multiple levels of neural network. It is capable of learning features automatically. The applications of DL in biomedical engineering can be categorized into four fields. They are bio and medical images analysis, brain, body, and machine interface, genomic sequencing and gene expression analysis, and public and medical health management system. This chapter discusses fundamentals of biomedical engineering and DL. It also explores applications of DL in various problems of biomedical field.

Applications of deep learning in biomedical engineering

Deep learning (DL) is adapted in many areas of artificial intelligence such as speech recognition, image recognition, natural language processing, robot navigation systems, and self-driving cars. This field also aids in analyzing the spread of dangerous diseases such as in the case of pandemics with many social behavior and the other environmental factor, modeling condition diseases such as obesity, and tracking public health. In addition to the aforementioned, there are several categories of tools that help deep learning engineers to do work faster and more effectively. There is much class of tools that enable deep learning engineers to actually do their work faster and more effectively. Some of the tools include TensorFlow, Keras, Caffe, Torch, and so on. DL models make use of several kinds of advanced algorithms. Some algorithms are best suited to perform specific tasks. To choose the right ones, it is good to gain grasp of all primary algorithms. Excellent knowledge of advanced DL techniques, their types, and applications can help users execute it for various purposes.

Application, algorithm, tools directly related to deep learning

Background: Image denoising is one of the primary challenges in the field of image processing. The objective is to derive the original image by suppressing noise from a noisy the image. The need of image denoising techniques is still in high demand. De-noising of medical images is degraded by various noises. Multiresolution techniques are very efficient for medical image denoising. Methodology: In this work, it is proposed to examine the efficiency of different wavelet shrinkage thresholding techniques and to determine the best one. Findings: The metric used for analysis are PSNR, VSNR and WSNR. The experimental results show that third level decomposition of Symlet in association with Neigh Shrink threshold outperforms all other approaches. Applications/Improvements: In this paper, denoising is applied to MRI images. Gaussian Noise, Salt and Pepper Noise, and Speckle Noise can be removed using the methods mentioned. The methods can also be extended to denoising other medical images like CT scan, X-RAY, and Ultra Sound etc.

Wavelet Coefficients Thresholding Techniques for Denoising MRI Images

Noise Removal in Medical Images Using Pulse Coupled Neural Networks

Bias field is a smooth intensity variation, which emanate during the process of image procurement Bias removal is an essential prerequisite while incorporating computer assisted diagnosis Several bias correction algorithms are proposed till date This paper scrutinizes the prominent bias correction algorithms, LEMS, MICO, BCFCM and N3 These investigations carried over the substantial amount of T1 and T2 weighted brain MR images with different bias spectrum from Brain Web website Algorithms efficiency are analyzed in spectral wise, slice wise, and type wise Based upon the performance indicators coefficient of variation and coefficient of joint variation, the algorithms are assessed and ranked The result concludes that which algorithm exterminates the bias field, presents in brain MR images in an efficient manner

S. Shajun Nisha

Papers

Applications of deep learning in biomedical engineering

Application, algorithm, tools directly related to deep learning

Wavelet Coefficients Thresholding Techniques for Denoising MRI Images

Noise Removal in Medical Images Using Pulse Coupled Neural Networks

Performance analysis of bias correction techniques in brain MR images