Cloud and mobile edge computing (MEC) provides a wide range of computing services for mobile applications. In particular, mobile edge computing enables a computing and storage infrastructure provisioned closely to the end-users at the edge of a cellular network. The small base stations are deployed to establish a mobile edge network that can be coined with cloud infrastructure. A large number of enterprises and individuals rely on services offered by mobile edge and clouds to meet their computational and storage demands. Based on user behavior and demand, the computational tasks are first offloaded from mobile users to the mobile edge network and then executed at one or several specific base stations in the mobile edge network. The MEC architecture has the capability to handle a large number of devices that in turn generate high volumes of traffic. In this work, we first provide a holistic overview of MCC/MEC technology that includes the background and evolution of remote computation technologies. Then, the main part of this paper surveys up-to-date research on the concepts of offloading mechanisms, offloading granularities, and computational offloading techniques. Furthermore, we discuss the offloading mechanism in the static and dynamic environment along with optimization techniques. We further discuss the challenges and potential future directions for MEC research.

/pdf/computation-offloading-in-mobile-cloud-computing-and-mobile-21hjlajj.pdf

Computation Offloading in Mobile Cloud Computing and Mobile Edge Computing: Survey, Taxonomy, and Open Issues

In the face of an adversary, cryptography is a matter of coordination. It addresses a variety of topics such as confidentiality, authentication, and several vital distributions. Modern cryptography offers the framework for knowing precisely what these concerns are, how to test protocols intended to address them, and how to create protocols that you can trust in their protection. Advanced computer technology can access multimedia easily. Multimedia technologies have recently become popular in operation, and multimedia data protection has become the key concern. In this correspondence, the fundamental problems related to the problem of cryptography were addressed, and surveys of imaging strategies focused on disorderly schemes were also discussed. The chaotic cryptography of images can be accomplished with chaos properties, including deterministic dynamics, unpredictable action, and nonlinear transformation. This definition contributes to approaches that can simultaneously provide protection functionality and an overall visual check that might be acceptable for such applications. In different implementations, including military, legal, and medical processes, digital photographs are commonly used. These applications must monitor the access to images and include ways of checking the accuracy of images. In this paper, a detailed review of chaotic Scheme-based image encryption techniques is presented.

A Review on Chaotic Scheme-Based Image Encryption Techniques

In the new era, multimedia technology is used extensively. Multimedia data transfers over the Internet are not adequately stable. Data protection is needed for digital images transfer to avoid unauthorized entities. Many encryption strategies have been established for multimedia data over a medium that is not stable. This paper aims to compare the techniques used in encoding multimedia content. This paper explains the process for assessment for chaos image encryption algorithm. The chaos-based image encryption algorithm is used mainly because of its high protection and efficiency. Parameters such as MSC, encryption quality and avalanche effect check the image’s quality. This paper presents a comparative review of non-chaotic and chaotic image encryption techniques.

A Comparative Review on Non-chaotic and Chaotic Image Encryption Techniques

Convolutional Neural Networks (CNNs) operate within a wide variety of hyperparameters, the optimization of which can greatly improve the performance of CNNs when performing the task at hand. However, these hyperparameters can be very difficult to optimize, either manually or by brute force. Neural architecture search or NAS methods have been developed to address this problem and are used to find the best architectures for the deep learning paradigm. In this article, a CNN has been evolved with a well-known nature-inspired metaheuristic paddy field algorithm (PFA). It can be seen that PFA can evolve the neural architecture using the Google Landmarks Dataset V2, which is one of the toughest datasets available in the literature. The CNN’s performance, when evaluated based on the accuracy benchmark, increases from an accuracy of 0.53 to 0.76, which is an improvement of more than 40%. The evolved architecture also shows some major improvements in hyperparameters that are normally considered to be the best suited for the task.

https://www.mdpi.com/2079-9292/11/7/1075/pdf?version=1648717306

Evolving CNN with Paddy Field Algorithm for Geographical Landmark Recognition

With the rapid advancement of information technology, online information has been exponentially growing day by day, especially in the form of text documents such as news events, company reports, reviews on products, stocks-related reports, medical reports, tweets, and so on. Due to this, online monitoring and text mining has become a prominent task. During the past decade, significant efforts have been made on mining text documents using machine and deep learning models such as supervised, semisupervised, and unsupervised. Our area of the discussion covers state-of-the-art learning models for text mining or solving various challenging NLP (natural language processing) problems using the classification of texts. This paper summarizes several machine learning and deep learning algorithms used in text classification with their advantages and shortcomings. This paper would also help the readers understand various subtasks, along with old and recent literature, required during the process of text classification. We believe that readers would be able to find scope for further improvements in the area of text classification or to propose new techniques of text classification applicable in any domain of their interest.

https://downloads.hindawi.com/journals/cin/2022/1883698.pdf

A Complete Process of Text Classification System Using State-of-the-Art NLP Models

https://robots.iopscience.iop.org/article/10.1088/1757-899X/993/1/012062/pdf

Secure Surveillance System Using Chaotic Image Encryption Technique

Securing VANET Using Blockchain Technology

In this paper, the sentiment classification approaches are introduced in Indian banking, governmental and global news. The study assesses state-of-art deep contextual language representation, DistilBERT, and traditional context-independent system, TF-IDF, on multiclass (positive, negative, and neutral) sentiment classification news-events. The DistilBERT model is fine-tuned and fed into four supervised machine learning classifiers Random Forest, Decision Tree, Logistic Regression, and Linear SVC, and similarly with baseline TF-IDF. The findings indicate that DistilBERT can transfer basic semantic understanding to further domains and lead to greater accuracy than the baseline TF-IDF. The results also suggest that Random Forest with DistilBERT leads to higher accuracy than other ML classifiers. The Random Forest with DistilBERT achieves 78% accuracy, which is 7% more than with TF-IDF.

M. N. Talib

Papers

Secure Surveillance System Using Chaotic Image Encryption Technique

A Review on Chaotic Scheme-Based Image Encryption Techniques

A Comparative Review on Non-chaotic and Chaotic Image Encryption Techniques

Securing VANET Using Blockchain Technology

Analyzing DistilBERT for Sentiment Classification of Banking Financial News