
 The paper presents the results of the research on the advantages and disadvantages of innovative e-learning during the COVID-19 pandemic in higher education. In the first stage, an internet questionnaire was used for the study. The research was carried out in Poland on a sample of 621 students. The results allowed the researchers to determine variables that are important for the proper implementation of innovative e-learning, and the emergent perspectives for this form of education after the end of the COVID-19 pandemic. Among the most significant disadvantages, there was a lack of direct contact with colleagues, difficulties with teaching practical subjects, lack of direct contact with the teacher, and too much time spent in front of a computer/telephone or other mobile device. The most important correlations included the following: students satisfied with e-learning assess the advantages highly and the disadvantages lower; all the advantages of innovative e-learning are positively correlated with the student’s assessment of the ease of acquiring content in e-learning, and negatively correlated with the student’s assessment of the ease of acquiring content in traditional education; the easier the student assimilates innovative e-learning content, the higher the student’s evaluation of the advantages of e-learning; students who perceive e-learning content as difficult to absorb have low motivation to learn remotely; the better the student knows information technology, the more highly the student evaluates the advantages of innovative e-learning; the better resources to participate in e-learning activities the student has, the higher the student’s evaluation of the advantages of e-learning; the more often innovative e-learning solutions are used in a given university, the better the student assesses the advantages of e-learning.


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Advantages and Disadvantages of E-Learning Innovations during COVID-19 Pandemic in Higher Education in Poland

Image encryption plays a pivotal rule in enhancing telecommunications media. Since Privacy is necessary in our daily life in many areas, the personal image will be encrypted when it sent it over the Internet to the recipient to maintain privacy issue. In this paper, the image is encrypted using ChaCha symmetric stream cipher with Hyperchaotic Map. Due to the sensitivity characteristics of initial conditions, pseudo randomness chaotic maps and control parameters in chaotic, Hyperchaotic maps is use, higher security is obtained via using initial seed number, variance of parameters, and unpredictable direction of chaotic. The suggested lightweight image encryption has confirmed robustness contra brute force attacks by providing a massive key space. Furthermore, the suggested lightweight image encryption is eligible to defense from statistical cracking, insecurity of image based on criteria's histogram correlation and entropy.

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A proposed lightweight image encryption using ChaCha with hyperchaotic maps

Lately, the COVID-19 pandemic is the first reason of deaths in people. Where the number of patients who have the same symptoms increased; however, the main causative agent separated and analysed. At the first, it called a novel coronavirus (2019-nCoV) then it was renamed as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that cause a disease called COVID- 19. One of the symptoms of the disease is the difficulty breathing caused by lung damage that needs to be detected earlier as much as possible. Chest X-ray images are one of medical images, which can be read by physicians to detect COVID-19. In this paper, the suggested Schema of convolutional neural network (CNN) that can aid the doctors in hospital to improve the diagnosis of the five different classes (COVID-19, MERS SARS, ARDS and Normal). For evaluating testing set, the practical outcomes demonstrates the suggested Schema of CNN classifier with an accuracy of 98%.

A Novel Aided Diagnosis Schema for COVID 19 Using Convolution Neural Network

Malaria parasite detection using deep learning algorithms based on (CNNs) technique

The concept Web of Things (WoT) goes well beyond the emphasis on the Internet as a means of sharing data, instead of introducing all resources and connections involving computers, data, and people to the Web. It, therefore, focuses on a range of problems and opportunities, thus paving the way for several exciting industries applications.  In cryptography a range of main characteristics of chaotic systems such as non-linearity, initial condition sensitivity, and mixing properties are available. These characteristics provide an essential connection between cryptography and chaos. GOST block cipher is based on secret key secrecy. However, when the encryption process with the same key is used for plaintext, the same cipher text is created. Message replication can be easily detected by an adversary who is a bad link in every communication. In this paper, propose to use a 5d chaotic system combined with GOST block cipher to create a new secure Web of Things (WoT) cryptography system. The 5D chaotic system was used to generate chaotic random keys used in GOST algorithm to provide proper security with as high hardness randomly enhances the NIST fifteen statistical tests and modifies key schedule as security operations.

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A New WoT Cryptography Algorithm Based on GOST and Novel 5d Chaotic System

Lately, Internet improved in the various trends, especially, the use of the image increased due to the daily use in several scopes like social media (Facebook, Twitter, WhatsApp, etc.), connected devices (sensor, IP camera, Internet of Things (IoT) Internet of Everything (IoE), etc) and smart phone devices that users interchanged images estimated in the billions. So, images issues in internet can be summarized into two criteria, the first criteria is considered with transmit image size.  The second criteria is considered with low bandwidth through transmission. This paper exhibits a methodology for image compression using an idea of multiplication Table. The suggested algorithm helpful in realizing a preferable achievement by presenting a high Compression Ratio, preserve image quality with a high PSNR, small losing in the original image and efficiently in running time.

/pdf/a-proposed-lossy-image-compression-based-on-multiplication-49koeyadxl.pdf

A Proposed Lossy Image Compression based on Multiplication Table

Face-to-face learning has been replaced by E-learning due to the closing of academic institutions in the world during the covid-19 pandemic. Educational institutions faced many challenges in the online platforms and the most important of which was assessing students' performance, which resulted in the general problem of cheating detection in the online exams. E-learning has grown significantly every day over the last decade with the growth of the internet and technology. Therefore, an online examination can be beneficial for people to take the exam, but cheating in tests is a common phenomenon around the world. As a consequence, the prevention of cheating can no longer be completely effective. Many researchers discussed online examination cheating without addressing an important point, which is analyzing students' answers to find similar responses between them. This paper proposed a recommendation system for evaluating students' answers and detecting cheating during an online exam utilizing statistical methods, similarity measures, and clustering algorithms by presenting a set of features derived from an online exam based on the Moodle platform. The results showed that the suggested online examination system effectively reduces cheating and provides a reliable online exam. In conclusion, presenting an effective and fair system that maintains academic integrity, which is the most important aspect of education.

Cheating Detection in Online Exams during Covid-19 Pandemic Using Data Mining Techniques

Speech encryption approaches are used to prevent eavesdropping, tracking, and other security concerns in speech communication. In this paper, a new cryptography algorithm is proposed to encrypt digital speech files. Initially, the digital speech files are rearranged as a cubic model with six sides to scatter speech data. Furthermore, each side is encrypted by random keys that are created by using two chaotic maps (Hénon and Gingerbread chaotic maps). Encryption for each side of the cube is achieved, using the based map vector that is generated randomly by using a simple random function. Map vector that consists of six bits, each bit refers to one of the specific chaotic maps that generate a random key to encrypt each face of the cube. Results show that the pseudo-random keys created by using chaotic maps for cryptographic speech file have an acceptable characteristic concerning randomness tests, which is confirmed in this paper by using five statistical tests. The final evaluation of the speech encryption algorithm is measured by using different quality metrics, and the results show that the algorithm can achieve resist encryption.

https://ijs.uobaghdad.edu.iq/index.php/eijs/article/download/4430/2423

Digital Speech Files Encryption based on Hénon and Gingerbread Chaotic Maps

Street lighting is very important now-days especially at dangerous areas and highways but it consume a lot of power and it became challenging for many researchers in the past few years. Enormous efforts have been placed on the issue of reducing power consumption in illuminating cities and streets, researchers had various approaches and methods in tackling this challenging matter, till now there is no ideal system that has been developed to reduce the electricity usage. In this paper intelligent controller based on deep learning proposed to control the light at the street from sunset to sunrise, the system will decrease the light used to illuminate the streets in the absence of movements, the network trained based on deep learning with several image of different objects to help the system detecting any moving objects in the street to provide the street with the exact amount of light needed in order to reduce the waste of electrical energy resulting from street lighting and to help reduce accidents hence high percentage of criminal activity and life threatening conditions occur in the absence of light. The system was trained with a vast and diverse dataset to assure the accuracy and efficiency of the proposed system, the trained system showed a result of 90 precision of detecting moving objects, the proposed system was tested with a new dataset to assure the reliability and dependency of the system and reducing the errors to the minimum, the system shows promising results in detecting movements and objects, after the detection being complete, the system will send a pulse width modulation causing a 20 % light dimming, leading to enormous reduction in the power consumption, adding to that the proposed system is easy to use

https://zenodo.org/record/6861238/files/Using%20deep%20learning%20to%20design%20an%20intelligent%20controller%20for%20street%20lighting%20and%20power%20consumption.pdf

Mohammed Salih Mahdi

Papers

A proposed lightweight image encryption using ChaCha with hyperchaotic maps

A Proposed Lossy Image Compression based on Multiplication Table

Cheating Detection in Online Exams during Covid-19 Pandemic Using Data Mining Techniques

Digital Speech Files Encryption based on Hénon and Gingerbread Chaotic Maps

Using deep learning to design an intelligent controller for street lighting and power consumption