Developing ICT as English Language Teaching (ELT) materials in Indonesia is still being studied in higher education contexts. Nowadays, amid covid 19 there are many tools, apps and technologies are being integration in virtual classroom.  ICT can be used as media for teaching and learning process it should be related today’s fact by integrating ICT easy enough to share it and easy to access by the students’ technologies devices (Muthmainnah, et.all, 2021 and Apriani et.al, 2021 The purposes of this study is to investigate the impact of development instructional design incorporating social media- movie based learning project (SMMBLP) in Computer Science Faculty in learning English to engagement student’s online environment. The method of this study is quantitative research and used pre-experimental design to investigates the impact of SMMBLP on students’ English skill and student’s motivation in ELT. The participants in this study were the first semester students of Computer Science Faculty academic year 2020-2021 at Universitas Al Asyariah Mandar consisted 40 students. The data were collected by test; pre-test, post-test and questionnaire.  The result of the study implied the impact on integration social media- movie based learning project (SMMBLP) that very effective to enhancing students’ English skill and motivation. The study suggests SMMBL can be integration in online or Hybrid/ Blended environment for the next education teaching model in ELT for EFL students.

Adoption Social Media- Movie Based Learning Project (SMMBL) To Engage Students' Online Environment

The need for remote health and e-health monitoring devices grows rapidly because of their utilisation in day-to-day living. In this phase, information can be transmitted from the patient to the remote clinic for quick diagnosis and treatment. Neurological diseases (NuDs) are currently the most chronic diseases. However, energy consumption and data security are increasing, which are faced with numerous problems including the storage and transmission of the EEG signal. To make it safer, we utilise the Diffie-Hellman key exchange, RSA-key generation for data encryption and decryption (EncDec), to authenticate the recipient. Hence, in this manuscript, the EEG data compression and encryption technique is presented together. Huffman adaptive coding and encryption are performed using the 256-bit AES (CBC) system. The suggested technique shows that compression and encryption performance on the CHB-MIT Scalp EEG database is improved. 

A Brain Seizure Diagnosing Remotely Based on EEG Signal Compression and Encryption: A Step for Telehealth

The constantly evolving phishing attacks have forced scientists to counter such an attack. Most phishing attacks come with dangerous links or URLs that can potentially lead to data or information leaking to third parties. Machine Learning has been proven great in many cases of both known and unknown types of analysis. Its ability to both give reasoning and “self-taught” in addition with easier duplication makes it great to counter phishing attacks. This paper compares different machine learning algorithms to detect whether a URL is a legitimate URL or a phishing URL with a certain feature using a Web page phishing detection dataset. The machine learning algorithms that were compared are Naive Bayes, K-Nearest Neighbor, Random Forest, Decision Tree, Support Vector Machine, and Logistic Regression. The models were trained using a phishing dataset that has been passed through a preprocessing and encoding layer. The model resulting accuracy, along with other evaluation metrics are recorded and compared to each other. The results are pretty close with the highest accuracy claimed by the Random Forest algorithm which is 98,04% out of 11429 URLs.

Accuracy Comparison of Different Machine Learning Models in Phishing Detection

The constantly evolving phishing attacks have forced scientists to counter such an attack. Most phishing attacks come with dangerous links or URLs that can potentially lead to data or information leaking to third parties. Machine Learning has been proven great in many cases of both known and unknown types of analysis. Its ability to both give reasoning and “self-taught” in addition with easier duplication makes it great to counter phishing attacks. This paper compares different machine learning algorithms to detect whether a URL is a legitimate URL or a phishing URL with a certain feature using a Web page phishing detection dataset. The machine learning algorithms that were compared are Naive Bayes, K-Nearest Neighbor, Random Forest, Decision Tree, Support Vector Machine, and Logistic Regression. The models were trained using a phishing dataset that has been passed through a preprocessing and encoding layer. The model resulting accuracy, along with other evaluation metrics are recorded and compared to each other. The results are pretty close with the highest accuracy claimed by the Random Forest algorithm which is 98,04% out of 11429 URLs. 

--- Imidazole azo ligand (DPIDA), one of the azo compounds, was prepared and tested in the sample. In addition to the regular azoth protocol. Because of their ability to form stable complexes with metal ions in some oxidation states, imidazole azo ligands are important heterocyclic azo ligands in coordination chemistry. The complexation of the imidazole azo ligand (DPIDA) was accomplished by adding 4,5-diphenyl imidazole and N1, N1-dimethylbenzene1,4-diamine dihydrochloride. Measurements of 1HNMR, IR, UV-Vis, and molarity Their conductivity demonstrates their octahedral geometry with a bidentate ligand coordinated from an (N3) atom of the imidazole ring and one nitrogen atom of the azo group. The thesis also looked at using this reagent was correspondingly. As a part of the complex the preparation of the solid, as well as some of its physical properties including solubility and molar conductivity, as well as the moment at which the complex melts away, were all investigated. Spectroscopic methods [FT.IR., UV-Vis] were used to classify both substances. As compared to the free reagent, the bathochromic transition is visible in (UV-Vis) absorption spectra, demonstrating the exactness and exactitude of the tool used to make educated guesses about the element mercury the standard deviation from the mean (RSD per cent), although the relative error (E per cent) differed from (4.011 per cent, 0.803 per cent). The thermodynamic functions (∆H,∆G,∆S) were determined as well by investigating the influence of temperature. The approach was extended to both models of the environment and industry, producing results that were surprisingly precise and informative.

Spectrophotometric determination of micro amount of mercury (II) using an (Azo) derivative in the presence of surfactant: Study of thermodynamic functions and their analytical application

The Internet has become one of the most important daily socials, financial and other activities. the number of customers who use the Internet to conduct their business and purchases is very large. This results in billions of dollars being transferred every day online. Such a large amount of money attracts the attention of cybercriminals to carry out their illegal activities. “Fraud” is one of the most dangerous of these methods, especially phishing, where attackers try to steal user credentials using fraudulent emails, fake websites, or both. The proposed system for this paper includes efficient data extraction from the web file through data collection and preprocessing. and web usage mining procedure to extract features that demonstrate user behavior. and feature-extracting URL analysis to detect website phishing addresses. After that, the features from the above two parts are combined to make the number of features sixty-three. Finally, a classification algorithm (Random Forests) is applied to determine if website addresses are phishing or legitimate. Suggested algorithms performance is determined by using a confusion matrix and a number of metrics that shows the robustness of the proposed system.

https://pen.ius.edu.ba/index.php/pen/article/download/2398/980

An adaptive approach for internet phishing detection based on log data

A Telemedicine network that uses connectivity and information technology to transmit medical signals such as neurological signals Electroencephalography (EEG) has become a reality for medical services of long distances. In the monitoring of mobile healthcare, these signals need to be compressed for the efficient utilization of bandwidth and the confidentiality of these signals, where compression is a critical tool to solve storage and transmission problems and can then retrieve the original signal (OS) from the compressed signal. The objective of this manuscript is to achieve higher compression gains at a low bit rate while maintaining the integrity of clinical details and also encrypting the signal to keep it private, except for specialists. Thresholding techniques are utilized in the compression stage, the Discrete Wavelet Transform (DWT). Instead, Huffman Encoding (HuFE) is utilized for compression and EEG signal encryption with chaos. This manuscript addresses the encoding of EEG signals with consistency for Telemedicine applications. To test the proposed method, overall compression and reconstruction (ComRec) time (T) was measured, the root mean square (RMSE), and the compression ratio (CR). Findings from the simulation show that the addition of HuFE after the DWT algorithm gives the best CR and complexity efficiency. The findings show that the consistency of the reconstructed signal (Rs) is maintained at a low PRD while yielding better findings in compression. Utilizing the DWT as a loss compression algorithm followed by the HuFE as a lossy compression algorithm, CR = 92.9% at RMS = 0.16 and PRD = 5. 4131%.

A Tele Encephalopathy Diagnosis Based on EEG Signal Compression and Encryption.

Face recognition is generally considered exploration issue since from most recent twenty years due its significance in biometric confirmation applications. Accordingly, with propels in face catching gadgets face recognition frameworks should be versatile to precise play out the face recognition. For productive face recognition, there various conditions ought to be in contemplations like low goal face pictures, facial feelings, diverse enlightenment conditions like shades. This paper proposed novel way to deal with play out the powerful and effective face recognition paying little heed to face picture quality and enlightenment conditions. We planned the novel face descriptor strategy dependent on half and half neighbourhood heading number (HLDN) to address the issue of various picture brightening conditions and bad quality pictures. The HLDN descriptor is made out of face picture pre-handling utilizing versatile Gaussian sifting utilizing distinction of Gaussian (DoG) and face descriptor age utilizing Kirsch compass veils to produce the directional code for each face picture. The Kirsch administrator deteriorate face picture into 8 distinct bearings and create the exceptional face descriptor. After the face descriptor age, we separated and combined the 2D-DWT and histogram based highlights. We separated both single and staggered histograms to improve the recognition exactness. We applied the Principal part investigation (PCA) to create last picture highlight vector. PCA assists with advancing the recognition execution. The proposed strategy is assessed with various face datasets utilizing ANN classifier. The exhibition of proposed strategy is improved when contrasted with against condition of-craftsmanship strategies.

Face Recognition utilizing Novel Face Descriptor & Algorithm of Feature Extraction

Face recognition is generally considered exploration issue since from most recent twenty years due its significance in biometric confirmation applications. Accordingly, with propels in face catching gadgets face recognition frameworks should be versatile to precise play out the face recognition. For productive face recognition, there various conditions ought to be in contemplations like low goal face pictures, facial feelings, diverse enlightenment conditions like shades. This paper proposed novel way to deal with play out the powerful and effective face recognition paying little heed to face picture quality and enlightenment conditions. We planned the novel face descriptor strategy dependent on half and half neighbourhood heading number (HLDN) to address the issue of various picture brightening conditions and bad quality pictures. The HLDN descriptor is made out of face picture pre-handling utilizing versatile Gaussian sifting utilizing distinction of Gaussian (DoG) and face descriptor age utilizing Kirsch compass veils to produce the directional code for each face picture. The Kirsch administrator deteriorate face picture into 8 distinct bearings and create the exceptional face descriptor. After the face descriptor age, we separated and combined the 2D-DWT and histogram based highlights. We separated both single and staggered histograms to improve the recognition exactness. We applied the Principal part investigation (PCA) to create last picture highlight vector. PCA assists with advancing the recognition execution. The proposed strategy is assessed with various face datasets utilizing ANN classifier. The exhibition of proposed strategy is improved when contrasted with against condition of-craftsmanship strategies. 

Salwa Mohammed Nejrs

Papers

An adaptive approach for internet phishing detection based on log data

A Tele Encephalopathy Diagnosis Based on EEG Signal Compression and Encryption.

Face Recognition utilizing Novel Face Descriptor & Algorithm of Feature Extraction

Face Recognition utilizing Novel Face Descriptor & Algorithm of Feature Extraction

Salwa Mohammed Nejrs

Papers

An adaptive approach for internet phishing detection based on log data

A Tele Encephalopathy Diagnosis Based on EEG Signal Compression and Encryption.

Face Recognition utilizing Novel Face Descriptor & Algorithm of Feature Extraction

Face Recognition utilizing Novel Face Descriptor &amp; Algorithm of Feature Extraction

Face Recognition utilizing Novel Face Descriptor & Algorithm of Feature Extraction