The wireless sensor network (WSNs) is constructed with a combination of the sensor nodes and the sink nodes. WSNs applied in many applications. The Security in WSNs is necessary because of limitations of storage, computing capacity, power use. Encryption is one of the most important tools utilized to supply the security services for WSNs. Asymmetric and symmetric encryption algorithm can be utilized in the WSNs structure to supply the security. The asymmetric key encryption algorithm gives a higher level of the security, but compared to symmetric key encryption, that causes extra sensor overhead. In this research KCMA method used to generate chains key of twelve experiments of the algorithms (ECC, RSA, ELGamal). These chains merged with hash function SHA2 and XOR. Diehard test was utilized in all experiments to evaluate the randomness of the secret key generated, and show it’s more security system. SHA2 was the best as compared with XOR. Also, the work evaluated the performance (time) for the system and throughput network.

The performance of Wi-Fi fingerprinting indoor localization systems (ILS) in indoor environments depends on the channel state information (CSI) that is usually restricted because of the fading effect of the multipath. Commonly referred to as the next positioning generation (NPG), the Wi-Fi™, IEEE 802.11az standard offers physical layer characteristics that allow positioning and enhanced ranging using conventional methods. Therefore, it is essential to create an indoor environment dataset of fingerprints of CIR based on 802.11az signals, and label all these fingerprints by their location data estimate STA locations based on a portion of the dataset for fingerprints. This work develops a model for training a convolutional neural network (CNN) for positioning and localization through generating IEEE® 802.11data. The study includes the use of a trained CNN to predict the position or location of several stations according to fingerprint data. This includes evaluating the performance of the CNN for multiple channel impulses responses (CIRs). Deep learning and Fingerprinting algorithms are employed in Wi-Fi positioning models to create a dataset through sampling the fingerprints channel at recognized positions in an environment. The model predicts the locations of a user according to a signal acknowledged of an unidentified position via a reference database. The work also discusses the influence of antenna array size and channel bandwidth on performance. It is shown that the increased training epochs and number of STAs improve the network performance. The results have been proven by a confusion matrix that summarizes and visualizes the undertaking classification technique. We use a limited dataset for simplicity and last in a short simulation time but a higher performance is achieved by training a larger data.

In the era of information technology, users had to send millions of images back and forth daily. It's crucial to secure these photos. It is important to secure image content using digital image encryption. Using secret keys, digital images are transformed into noisy images in image encryption techniques, and the same keys are needed to restore the images to their original form. The majority of image encryption methods rely on two processes: confusion and diffusion. However, previous studies didn’t compare recent techniques in the image encryption field.This research presents an evaluation of three types of image encryption algorithms includinga Fibonacci Q-matrix in hyperchaotic, Secure Internet of Things (SIT), and AES techniques. The Fibonacci Q-matrix in the hyperchaotic technique makes use of a six-dimension hyperchaotic system's randomly generated numbers and confuses the original image to dilute the permuted image. The objectives here areto analyze the image encryption process for the Fibonacci Q-matrix in hyperchaotic, Secure Internet of Things (SIT), and Advanced Encryption Standard (AES), and compare their encryption robustness. The discussed image encryption techniques were examined through histograms, entropy, Unified Average Changing Intensity (UACI), Number of Pixels Change Rate (NPCR), and correlation coefficients. Since the values of the Chi-squared test were less than (293) for the Hyperchaotic System & Fibonacci Q-matrix method, this indicates that this technique has a uniform distribution and is more efficient. The obtained results provide important confirmation that the image encryption using Fibonacci Q-matrix in hyperchaotic algorithm performed better than both the AES and SIT based on the image values of UACI and NPCR.

In recent days, a wide variety of tools have appeared for performing educational data mining (EDM) . The current education systems show that there are several factors affecting students’ performances. First and foremost, students need motivation in order to learn  and this  motivation results into their success.  The prediction of student performances is an important field of research in Educational Data Mining, particularly through the application of different data mining techniques. The majority of EDM research focuses on prediction algorithms. The current work presents a review of the data mining predicting algorithms and tools that have been adopted in EDM. It also provides insight into the algorithms and powerful data mining tools that most widely used in student performance prediction. This will mainly be of use for  educators, instructors and institutions, increasing the students’ levels of study.

The popular technology Wireless sensor networks (WSNs) are used in many fields of the application such as the medical, the military, the industry, the agricultural, etc. In this paper, explains the security issues in the WSNs. Firstly explain the challenges of wireless sensor networks, the security requirements such as confidentiality, integrity, authenticity, data freshness, and availability and the attacks in the WSNs, the security issues are accomplished via these classes: [the encryption algorithms (symmetric, asymmetric, hybrid) , the security protocols such as (Tinysec, SPINS, LEDS, Minisec, LEAP, MASA, Lightweight LCG, MiniSec, VEBEK of WSN), the secure data aggregation, and the key management, etc.]. Also, this paper concentrates on the study researches that fulfill the high level of the security in the WSNs.