IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING

Thomas and Patrik are working on a stream cipher called SNOW 3G. In 2006, it was chosen as the centerpiece of a new set of confidentiality and integrity algorithms for the Universal Mobile Telecommunications System (UMTS). In 2008, Böhm published an article named "Statistical Evaluation of Stream Cipher SNOW 3G." He put the randomness of the SNOW 3G key stream generator to the test. As a randomness test tool, Böhm uses the NIST test statistics package, which consists of three kinds of tests: lengthy key stream data, short key stream data, and initialization vector data. Only the short key stream set of data failed eight random chance test results out of three kinds of tests, according to the report's findings. The SNOW 3G suggestions, he claims, fail due to a flaw with in cipher's initialization. In this paper, we use the PLL algorithm to modify the SNOW 3G algorithm for key initialization and generation keystream. We employ the same itself Böhm employed. The modify SNOW 3G algorithm exceed whole of the statistic exam in a experiment. The findings show that PLL has an effect on algorithm randomness.

Artificial Neural Networks (ANNs) are utilized to solve a variety of problems in many domains. In this type of network, training and selecting parameters that define networks architecture play an important role in enhancing the accuracy of the network's output; Therefore, Prior to training, those parameters must be optimized. Grey Wolf Optimizer (GWO) has been considered one of the efficient developed approaches in the Swarm Intelligence area that is used to solve real-world optimization problems. However, GWO still faces a problem of the slump in local optimums in some places due to insufficient diversity. This paper proposes a novel algorithm Levy Flight- Chaotic Chen mapping on Wolf Pack Algorithm in Neural Network. It efficiently exploits the search regions to detect driving sleepiness and balance the exploration and exploitation operators, which are considered implied features of any stochastic search algorithm. Due to the lack of dataset availability, a dataset of 15 participants has been collected from scratch to evaluate the proposed algorithm's performance. The results show that the proposed algorithm achieves an accuracy of 99.3%.

Feature space high dimensionality is a well-known problem in text classification and web mining domains, it is caused mainly by the large number of vocabularies contained within web documents. Several methods were applied to select the most useful and important features over the years; however, the performance of such methods is still improvable from different aspects such as the computational cost and accuracy. This research presents an enhanced cosine similarity-based hybridization of two efficient feature selection methods for higher classification performance. The reduced feature sets are generated using the Random Projection (RP) and the Principal Component Analysis (PCA) methods, individually, then hybridized based on the cosine similarity values between features’ vectors. The performance of the proposed method in terms of accuracy and F-measure was tested on a dataset of web pages based on several term weighting schemes. As compared to relevant methods, results of the proposed method show significantly higher accuracy and f-measure performance based on less feature set size.

In recent years, especially with COVID-19, video conference applications have become very important. Millions of peoples around the world have become to communicate with each other through using video conference applications. The most critical factor in the performance success of video conference applications is the user's perception of the quality of the experience. In this work, an Extreme Learning Machine (ELM) model was proposed for predicting video quality of experience. The proposed system extracts several features from videos that have a significant impact on the quality of the experience. The model performance was validated with unseen data. Spearman’s Rank Correlation Coefficient (SRCC), Root Mean Square Error (RMSE), Pearson’s Linear Correlation Coefficient (PLCC) metrics have been used to measure the accuracy of the model and correlation. The results demonstrate that the proposed model had better performance than models used by the previous researchers that were used for predicting video QoE in terms of precision, correlation, and running time.

since the global pandemic of COVID-19 has spread out, the use of Artificial Intelligence to analyze Chest X-Ray (CXR) image for COVID-19 diagnosis and patient treatment is becoming more important. This research hypothesized that using COVID19 radiographic changes in the X-Ray images. Artificial Intelligence (AI) systems may extract certain graphical elements regarding COVID-19 and offer a clinical diagnosis ahead of pathogenic test; therefore, saving vital time for disease prevention. Employing 2614 CXR radiographs from Kaggle data collection of verified COVID-19 cases and healthy persons, a new Convolutional Neural Network (CNN) model that is inspired by the Xception architecture was presented for the diagnosis of coronavirus pneumonia infected patients. The suggested technique reached an average validation accuracy of 0.99, precision of 0.95, recall of 0.92, and F1-score of 0. 95. Finally, such findings revealed that the Deep Learning (DL) technique has the potential to decrease frontline radiologists' stress, enhance early diagnosis, treatment, and isolation; therefore, aid in epidemic control.

Hackerscanconductmoredestructivecyber-attacksthankstotherapidspread of Internet of Things (IoT) devices, posing significant security risks for users. Through a malicious process, the attacker intended to exhaust the capital of the target IoT network. Researchers and company owners are concerned about the reliability of IoT networks, which is taken into account because it has a significant impact on the delivery of facilities provided by IoT systems and the security of user groups. The intrusion prevention system ensures that the network is protected by detecting malicious activity. In this paper, the focus is on predicting attacks and distinguishing between normal network use and network exploitation for intrusion and network attack and we will use Swarm Intelligence (SI) which is one of the types of artificial intelligence (AI) that we harness to choose features to determine the task of them and specifically we will use an algorithm Meerkat Clan (MCA) for this purpose, as this research suggested a modified IDS in machine learning (ML) based IoT environments to identify features and these features will be input into Random Forest algorithm. The IoTID20 dataset is used where nominal traits are removed, so the final dataset contains 79 traits. The data set contains three categories: the label that identifies whether it is a natural use or exploitation, the category that characterizes the type of exploitation, and the subcategory that describes that exploitation more accurately. The number of trees in a random forest (RF) classifier for binary, class, and subclass will be determined by the experiment. The trained classifier is then tested and the approach achieves 100% accuracy for binary target prediction, 96.5% for category and accuracy ranges of 83.7% for sub-category target prediction. The proposed system is evaluated and compared with previous systems and its performance is shown through the use of confusion matrix and others.

Traditional identification techniques for electronic payments, such as the Personal Identification Number (PIN), are becoming outdated and unsafe, while mobile payments are becoming more popular and widely used. It presents a risk to issuers since there is no reliable consumer verification method available, and the lack of safe and reliable e-payment systems is one of the key issues restricting progress. As a result, efforts have been made to develop and maintain a unified payment system that is well-organized, efficient, dependable, and secure. This system avoids the need for physical cash while also still satisfying all payment and identification requirements, a safe and trustworthy method is required for the country's successful adoption of an e-payment system. This article focuses on the future of online payment and the security problems through using effective biometric authentication technologies to provide a trustworthy authentication method for an e-payment system.

Content-Based Image Retrieval (CBIR) is an actual application in computer vision, which retrieves similar images from a database. Deep Learning (DL) is essential in many applications, including image retrieval applications. However, encryption techniques are used to protect data privacy because these data are vulnerable to being viewed by unauthorized parties while being transmitted over unsecured channels.
This paper includes two parts for images retrieval. In the first part, features of all images of a Canadian Institute for Advanced Research CIFAR-10 dataset were extracted and stored on the Server-side. In the second part, the Brakerski/Fan-Vercauteren (BFV) homomorphic encryption scheme method for encrypting an image sent by the client-side. First, their decryption and image features are extracted depending on the trainer model when they arrive on the server-side. Then an extracted features are compared with stored features using the Cosine Distance method, and then the server encrypts the retrieved images and sends them to the client-side. Deep-learning results on plain images were 97% for classification and 96.7% for retriever images. At the same time, TheNational Institute of Standards and Technology (NIST ) test was used to check the security of BFV when applied to CIFAR-10 dataset.

Network traffic has risen in recent years to the point that it is obviously and astonishingly in 2020, with the increase predicted to double in the following days. Up to 23 Teraa bit every month is an incredible amount. The Active Queue Management (AQM) algorithm is one of the most significant study areas in network congestion control; nevertheless, new self-learning network management algorithms are needed on nodes to cope with the huge quantity of traffic and minimize queuing latency, used reinforcement learning for automatic adaptive parameter with the AQM algorithm for effective network management, and present a novel AQM algorithm that focuses on deep reinforcement learning to deal with latency and the trade-off between queuing delay and throughput; choose Deep Q-Network (DQN) as the foundation for our scheme and equate it with Random Early Detection (RED) Results based on Network simulation (NS3) simulation suggest that the DQN algorithm has good and better results were obtained from RED, where the difference reached a drop rate of 2%, and this percentage is considered good, in addition to the percentage of throughput and the packet transfer rate of 3% is better in the proposed algorithm.

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Trees (DT), K- nearest neighbor (KNN), and Logistic Regression (LR), have been used to identify the parameters that allow for effective price estimation. These approaches were tested on a data set of an extensive Indian airline network. When it came to estimating flight prices, the results demonstrate that the Decision tree method is the best conceivable Algorithm for predicting the price of a flight in our particular situation with 89% accuracy. The SGD method had the lowest accuracy, which was 38 %, while the accuracies of the KNN, NB, ADA, and LR algorithms were 69 %, 45 %, and 43 %, respectively. This study's presented methodologies will allow airline firms to predict flight prices more accurately, enhance air travel, and eliminate delay dispersion.

The main reason for the emergence of a deepfake (deep learning and fake) term is the evolution in artificial intelligence techniques, especially deep learning. Deep learning algorithms, which auto-solve problems when giving large sets of data, are used to swap faces in digital media to create fake media with a realistic appearance. To increase the accuracy of distinguishing a real video from fake one, a new model has been developed based on deep learning and noise residuals. By using Steganalysis Rich Model (SRM) filters, we can gather a low-level noise map that is used as input to a light Convolution neural network (CNN) to classify a real face from fake one. The results of our work show that the training accuracy of the CNN model can be significantly enhanced by using noise residuals instead of RGB pixels. Compared to alternative methods, the advantages of our method include higher detection accuracy, lowest training time, a fewer number of layers and parameters.

The Internet is a massive network that connects millions of users from all over the world and the data transmitted via it needs great protection, especially since that are in the age of big data. To solve part of this problem, IPsec was utilized, which is a set of protocols necessary to offer security to units of the Internet in general and the IP layer in particular. It is mostly based on major exchange protocols. The most frequent mechanism for transferring key materials and establishing security linkages between two entities is Internet Key Exchange (IKE). In the present work, it is proposed to use a public key that works together with Diffie-Hellman cryptography and the main advantages of a single-stage contribution (as opposed to the two-stage in standard IKE) it is better in terms of improved transfer and time (more time for the corresponding negotiation) to make the proposed IKE more secure with Simple account constraints

Crowd detection has various applications nowadays. However, detecting humans in crowded circumstances is difficult because the features of different objects conflict, making cross-state detection impossible. Detectors in the overlapping zone may therefore overreact. The proposal uses the YOLO v5 (You Only Look Once) method to improve crowd recognition and counting. This algorithm is entirely accurate and detects things in real-time. The idea relies on edge enhancement and pre-processing to solve overlapping feature regions in the image and improve performance. The CrowdHuman data set is used to train YOLO v5. The system counts the number of humans in the image to detect a crowd. Before training, this model enhanced the image with several filters. The YOLO v5 algorithm distinguishes a person inside a crowd by utilizing the surrounding box on the head and overall body. Therefore, the number of head detection is x- coordinated compared to the body. Assume the detected heads outnumber the bodies. A square of the head will be extracted, but not a body square. Also, cropping the image reduces interference between human beings and enhances the edge features. Thus, YOLOv5 can detect it. The idea improves head and body detection by 2.17 and 4.1 percent, respectively.

Cryptographic approaches based on chaos theory provide a several new and promising avenues for developing safe picture encryption solutions. This paper aims to complicate the process of decrypting images by adding encryption with keys, this was achieved by applying the principle of the 3D-chaotic system with the encryption algorithm, so we present an image encryption algorithm called black mask by using an efficient Multidimensional Chaotic Map represented by Lorenz system. For the confusion process, the suggested approach is based on a keys stream generator. The process of confusion is initiated by a 256-bit secret keys, which is produced by a logistic maps. To make the cipher more dynamic in the face of any threat. The suggested digital image encryption technique, as well as its security analysis and implementation, are discussed in depth. The experimental results suggest that the proposed method for image encryption and transmission is both efficient and safe.

Text summarization can be utilized for variety type of purposes; one of them for summary lecture file. A long document expended long time and large capacity. Since it may contain duplicated information, more over, irrelevant details that take long period to access relevant information. Summarization is a technique which provides the primary points of the whole document, and in the same time it will indicates the majority of the information in a small amount of time. For this reason it can save user time, decrease storage, and increase transfer speed to transmit through the internet. The summarization process will eliminate duplicated data, unimportant information, and also replace complex expression with simpler expression. The proposed method is using convolutional recurrent neural network deep model as a method for abstractive text summarization of lecture file that will be great helpful to students to address lecture notes. This method proposes a novel encoder-decoder deep model including two deep model networks which are convolutional and recurrent. The encoder part which consists of two convolutional layers followed by three recurrent layers of type bidirectional long short term memory. The decoder part which consists of one recurrent layer of type long short term memory. And also using attention mechanism layer. The proposed method training using standard CNN/Daily Mail dataset that achieved 92.90% accuracy.

This article presents a decentralized controller/observer for nonlinear large- scale interconnected systems with actuator fault. The proposal integrates a robust proportional-integral-derivative (PID) controller with the unknown input observer (UIO) to achieve closed-loop robustness against the interactions and the actuator faults. In this scheme, the PID controller is tuned using the Bacterial foraging optimization algorithm (BFO) algorithm. On the other hand, the unknown input observer can diagnose the actuator faults from the controller input. A numerical example consisting of two subsystems is adopted to clarify the effectiveness of the suggested method with a guarantee that the state estimation error is asymptotically converged to zero. The actuator faults have been added to the second subsystem, keeping the first subsystem free of fault. The simulation results demonstrated the influence of the interactions between subsystems, verifying that the unknown input observer can detect the actuator faults despite the presence of these interactions between the subsystems.

Cryptography is a security strategy that prevents disclosure of the information while it is transit, in storage, or both. There are a variety of methods for maintaining data security, including utilizing light weight speed algorithms for encryption and parameter validation. Many algorithms have originated in the area of information protection, helping to assure the validity of the information generated. These include the following algorithms: SHA-1, SHA-2, SHA-3, AES, RC5, RSA, and more. In order to secure the legitimacy of the information and monitoring data , the speed of encryption and authentication must be critical. . Due to the necessity of fast and secure algorithms, these features are required. In this work, modification of the SHA-3 algorithm by introducing a new function called (the keccak function), which has an extremely quick execution time and a high level of security, also versatile cryptographic function. This change is implemented via the 2D chaotic system, which is geared towards generating random values for constants for the SHA3 algorithm. These constants values are generated by the SHA3 algorithm, and so are random and unguessable by the intrude. Statistical tests conducted by the National Institute of Standards and Technology (NIST) effectively outperformed the randomness of a proposed approach .The proposed algorithm shows lower execution time compared to previous studies, which is 0.041616sec for 1MB.

This Face detection is considering one of the important topics for recognizing human, it is the first step before the face recognition process, it is considered one of the biggest challenges in the field of vision computer. In recent years Many algorithms for detection have appeared, which depend on extracting the features of the human face, and works continue to develop them to this day. This paper aims to make a comparison between two of the most commonly face detection methods, Viola Jones (V_J) and YOLO v3. This comparison is made to determine which of the two algorithms is being most useful when used to detect faces in digital video. These algorithms are used in many applications, including image classification, medical analysis of image, and objects detection in real time (especially in surveillance cameras). Both algorithms are applied to detect faces in the real time video. The experimental results of a sample consists of 20 video frames show that V_J algorithm consumes less time in comparison with YOLO v3 algorithm, but its results are less accurate, unlike the YOLO v3 algorithm, which is slower in detect face with high accurate rate.

Due to sophisticated cyber-attacks, and to produce false alarms on suspicious or unusual behavior to monitor computer resources, Intrusion Detection Systems (IDSs) are required. Hence, Many Machine Learning (ML) and data mining techniques have been proposed to increase the effectiveness of IDSs, whereas current IDS algorithms are still struggling to perform effectively while many IDSs depend on a single classifier to detect intrusions. Single- classifier IDSs cannot achieve high accuracy and low false alarm rates because of zero-day attacks. In this paper, a hybrid ensemble method using AdaBoosting and Bagging for IDS is proposed. This study aims to identify unknown (zero-day attacks) and known (well-known) attacks. So, the proposed model comprises three stages. The first stage is preprocessing. The second stage involves the application of AdaBoosting and Bagging methods by four different classifiers modifying (i.e., Naïve Bayesian (NB), Support Vector Machine (SVM), random forest (RF), and K_Nearest Neighbor (KNN)). Such a modification is performed for the AdaBoosting methods. The AdaBoosting classifier is then combined to work in the Bagging method. For attack recognition, uses the voting technique as the third stage. Experimental results reveal that using the UNSW BN15 dataset yields testing with 85.49% accuracy, 99.96% detection rate, and 0.006 false alarm rate. Therefore, the proposed Hybrid AdaBoosting and Bagging Method (HABBM) can outperform other comparable and state-of-the-art techniques across a variety of parameters.

Computer worms execute damaging functions in the network systems, compromising system security. Although researchers use a variety of methods to detect worms and prevent their spread. Detecting worms remains a challenge for the following reasons: First, a huge volume of irrelevant data affects classification accuracy. Second, frequently used individual classifiers in systems are poor at detecting all types of worms, Third, many systems are built on out-of-date information, rendering them useless for new worm species. As a result, providing a network intrusion detection system is vital for ensuring security and reducing the harm caused by worms on networks to information systems. The goal of the study is to discover computer worms in the computer networks and protect the systems from their damages. The proposed method uses the UNSW NB15 dataset to train and test the ensemble Ada boosting and Bagging algorithms with the Support Vector Nachine (SVM) as a contribution rather than a decision tree. Due to Correlation Feature Selection (CFS) identifying relationships between features and classes, and Chi-square (Chi2) determining whether features and classes are independent or not, we combined these two algorithms as a contribution in a method called CFS&Chi2fs to select the relevant features and reduce the time. The system achieved accuracy reaching 0.998 with Bagging(SVM), and 0.989 with Ada boost(SVM).

Recently, the Internet of Things (IoT) is begin used in many fields such as smart homes, healthcare systems, industrial applications, etc. Therefore, the use of the IoT led to a growth in the number of dangers especially in the areas of privacy and security for applications running on low- resource computers. Consequently, the demand for lightweight encryption methods is growing. To safeguard sensing data, this study introduces a Lightweight Advanced Encryption Standard (LAES) depending on dynamic ShiftRows, initial permutation instead of MixColumns, and a dynamic number of rounds. It was created with the goal of reducing encryption/decryption time. The proposed approach was assessed by using various measurements such as lengths of the key used was 2128 and it is quite enough for security, key sensitivity values were 100%, Also, this study compared the encryption/decryption time, NIST statistical test, and security strength of the proposed architecture to those of XTEA, SIMON, Skinny, SPECK, and PRESENT. The encryption/decryption time of the proposed approach was had the shortest period (0.0169 S) while the SPECK algorithm was had the longest period (4.1249 S) among the comparative algorithms. Whereas, NIST statistical test values of the proposed approach were passed successfully and had higher values than the comparative algorithms. Moreover, the proposed approach utilized 1280, 1024, and 768 GE with 6, 8, or 10 rounds respectively. The average number of GE was approximately 1000 GE. These numbers of GE are considered highly efficient with the IoT environment.

Ease of access to digital images and the many images editing programs available, like photoshop. All this makes the Issue of protecting images against modification becomes essential. Some images contain crucial information that can risk a patient's life, such as medical images and e- government images that relate to citizen information and state or ministry security. The watermark was one of the essential methods for this type of protection, especially the fragile watermark, which is very sensitive to any attack. Because of its other characteristics, it was one of the techniques that proved its efficiency in detecting tampering and the authenticity of images— also, watermarking focuses on protecting the image itself, not about protecting the secret message. A fragile watermark is a watermarking which inserts some information to cover an image to secure it .fragile watermarking could use in such a way and implement in spatial or frequency domain or in both so, making it a hybrid watermarking scheme. The Paper presented set of fragile watermark techniques used by the researchers with the performance metrics of an algorithm used in spatial and frequency domains, also showing how to use artificial intelligence with a watermarking technique to protect Document images from manipulation and forgery.

In the last few years, many applications have viewed great development, such as smart city applications, social media, smartphones, security systems, etc. In most of these applications, facial recognition played a major role. The work of these applications begins by locating the face within the image and then recognizing the face. The circumstances surrounding the person at the moment of taking the picture greatly affect the accuracy of these applications, especially the inappropriate lighting. Therefore, the stage of preparing the images is very important in the work. To solve this problem, we proposed a system that combines the use of gamma and Histogram Equalization algorithm (HE) to improve the images before starting to detect the face using the Viola-Jones. Then extract the facial features and identify the person using convolutional neural networks. The proposed system achieved a very small error rate and an accuracy during training that reached 100%.

Many companies recognize the importance of cloud computing all around the world. However, various worries keep businesses from adopting cloud computing. Data security, privacy, and trust difficulties are among them. Recently, there have been rapid developments in the progression of cloud computing services. This paper focuses on the design and implementation of the secure cloud services framework by providing secure and trusted storage for user data. Proposed framework generated an encryption key based on a chaotic map generator and encrypted user data. proposed work shows that integration of key with defensive options is more efficient than approaches from those categories of using external keys. A test has been applied on the frame work in cloud slime services and show the effectiveness of the proposed solution to provide secure cloud services. Our model of cloud services show valid ad promising performance with multiple users trail.

Task scheduling is one of the very crucial facets of cloud computing. The task scheduling method must assign jobs to virtual machines. In cloud computing, task scheduling includes a frontal influence on a system's resource utilization and operational costs. Diverse meta-heuristic algorithms, in addition to their modifications, have been developed to improve the efficiency of task executions in the cloud. In this paper, a multiobjective optimization model is applied using the metaheuristics cuckoo search optimization algorithm (MCSO) to enhance the performance of a cloud system with limited computing resources while minimizing the time and cost. Finally, we analyze the performance of the proposed MCSO with the existing methods, such as Bee Life Algorithm (BLA), A Time–Cost aware Scheduling (TCaS) algorithm, Modified Particle Swarm Optimization (MPSO), and Round Robin (RR), for the evaluation metrics makespan and cost. Based on the outcomes of the experiments, it can be inferred that the proposed MCSO provides essential schedule jobs with the shortest makespan and average cost.

Designing an efficient irrigation system is a crucial issue in agriculture, due to water scarcity problem around the world with the need of increasing agricultural production to satisfy the demands of the enlargement of population. Therefore, to design a smart irrigation system, a real monitoring of field’s information that affects the watering status is required which can be achieved with Wireless Sensor Networks (WSN). In this paper, an irrigation system based WSN is proposed to save water, power, labor, and as a result, saving cost with production and profit increase. Sensor nodes collect field data to be sent to the Raspberry pi, as a main controller, to make optimal decisions about irrigation process. The field data includes the sensor readings of temperature and soil moisture. Crop evapotranspiration is also considered; thus, the required amount of water is estimated with a particular irrigation time to avoid over irrigation that hurts the plants growth and yields quality. The obtained results show the efficiency of the proposed system operation and controlling on the irrigation process. These results are taken for tomato plant as a case study. The monitoring tools are used to verify the suggested algorithm effectiveness in irrigation scheduling.

Biometrics signs are the most important factor in the human recognition field and considered an effective technique for person authentication systems. Voice recognition is a popular method to use due to its ease of implementation and acceptable effectiveness. This research paper will introduce a speaker recognition system that consists of preprocessing techniques to eliminate noise and make the sound smoother. For the feature extraction stage, the method Mel Frequency Cepstral Coefficient (MFCC) is used, and in the second step, the four features (FF) Mean, Standard Division, Zero-Cross and Amplitude, which added to (MFCC) to improve the results. For data representation, vector quantization has been used. The evaluation method (k-fold cross-validation) has been used. Supervised machine learning (SML) is proposed using Quadratic Discriminant Analysis (QDA) classification algorithms. And the results obtained by the algorithm (QDA) varied between 98 percent and 98.43 percent, depending on the way of features extraction that was used. These results are satisfactory and reliable.

The university courses timetabling problem (UCTP) is a big topic among academics and institutions since it occurs every academic year. In general, UCTP is the distribution of events across slots time for each room based on a list of restrictions provided in one semester, such as (hard constraint and soft constraint), with the objective of avoiding conflicts in such assignments. Hard constraints should never be breached when striving to satisfy as many soft constraints as possible. There are many different methods used in automating the problems of the university timetabling course in higher education institutions. This paper presents an improved algorithm for the Meerkat Clan to solve the UCTP. This is done by studying the behavior of the Meerkat Clan Algorithm and Specifying the points that are able to improve without changing the main behavior of the Meerkat Clan Algorithm. And by testing with four datasets of different sizes, good results were obtained by optimizing this algorithm.