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The thesis applies solutions to boundary value problems of a class of fractional differential equation coupled systems with a p-Laplacian operator. This article applies this part of the content to alleviate the psychological barriers of college students. This article transforms the tendency of individual consciousness in college students’ psychological counselling into a problem of solving boundary value problems of fractional differential equations. Studies have shown that using fractional differential equations to analyse the causes of college students’ psychological barriers has an essential role in eliminating college students’ psychological barriers.

The power generation of distributed power generation systems in regional power grids is mainly affected by external environmental factors, and there are volatility and intermittent power generation issues. This paper establishes a relatively simple multiple linear regression prediction model to predict grid-connected photovoltaic power generation systems. The article uses a hybrid modelling method to filter more important weather forecast input variables through a step-by-step linear regression method to realize the visual calculation of power data. The research results show that the multiple linear regression equation methods proposed in the article have pervasive application value in the actual application of solar photovoltaic power consumption data.

Under the background of ‘mass entrepreneurship and innovation’, Chinese colleges and universities have strengthened students’ entrepreneurial and innovative abilities. The article first analyses the reasons why applied universities should strengthen innovation and entrepreneurship education. On this basis, the evaluation index system of students’ innovation and entrepreneurship ability is constructed. The thesis uses the nonlinear structural model to complete the index weight setting. Finally, the paper verifies the effectiveness of the combined evaluation model through the data on innovation and entrepreneurship of college students in an university where applied. At the same time, the article proposes measures for optimising the ecological environment of innovation and entrepreneurship education for colleges and universities.

The paper proposes a data model analysis algorithm for human motion function based on short-term behaviour. The algorithm uses a functional data analysis (FDA) method to perform Fourier fitting on the motion data and extract the fitted approximate single period data. Finally, the algorithm depicts the internal change in the motion in the low-dimensional space. The study found that the characteristic motion data obtained by the algorithm has smooth characteristics, and the relevant case analysis also verifies the algorithm's effectiveness.

A bridge structure is one of the most expressive forms of art design. The artistic expression of bridge structure combines different concepts of structural design and architectural art design. Finite element differential equations are widely used in bridge art design theory and based on these features, the paper adopts the bridge modal parameter recognition algorithm and uses the finite element model to modify and realise the bridge's artistic design. The simulation results show the feasibility of the author's attempt to use the finite element differential equation as the bridge structure art design carrier. After the finite element differential equation modelling, the bridge art structure correction is highly consistent with the experimental results.

The 3-year migration of the computing faculty for three undergraduate programs from ad hoc teaching to three accredited programs is the focus of this paper. This journey started after numerous international accreditation organizations were surveyed, and ABET was chosen as the faculty's target. In this paper, the timelines and processes for covering missing data, persuading instructors to collaborate, involving students in the process, and building an assessment model are discussed. Evidence from one of the three programs, the Information Systems program, is provided. In addition, during the 3 years, an accreditation system called AIMS was developed using Oracle to reduce the cost of writing numerous reports in different formats. This system aided the faculty in receiving accreditation from ABET for three programs in addition to other accreditation systems at low cost.

Advancements in wireless communication and automation have revolutionized mobility systems, notably through autonomous vehicles and unmanned aerial vehicles (UAVs). UAV spatial coordinates, determined via Global Positioning System (GPS) signals, are susceptible to cyberattacks due to unencrypted and unauthenticated transmissions with GPS spoofing being a significant threat. To mitigate these vulnerabilities, intrusion detection systems (IDSs) for UAVs have been developed and enhanced using machine learning (ML) algorithms. However, Adversarial Machine Learning (AML) has introduced new risks by exploiting ML models. This study presents a UAV-IDS employing AML methodology to enhance the detection and classification of GPS spoofing attacks. The key contribution is the development of an AML detection model that significantly improves UAV system robustness and security. Our findings indicate that the model achieves a detection accuracy of 98%, demonstrating its effectiveness in managing large-scale datasets and complex tasks. This study emphasizes the importance of physical layer security for enhancing IDSs in UAVs by introducing a novel detection model centered on an adversarial training defense method and advanced deep learning techniques. [ABSTRACT FROM AUTHOR]

In this paper, the authors present a cloud system that incorporate tools developed in HTML5 and JQuery technologies, which are offered to professors and students in the development of a teaching methodology called flipped classroom, where the theoretical content is usually delivered by video files and self-assessment tools that students can display from home. This system aims to eliminate the technical limitations to facilitate communication and a real learning process by monitoring and evaluating the students and teachers. Highlighting main functions, the authors do find: the wizard of quality content, visual interactive wizard, the audio note as tool for audio records, and finally the graphical evaluator that provides a graphical and intuitive result, providing a clear image of the theoretical and practical difficulties that students experience.

Wireless communication plays a crucial role in the automation process in the industrial environment. However, the open nature of wireless communication renders industrial wireless sensor networks susceptible to malicious attacks that impersonate authorized nodes. The heterogeneity of the wireless transmission channel, coupled with hardware and software limitations, further complicates the issue of secure authentication. This form of communication urgently requires a lightweight authentication technique characterized by low complexity and high security, as inadequately secure communication could jeopardize the evolution of industrial devices. These requirements are met through the introduction of physical layer authentication. This article proposes novel deep learning (DL) models designed to enhance physical layer authentication by autonomously learning from the frequency domain without relying on expert features. Experimental results demonstrate the effectiveness of the proposed models, showcasing a significant enhancement in authentication accuracy. Furthermore, the study explores the efficacy of various DL architecture settings and traditional machine learning approaches through a comprehensive comparative analysis. [ABSTRACT FROM AUTHOR]

Wireless physical layer authentication has emerged as a promising approach to wireless security. The topic of wireless node classification and recognition has experienced significant advancements due to the rapid development of deep learning techniques. The potential of using deep learning to address wireless security issues should not be overlooked due to its considerable capabilities. Nevertheless, the utilization of this approach in the classification of wireless nodes is impeded by the lack of available datasets. In this study, we provide two models based on a data-driven approach. First, we used generative adversarial networks to design an automated model for data augmentation. Second, we applied a convolutional neural network to classify wireless nodes for a wireless physical layer authentication model. To verify the effectiveness of the proposed model, we assessed our results using an original dataset as a baseline and a generated synthetic dataset. The findings indicate an improvement of approximately 19% in classification accuracy rate. [ABSTRACT FROM AUTHOR]

Detection of cyber‐threats in the smart grid Supervisory Control and Data Acquisition (SCADA) is still remains one of the complex and essential processes need to be highly concentrated in present times. Typically, the SCADA is more prone to the security issues due to their environmental problems and vulnerabilities. Therefore, the proposed work intends to design a new detection approach by integrating the optimization and classification models for smart grid SCADA security. In this framework, the min‐max normalization is performed at first for noise removal and attributes arrangement. Here, the correlation estimation mechanism is mainly deployed to reduce the dimensionality of features by choosing the relevant features used for attack prediction. Moreover, the optimal features are selected by using the optimal solution provided by the Holistic Harris Hawks Optimization (H3O). Finally, the Perceptron Stochastic Neural Network (PSNN) is utilized to categorize the normal and attacking data flow in the network with minimal processing time and complexity. By using the combination of proposed H3O‐PSNN technique, the detection accuracy is improved up to 99% for all datasets used in this study, and also other measures such as precision to 99.2%, recall to 99%, f1‐score to 99.2% increased, when compared to the standard techniques. [ABSTRACT FROM AUTHOR]

The massive Internet of Things (IoT) comprises different gateways (GW) covering a given region of a massive number of connected devices with sensors. In IoT networks, transmission interference is observed when different sensor devices (SD) try to send information to a single GW. This is mitigated by allotting various channels to adjoining GWs. Furthermore, SDs are permitted to associate with anyGWin a network, naturally choosing the one with a higher received signal strength indicator (RSSI), regardless of whether it is the ideal choice for network execution. Finding an appropriate GW to optimize the performance of IoT systems is a difficult task given the complicated conditions among GWs and SDs. Recently, in remote IoT networks, the utilization of machine learning (ML) strategies has arisen as a viable answer to determine the effect of various models in the system, and reinforcement learning (RL) is one of these ML techniques. Therefore, this paper proposes the use of an RL algorithm for GW determination and association in IoT networks. For this purpose, this study allows GWs and SDs with intelligence, through executing the multi-armed bandit (MAB) calculation, to investigate and determine the optimal GW with which to associate. In this paper, rigorous mathematical calculations are performed for this purpose and evaluate our proposed mechanism over randomly generated situations, which include different IoT network topologies. The evaluation results indicate that our intelligentMAB-based mechanism enhances the association as compared to state-of-the-art (RSSI-based) and related research approaches. [ABSTRACT FROM AUTHOR]

Rapid technological advancement has enabled modern healthcare systems to provide more sophisticated and real-time services on the Internet of Medical Things (IoMT). The existing cloud-based, centralized IoMT architectures are vulnerable to multiple security and privacy problems. The blockchain-enabled IoMT is an emerging paradigm that can ensure the security and trustworthiness of medical data sharing in the IoMT networks. This article presents a private and easily expandable blockchain-based framework for the IoMT. The proposed framework contains several participants, including private blockchain, hospitalmanagement systems, cloud service providers, doctors, and patients. Data security is ensured by incorporating an attributebased encryption scheme. Furthermore, an IoT-friendly consensus algorithm is deployed to ensure fast block validation and high scalability in the IoMT network. The proposed framework can perform multiple healthcare-related services in a secure and trustworthy manner. The performance of blockchain read/write operations is evaluated in terms of transaction throughput and latency. Experimental outcomes indicate that the proposed scheme achieved an average throughput of 857 TPS and 151 TPS for read and write operations. The average latency is 61 ms and 16 ms for read and write operations, respectively. [ABSTRACT FROM AUTHOR]

The emergence of Industry 5.0 has revolutionized technology by integrating physical systems with digital networks. These advancements have also led to an increase in cyber threats, posing significant risks, particularly for small and medium-sized enterprises (SMEs). This research investigates the resistance of SMEs in Saudi Arabia and the United Kingdom (UK) to cyber security measures within the context of Industry 5.0, with a specific focus on governance and policy. It explores the cultural and economic factors contributing to this resistance, such as limited awareness of cyber security risks, financial constraints, and competing business priorities. Additionally, the study examines the role of government policies and regulations in promoting cyber security practices among SMEs and compares the approaches adopted by Saudi Arabia and the UK. By employing a mixed methods analysis, including interviews with SME owners and experts, the research highlights challenges and opportunities for improving cyber security governance and policy in both countries. The findings emphasize the need for tailored solutions due to the differing cultural and economic contexts between Saudi Arabia and the UK. Specifically, the study delves into the awareness and implementation of cyber security measures, focusing on SMEs in Saudi Arabia and their adherence to the Essential Cyber Security Controls (ECC-1:2018) guidelines. Furthermore, it examines the existing cyber security awareness practices and compliance in the UK, while also comparing official guidance documents aimed at supporting SMEs in achieving better cyber security practices. Based on the analysis, greater engagement with these documents is recommended in both countries to foster awareness, confidence, and compliance among SMEs, ultimately enhancing their cyber security posture. This paper offers a comparative research study on governance and policy between Saudi Arabia and the UK, presenting a set of recommendations to strengthen cyber security awareness and education, fortify regulatory frameworks, and foster public–private partnerships to combat cyber security threats in the Industry 5.0 landscape. [ABSTRACT FROM AUTHOR]

SQL injection attacks are one of the most common types of attacks on Web applications. These attacks exploit vulnerabilities in an application's database access mechanisms, allowing attackers to execute unauthorized SQL queries. In this study, we propose an architecture for detecting SQL injection attacks using a recurrent neural network autoencoder. The proposed architecture was trained on a publicly available dataset of SQL injection attacks. Then, it was compared with several other machine learning models, including ANN, CNN, decision tree, naive Bayes, SVM, random forest, and logistic regression models. The experimental results showed that the proposed approach achieved an accuracy of 94% and an F1-score of 92%, which demonstrate its effectiveness in detecting QL injection attacks with high accuracy in comparison to the other models covered in the study. [ABSTRACT FROM AUTHOR]

Recently, the concept of e-commerce product review evaluation has become a research topic of significant interest in sentiment analysis. The sentiment polarity estimation of product reviews is a great way to obtain a buyer's opinion on products. It offers significant advantages for online shopping customers to evaluate the service and product qualities of the purchased products. However, the issues related to polysemy, disambiguation, and word dimension mapping create prediction problems in analyzing online reviews. In order to address such issues and enhance the sentiment polarity classification, this paper proposes a new sentiment analysis model, the Ensemble Random Forest-based XG boost (ERF-XGB) approach, for the accurate binary classification of online e-commerce product review sentiments. Two different Internet Movie Database (IMDB) datasets and the Chinese Emotional Corpus (ChnSentiCorp) dataset are used for estimating online reviews. First, the datasets are preprocessed through tokenization, lemmatization, and stemming operations. The Harris hawk optimization (HHO) algorithm selects two datasets' corresponding features. Finally, the sentiments from online reviews are classified into positive and negative categories regarding the proposed ERF-XGB approach. Hyperparameter tuning is used to find the optimal parameter values that improve the performance of the proposed ERF-XGB algorithm. The performance of the proposed ERF-XGB approach is analyzed using evaluation indicators, namely accuracy, recall, precision, and F1-score, for different existing approaches. Compared with the existing method, the proposed ERF-XGB approach effectively predicts sentiments of online product reviews with an accuracy rate of about 98.7% for the ChnSentiCorp dataset and 98.2% for the IMDB dataset. [ABSTRACT FROM AUTHOR]

Recently, cloud computing resources have become one of the trending technologies that permit the user to manage diverse resources and a huge amount of data in the cloud. Task scheduling is considered one of the most significant challenges and ineffective management results in performance degradation. It is necessary to schedule the task effectively with maximum resource utilization and minimum execution time. Therefore, this paper proposes a novel technique for effective task scheduling with enhanced security in the cloud computing environment. A novel convolutional neural network optimized modified butterfly optimization (CNN-MBO) algorithm is proposed for scheduling the tasks, thereby maximizing the throughput and minimizing the makespan. Secondly, a modified RSA algorithm is employed to encrypt the data, thereby providing secure data transmission. Finally, our proposed approach is simulated under a cloudlet simulator and the evaluation results are analyzed to determine its performance. In addition to this, the proposed approach is compared with various other task scheduling-based approaches for various performance metrics, namely, resource utilization, response time, as well as energy consumption. The experimental results revealed that the proposed approach achieved minimum energy consumption of 180 kWh, a minimum response time of the 20 s, a minimum execution time of 0.43 s, and maximum utilization of 98% for task size 100. [ABSTRACT FROM AUTHOR]

An SQL injection attack, usually occur when the attacker(s) modify, delete, read, and copy data from database servers and are among the most damaging of web application attacks. A successful SQL injection attack can affect all aspects of security, including confidentiality, integrity, and data availability. SQL (structured query language) is used to represent queries to database management systems. Detection and deterrence of SQL injection attacks, for which techniques from different areas can be applied to improve the detect ability of the attack, is not a new area of research but it is still relevant. Artificial intelligence and machine learning techniques have been tested and used to control SQL injection attacks, showing promising results. The main contribution of this paper is to cover relevant work related to different machine learning and deep learning models used to detect SQL injection attacks. With this systematic review, we aims to keep researchers up-to-date and contribute to the understanding of the intersection between SQL injection attacks and the artificial intelligence field. [ABSTRACT FROM AUTHOR]

Recently, the Internet of Things (IoT) has gained tremendous popularity in several realms such as smart cities, healthcare, industrial automation, etc. IoT networks are increasing rapidly, containing heterogeneous devices that offer easy and user-friendly services via the internet. With the big shift to IoT technology, the security of IoT networks has become a primary concern, especially with the lack of intrinsic security mechanisms regarding the limited capabilities of IoT devices. Therefore, many studies have been interested in enhancing the security of IoT networks. IoT networks need a scalable, decentralized, and adaptive defense system. Although the area of development provides advanced security solutions using AI and Blockchain, there is no systematic and comprehensive study talking about the convergence between AI and Blockchain to secure IoT networks. In this paper, we focus on reviewing and comparing recent studies that have been proposed for detecting cybersecurity attacks in IoT environments. This paper address three research questions and highlights the research gaps and future directions. This paper aims to increase the knowledge base for enhancing IoT security, recommend future research, and suggest directions for future research. [ABSTRACT FROM AUTHOR]

Adversarial machine learning is a recent area of study that explores both adversarial attack strategy and detection systems of adversarial attacks, which are inputs specially crafted to outwit the classification of detection systems or disrupt the training process of detection systems. In this research, we performed two adversarial attack scenarios, we used a Generative Adversarial Network (GAN) to generate synthetic intrusion traffic to test the influence of these attacks on the accuracy of machine learning-based Intrusion Detection Systems(IDSs). We conducted two experiments on adversarial attacks including poisoning and evasion attacks on two different types of machine learning models: Decision Tree and Logistic Regression. The performance of implemented adversarial attack scenarios was evaluated using the CICIDS2017 dataset. Also, it was based on a comparison of the accuracy of machine learning-based IDS before and after attacks. The results show that the proposed evasion attacks reduced the testing accuracy of both network intrusion detection systems models (NIDS). That illustrates our evasion attack scenario negatively affected the accuracy of machine learning-based network intrusion detection systems, whereas the decision tree model was more affected than logistic regression. Furthermore, our poisoning attack scenario disrupted the training process of machine learning-based NIDS, whereas the logistic regression model was more affected than the decision tree. [ABSTRACT FROM AUTHOR]

In recent years, educational institutions have worked hard to automate their work using more trending technologies that prove the success in supporting decision-making processes. Most of the decisions in educational institutions rely on rating the academic research profiles of their staff. An enormous amount of scholarly data is produced continuously by online libraries that contain data about publications, citations, and research activities. This kind of data can change the accuracy of the academic decisions, if linked with the local data of universities. The linked data technique in this study is applied to generate a link between university semantic data and a scientific knowledge graph, to enrich the local data and improve academic decisions. As a proof of concept, a case study was conducted to allocate the best academic staff to teach a course regarding their profile, including research records. Further, the resulting data are available to be reused in the future for different purposes in the academic domain. Finally, we compared the results of this link with previous work, as evidence of the accuracy of leveraging this technology to improve decisions within universities. [ABSTRACT FROM AUTHOR]

Epilepsy is a nervous system disorder. Encephalography (EEG) is a generally utilized clinical approach for recording electrical activity in the brain. Although there are a number of datasets available, most of them are imbalanced due to the presence of fewer epileptic EEG signals compared with non-epileptic EEG signals. This research aims to study the possibility of integrating local EEG signals from an epilepsy center in King Abdulaziz University hospital into the CHB-MIT dataset by applying a new compatibility framework for data integration. The framework comprises multiple functions, which include dominant channel selection followed by the implementation of a novel algorithm for reading XLtek EEG data. The resulting integrated datasets, which contain selective channels, are tested and evaluated using a deep-learning model of 1D-CNN, Bi-LSTM, and attention. The results achieved up to 96.87% accuracy, 96.98% precision, and 96.85% sensitivity, outperforming the other latest systems that have a larger number of EEG channels. [ABSTRACT FROM AUTHOR]

Teaching offensive security (ethical hacking) is becoming a required component of information security curricula to develop better cybersecurity practitioners. Many academics and industry professionals believe that a good knowledge of the attacks a system can face is required to protect a system. The early detection of an attack is critical to effectively defending a system. We can't wait for threats to be discovered in the wild to begin planning our defenses. For our study, we designed and developed an offensive model that aims to remain concealed in an image until it reaches the target location. Our attack approach exploits image steganography, which involves embedding malicious code and a geolocation code into a digital image. This study aimed to discover new ways to attack computer systems and stimulate awareness of such attacks among browser developers, thus encouraging them to handle images with more care. In our experiments, both stego-image analysis and geolocation techniques are tested. Our experience has confirmed that converting indiscriminate attacks into targeted attacks is possible. [ABSTRACT FROM AUTHOR]

Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times. Very few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this topic. We are using Deep Convolutional Generative Adversarial Networks (DCGAN) to trick the malware classifier to believe it is a normal entity. In this work, a new dataset is created to fool the Artificial Intelligence (AI) based malware detectors, and it consists of different types of attacks such as Denial of Service (DoS), scan 11, scan 44, botnet, spam, User Datagram Portal (UDP) scan, and ssh scan. The discriminator used in the DCGAN discriminates two different attack classes (anomaly and synthetic) and one normal class. The model collapse, instability, and vanishing gradient issues associated with the DCGAN are overcome using the proposed hybrid Aquila optimizer-based Mine blast harmony search algorithm (AO-MBHS). This algorithm helps the generator to create realistic malware samples to be undetected by the discriminator. The performance of the proposed methodology is evaluated using different performance metrics such as training time, detection rate, F-Score, loss function, Accuracy, False alarm rate, etc. The superiority of the hybrid AO-MBHS based DCGAN model is noticed when the detection rate is changed to 0 after the retraining method to make the defensive technique hard to be noticed by the malware detection system. The support vector machines (SVM) is used as the malicious traffic detection application and its True positive rate (TPR) goes from 80% to 0% after retraining the proposed model which shows the efficiency of the proposed model in hiding the samples. [ABSTRACT FROM AUTHOR]

The Deep Neural Networks (DNN) training process is widely affected by backdoor attacks. The backdoor attack is excellent at concealing its identity in the DNN by performing well on regular samples and displaying malicious behavior with data poisoning triggers. The state-of-art backdoor attacks mainly follow a certain assumption that the trigger is sample-agnostic and different poisoned samples use the same trigger. To overcome this problem, in this work we are creating a backdoor attack to check their strength to withstand complex defense strategies, and in order to achieve this objective, we are developing an improved Convolutional Neural Network (ICNN) model optimized using a Gradient-based Optimization (GBO)(ICNN-GBO) algorithm. In the ICNN-GBO model, we are injecting the triggers via a steganography and regularization technique. We are generating triggers using a single-pixel, irregular shape, and different sizes. The performance of the proposed methodology is evaluated using different performance metrics such as Attack success rate, stealthiness, pollution index, anomaly index, entropy index, and functionality. When the CNN-GBO model is trained with the poisoned dataset, it will map the malicious code to the target label. The proposed scheme's effectiveness is verified by the experiments conducted on both the benchmark datasets namely CIDAR-10 and MSCELEB 1M dataset. The results demonstrate that the proposed methodology offers significant defense against the conventional backdoor attack detection frameworks such as STRIP and Neutral cleanse. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are spatially distributed to independent sensor networks that can sense physical characteristics such as temperature, sound, pressure, energy, and so on. WSNs have secure link to physical environment and robustness. Data Aggregation (DA) plays a key role in WSN, and it helps to minimize the Energy Consumption (EC). In order to have trustworthy DA with a rate of high aggregation for WSNs, the existing research works have focused on Data Routing for In-Network Aggregation (DRINA). Yet, there is no accomplishment of an effective balance between overhead and routing. But EC required during DA remained unsolved. The detection of objects belonging to the same event into specific regions by the Bayes Node is distributed through the Sensor Nodes (SNs). Multi-Sensor Data Synchronization Scheduling (MSDSS) framework is proposed for efficient DA at the sink in a heterogeneous sensor network. Secure and Energy-Efficient based In-Network Aggregation Sensor Data Routing (SEE-INASDR) is developed based on the Dynamic Routing (DR) structure with reliable data transmission in WSNs. Theoretical analysis and experimental results showed that in WSN, the proposed Bayes Node Energy Polynomial Distribution (BNEPD) technique reduced Energy Drain Rate (EDR) by 39% and reduced 33% of Communication Overhead (CO) using poly distribution algorithm. Similarly, the proposed MSDSS framework increased the Network Lifetime (NL) by 15%. This framework also increased 10.5% of Data Aggregation Routing (DAR). Finally, the SEE-INASDR framework significantly reduced EC by 51% using a Secure and Energy-Efficient Routing Protocol (SEERP). [ABSTRACT FROM AUTHOR]

Recent years have seen a lot of interest in the study of chaotic structures and their accompanying cryptography frameworks. In this research, we came up with a new way to encrypt images that used the chaos and a modified block cipher named the SHARK cipher. The new algorithm looks at the creation of random sequences as a problem that needs to be solved in the best way possible, and then it uses the Duffing chaotic map to get even better random sequences. Chaos has been combined with a revised edition of the SHARK structure to make the algorithm design more robust with increased confusion and diffusion. The offered algorithm includes a complex encryption and decryption structure with minimal time consumption for secure data transmission. The proposed algorithm is verified with the encryption of some standard images of different sizes. Numerous analyses have been performed to see how well the algorithm works against a variety of assaults, and the outcomes show that the cryptosystem has a good level of robustness. The comparative results are also performed in this work, which guarantees the excellent performance of our cryptosystem. The system is also subjected to chosen-plaintext and chosen-ciphertext attacks which implies that it can resist many classical cryptographic attacks. Therefore, our cryptosystem is robust enough to use for image encryption. [ABSTRACT FROM AUTHOR]

Applications based on Wireless Sensor Networks (WSN) have shown to be quite useful in monitoring a particular geographic area of interest. Relevant geometries of the surrounding environment are essential to establish a successful WSN topology. But it is literally hard because constructing a localization algorithm that tracks the exact location of Sensor Nodes (SN) in a WSN is always a challenging task. In this research paper, Distance Matrix and Markov Chain (DM-MC) model is presented as node localization technique in which Distance Matrix and Estimation Matrix are used to identify the position of the node. The method further employs a Markov Chain Model (MCM) for energy optimization and interference reduction. Experiments are performed against two well-known models, and the results demonstrate that the proposed algorithm improves performance by using less network resources when compared to the existing models. Transition probability is used in the Markova chain to sustain higher energy nodes. Finally, the proposed Distance Matrix and Markov Chain model decrease energy use by 31% and 25%, respectively, compared to the existing DV-Hop and CSA methods. The experimental results were performed against two proven models, Distance Vector-Hop Algorithm (DV-HopA) and Crow Search Algorithm (CSA), showing that the proposed DM-MC model outperforms both the existing models regarding localization accuracy and Energy Consumption (EC). These results add to the credibility of the proposed DC-MC model as a better model for employing node localization while establishing a WSN framework. [ABSTRACT FROM AUTHOR]

Given the increase in online shopping and the prevailing research gap, this study designed and implemented an online shopping system for Gulf brands. It presents an online customization feature-based design and implementation concerning the case of GhazzawiGowns. The study used the B/S model (browser/server) to design and implement an online shopping system for Gulf brands. This system was developed because online shopping is increasing in the Gulf. Users were asked to provide their input concerning the services and website usage. Feedback showed that the developed system could effectively meet customers' online shopping needs. However, only a few agreed that Gulf brands provided customization. This highlights that very few services are being offered online that give users a choice to customize products. The provided functions and descriptions of the system were user-friendly and provided greater accessibility to address various design issues. However, the webpage needs to be updated to improve offerings and services. [ABSTRACT FROM AUTHOR]

Based on the improved BP neural network, this paper establishes an adaptive online controlling model and adopts the model to optimize the controlling accuracies in discrete nonlinear dynamic systems and inverted pendulum systems. To avoid the local minimum problem of the BP neural network's objective function in the training process, this paper proposes a neural network training method based on the quasi-Newton method (BFGS) optimization algorithm. Compared with other control methods, the neural network-based inverted pendulum control method proposed in this paper has higher control accuracy. Through the control simulation of its power system uses a discrete control method and the control of the inverted pendulum model system, this paper verifies the validity and good control has significantly improved the control method. [ABSTRACT FROM AUTHOR]