Volume 20, Issue 1

Cybersecurity awareness has a huge impact on individuals and an even bigger impact on firms, universities, and institutes to those individuals belong. Consequently, it is essential to explore and asses the factors affecting the awareness level of cybersecurity. More specifically this research study examines the impact of demographic features of individuals on cybersecurity awareness. The Studied literature’s limitations have been addressed and overcome in our research from the variability, and ambiguity aspects. A questionnaire was developed and responses were collected from 613 participants. Reliability and validity tests as well as correlations have been applied for the instruments and data employed in this study. Coefficients were calculated via multiple linear regression for the weights of each of the cybersecurity components. Data reliability test showed that Cronbach’s Alpha value of 0.707 for the used data which is acceptable for research purposes. Results analysis showed r-value for each of the questions is greater than the r table which was 0.07992. Examining the proposed hypotheses showed that there is a difference as the null hypothesis is rejected for one of the demographic features being tested namely, gender. While there is no significant difference when it comes to the other two factors, education level, and age. Using the weight for each of the components, password security, technical behavior, and social influence could provide a solid base for decision-makers to focus on and implement the available resources for gender-specific developments to raise the cybersecurity awareness level.

Searchable symmetric encryption (SSE) is a robust cryptographic method that allows users to store and retrieve encrypted data on a remote server, such as a cloud server, while maintaining the privacy of the user’s data. The technique employs symmetric encryption, which utilizes a single secret key for both data encryption and decryption. However, extensive research in this field has revealed that SSE encounters performance issues when dealing with large databases. Upon further investigation, it has become apparent that the issue is due to poor locality, necessitating that the cloud server access multiple memory locations for a single query. Additionally, prior endeavors in this domain centered on locality optimization have often led to expanded storage requirements (the stored encrypted index should not be substantially larger than the original index) or diminished data retrieval efficiency (only required data should be retrieved).we present a simple, secure, searchable, and cost-effective scheme, which addresses the aforementioned problems while achieving a significant improvement in information retrieval performance through site optimization by changing the encrypted inverted index storage mechanism. The proposed scheme has the optimal locality O(1) and the best read efficiency O(1)with no significant negative impact on the storage space, which often increases due to the improvement of the locality. Using real-world data, we demonstrate that our scheme is secure, practical, and highly accurate. Furthermore, our proposed work can resist well-known attacks such as keyword guessing attacks and frequency analysis attacks.

This article presents a developed intensity modulation/direct detection (IM/DD) optical orthogonal frequency division multiplexing (O-OFDM). More precisely, the presented C-O-OFDM is based on the C-transform as a unitary orthogonal transform instead of the state-of-the-art discrete Fourier transform (DFT). Due to the properties of the real C-transform, Hermitian symmetry (HS) is not required to produce real OFDM samples. Therefore, the proposed scheme supports twice the input symbols compared to conventional DFT-based OFDM system. Real data mapping and DC bias technology is considered to evaluate the performance of the presented scheme over optical wireless multipath. The simulation results shows that the proposed C-O-OFDM is more resilience to multipath phenomena than the competitive DFT-O-OFDM and DHT-O-OFDM schemes for similar bit rate. The proposed scheme achieves about 22 dB signal-to-noise ratio (SNR) gain in comparison with the DFT-O-OFDM and about 2.5 dB SNR gain in comparison with the DHT-O-OFDM scheme.

Facial retouching, also referred to as digital retouching, is the process of modifying or enhancing facial characteristics in digital images or photographs. While it can be a valuable technique for fixing flaws or achieving a desired visual appeal, it also gives rise to ethical considerations. This study involves categorizing genuine and retouched facial images from the standard ND-IIITD retouched faces dataset using a transfer learning methodology. The impact of different primary optimization algorithms—specifically Adam, RMSprop, and Adadelta—utilized in conjunction with a fine-tuned ResNet50 model is examined to assess potential enhancements in classification effectiveness. Our proposed transfer learning ResNet50 model demonstrates superior performance compared to other existing approaches, particularly when the RMSprop and Adam optimizers are employed in the fine-tuning process. By training the transfer learning ResNet50 model on the ND-IIITD retouched faces dataset with the “ImageNet” weight, we achieve a validation accuracy of 98.76%, a training accuracy of 98.32%, and an overall accuracy of 98.52% for classifying real and retouched faces in just 20 epochs. Comparative analysis indicates that the choice of optimizer during the fine-tuning of the transfer learning ResNet50 model can further enhance the classification accuracy.

Studies indicate cardiac arrhythmia is one of the leading causes of death in the world. The risk of a stroke may be reduced when an irregular and fast heart rate is diagnosed. Since it is non-invasive, electrocardiograms are often used to detect arrhythmias. Human data input may be error-prone and time-consuming because of these limitations. For early detection of heart rhythm problems, it is best to use deep learning models. In this paper, a hybrid bio-inspired algorithm has been proposed by combining whale optimization (WOA) with adaptive particle swarm optimization (APSO). The WOA is a recently developed meta-heuristic algorithm. APSO is used to increase convergence speed. When compared to conventional optimization methods, the two techniques work better together. MIT-BIH dataset has been utilized for training, testing and validating this model. The recall, accuracy, and specificity are used to measure efficiency of the proposed method. The efficiency of the proposed method is compared with state-of-art methods and produced 98.25 % of accuracy.

Nowadays, it is difficult to imagine a powerful algorithm of cryptography that can continue cryptanalyzing and attacking without the use of unconventional techniques. Although some of the substitution algorithms are old, such as Vigenère, Alberti, and Trithemius ciphers, they are considered powerful and cannot be broken. In this paper we produce the novelty algorithm, by using of biological computation as an unconventional search tool combined with an uninhibited analysis method is the vertical probabilistic model, that makes attacking and analyzing these ciphers possible and very easy to transform the problem from a complex to a linear one, which is a novelty achievement. The letters of the encoded message are processed in the form of segments of equal length, to report the available hardware components. Each letter codon represents a region of the memory strand, and the letters calculated for it are symbolized within the probabilistic model so that each pair has a triple encoding: the first is given as a memory strand encoding and the others are its complement in the sticker encoding; These encodings differ from one region to another. The solution space is calculated and then the parallel search process begins. Some memory complexities are excluded even though they are within the solution paths formed, because the natural language does not contain its sequences. The precision of the solution and the time consuming of access to it depend on the length of the processed text, and the precision of the solution is often inversely proportional to the speed of access to it. As an average of the time spent to reach the solution, a text with a length of 200 cipher characters needs approximately 15 minutes to give 98 % of the correct components of the specific hardware. The aim of the paper is to transform OTP substitution analysis from a NP problem to a Ο(n^m) problem, which makes it easier to find solutions to it easily with the available capabilities and to develop methods that are harnessed to attack difficult and powerful ciphers that differ in class and type from the OTP polyalphabetic substitution ciphers.

This study proposes a blind speech separation algorithm that employs a single-channel technique. The algorithm’s input signal is a segment of a mixture of speech for two speakers. At first, filter bank analysis transforms the input from time to time-frequency domain (spectrogram). Number of sub-bands for the filter is 257. Non-Negative Matrix Factorization (NNMF) factorizes each sub-band output into 28 sub-signals. A binary mask separates each sub-signal into two groups; one group belongs to the first speaker and the other to the second speaker. The binary mask separates each sub-signal of the (257×28) 7196 sub-speech signals. That separation cannot identify the speaker. Identification of the sub-signal speaker for each sub-signal is achieved by speaker clustering algorithms. Since speaker clustering cannot process without speaker segmentation, the standard windowed-overlap frames have been used to partition the speech. The speaker clustering process fetches the extracted phase angle from the spectrogram (of the mixture speech) and merges it into the spectrogram (of the recovered speech). Filter bank synthesizes these signals to produce a full-band speech signal for each speaker. Subjective tests denote that the algorithm results are accepted. Objectively, the researchers experimented with 66 mixture chats (6 females and 6 males) to test the algorithm. The average of the SIR test is 11.1 dB, SDR is 1.7 dB, and SAR is 2.8 dB.

Soft robots, which are often considered safer than rigid robots when interacting with humans due to the reduced risk of injury, have found utility in various medical and industrial fields. Pneumatic artificial muscles (PAMs), one of the most widely used soft actuators, have proven their efficiency in numerous applications, including prosthetic and rehabilitation robots. PAMs are lightweight, responsive, precise, and capable of delivering a high force-to-weight ratio. Their structure comprises a flexible, inflatable membrane reinforced with fibrous twine and fitted with gas-sealing fittings. For the optimal design and integration of these into control systems, it is crucial to develop mathematical models that accurately represent their functioning mechanisms. This paper introduces a general concept of PAM’s construction, its various types, and operational mechanisms, along with its key benefits and drawbacks, and also reviews the most common modeling techniques for PAM representation. Most models are grounded in PAM architecture, aiming to calculate the actuator’s force across its full axis by correlating pressure, length, and other parameters that influence actuator strength.

Given the role that pipelines play in transporting crude oil, which is considered the basis of the global economy and across different environments, hundreds of studies revolve around providing the necessary protection for it. Various technologies have been employed in this pursuit, differing in terms of cost, reliability, and efficiency, among other factors. Computer vision has emerged as a prominent technique in this field, albeit requiring a robust image-processing algorithm for spill detection. This study employs image segmentation techniques to enable the computer to interpret visual information and images effectively. The research focuses on detecting spills in oil pipes caused by leakage, utilizing images captured by a drone equipped with a Raspberry Pi and Pi camera. These images, along with their global positioning system (GPS) location, are transmitted to the base station using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol. At the base station, deep learning techniques, specifically Holistically-Nested Edge Detection (HED) and extreme inception (Xception) networks, are employed for image processing to identify contours. The proposed algorithm can detect multiple contours in the images. To pinpoint a contour with a black color, representative of an oil spill, the CIELAB color space (LAB) algorithm effectively removes shadow effects. If a contour is detected, its area and perimeter are calculated to determine whether it exceeds a certain threshold. The effectiveness of the proposed system was tested on Iraqi oil pipeline systems, demonstrating its capability to detect spills of different sizes.

Object detection has become faster and more precise due to improved computer vision systems. Many successful object detections have dramatically improved owing to the introduction of machine learning methods. This study incorporated cutting- edge methods for object detection to obtain high-quality results in a competitive timeframe comparable to human perception. Object-detecting systems often face poor performance issues. Therefore, this study proposed a comprehensive method to resolve the problem faced by the object detection method using six distinct machine learning approaches: stochastic gradient descent, logistic regression, random forest, decision trees, k-nearest neighbor, and naive Bayes. The system was trained using Common Objects in Context (COCO), the most challenging publicly available dataset. Notably, a yearly object detection challenge is held using COCO. The resulting technology is quick and precise, making it ideal for applications requiring an object detection accuracy of  97%.

In different modern and future wireless communication networks, a large number of low-power user equipment (UE) devices like Internet of Things, sensor terminals, and smart modules have to be supported over constrained power and bandwidth resources. Therefore, wireless-powered communication (WPC) is considered a promising technology for varied applications in which the energy harvesting (EH) from radio frequency radiations is exploited for data transmission. This requires efficient resource allocation schemes to optimize the performance of WPC and prolong the network lifetime. In this paper, harvest-then-transmit-based WP non-orthogonal multiple access (WP-NOMA) system is designed with time-split (TS) and power control (PC) allocation strategies. To evaluate the network performance, the sum rate and UEs’ rates expressions are derived considering power-domain NOMA with successive interference cancellation detection. For comparison purposes, the rate performance of the conventional WP orthogonal multiple access (WP-OMA) is derived also considering orthogonal frequency-division multiple access and time-division multiple access schemes. Intensive investigations are conducted to obtain the best TS and PC resource parameters that enable maximum EH for higher data transmission rates compared with the reference WP-OMA techniques. The achieved outcomes demonstrate the effectiveness of designed resource allocation approaches in terms of the realized sum rate, UE’s rate, rate region, and fairness without distressing the restricted power of far UEs.

Energy exchange between AC grid and DC supply that is a part of a hybrid electric micro-grid takes place using various power converter designs. The single-phase, single-stage, AC-DC power dual active bridge converter is one option. The phase-shift modulation is used to regulate energy flow in both directions. The topology of one stage AC-DC dual active bridge converter based in bidirectional switching modules has been introduced. This paper next introduces the analysis of the AC side current considering basic modulation functions and suggests an optimum phase-shifted modulation strategy. The proposed modulation function provides minimum harmonics distortion. A simulation study is presented to compare the proposed strategy to the basic sinusoidal and triangular modulation techniques. The results show that the modified modulation reduces the average THD by about 55% and 39% compared to the standard sinusoidal and triangular modulation strategies respectively and ensures linear relationship between the transferred power and magnitude control coefficient.

The coordination of overcurrent relay protection in the power framework is crucial for preserving electrical distribution systems. It ensures that both primary and backup protection are provided to the system. It is essential to maintain a minimal level of coordination between these relays in order to reduce the overall running time and guarantee that power outages and damage are kept to a minimum under fault conditions. Proper coordination between the primary and back-up relays can minimize the operation duration of overcurrent with instantaneous and earth fault relays by selecting the optimum TMS (Time Multiplier Setting) and PS (Plug Setting). The present study investigates the difficulty associated with determining the TMS and PS values of earth-fault and overcurrent relays at the 33/11 kV power distribution substation in Basra using the instantaneous setting element. Overcurrent and earth fault relays were simulated in two scenarios: one with a time delay setting and one with an immediate setting. This procedure was carried out to generate Time Current Characteristics (TCC) curves for each Circuit Breaker (CB) relay took place in the Nathran substation, which has a capacity of 2×31.5 MVA and operates at a voltage level of 33/11 kV. The substation is a part of the Basrah distribution network. The short circuit current is estimated at each circuit breaker (CB), followed by the simulation of protection coordination for the Nathran substation using the DIgSILENT Power Factory software. This research is based on real data collection, and the setting considers the short-circuit current at the farthest point of the longest feeders. The results show the effectiveness of the proposed coordination scheme, which reduced trip operation time by 20% compared to the presented case study while maintaining coordination between primary and backup protection.

Video prediction theories have quickly progressed especially after a great revolution of deep learning methods. The prediction architectures based on pixel generation produced a blurry forecast, but it is preferred in many applications because this model is applied on frames only and does not need other support information like segmentation or flow mapping information making getting a suitable dataset very difficult. In this approach, we presented a novel end-to-end video forecasting framework to predict the dynamic relationship between pixels in time and space. The 3D CNN encoder is used for estimating the dynamic motion, while the decoder part is used to reconstruct the next frame based on adding 3DCNN CONVLSTM2D in skip connection. This novel representation of skip connection plays an important role in reducing the blur predicted and preserved the spatial and dynamic information. This leads to an increase in the accuracy of the whole model. The KITTI and Cityscapes are used in training and Caltech is applied in inference. The proposed framework has achieved a better quality in PSNR=33.14, MES=0.00101, SSIM=0.924, and a small number of parameters (2.3 M).

In this paper, an analysis of performance acceleration of an external laser source (ELS) model based polymer fiber gratings (PFGs) by reducing the turn-on delay time (TDelay) is successfully investigated numerically by optimizing model parameters. In contrast to all previous studies that relied either on approximate or experimental equations, the analysis was based on an exact numerical formula. The analysis is based on the investigation of the effect of diode injected current (Iin j), temperature (T), recombination rate coefficients (i.e. Anr, B, and C), and optical feedback (OFB) level.
Results have demonstrated that by optimizing model parameters the Delay can be controlled and reduced effectively.

The phenomenal rise of the Internet in recent years, as well as the expansion of capacity in today’s networks, have provided both inspiration and incentive for the development of new services that combine phone, video, and text “over IP.” Although unicast communications have been prevalent in the past, there is an increasing demand for multicast communications from both Internet Service Providers (ISPs) and content or media providers and distributors. Indeed, multicasting is increasingly being used as a green verbal exchange mechanism for institution-oriented programmers on the Internet, such as video conferencing, interactive college games, video on demand (VoD), TV over the Internet, e-learning, software programme updates, database replication, and broadcasting inventory charges. However, the lack of security within the multicast verbal exchange model prevents the effective and large-scale adoption of such important company multi-celebration activities. This situation prompted a slew of research projects that addressed a variety of issues related to multicast security, including confidentiality, authentication, watermarking, and access control. These issues should be viewed within the context of the safety regulations that work in the specific conditions. For example, in a public inventory charge broadcast, while identification is a vital necessity, secrecy is not. In contrast, video-convention programme requires both identification and confidentiality. This study gives a complete examination and comparison of the issues of group key management. Both network-dependent and network-independent approaches are used. The study also addresses the advantages, disadvantages, and security problems of various protocols.

This article introduces a novel Quantum-inspired Future Search Algorithm (QFSA), an innovative amalgamation of the classical Future Search Algorithm (FSA) and principles of quantum mechanics. The QFSA was formulated to enhance both exploration and exploitation capabilities, aiming to pinpoint the optimal solution more effectively. A rigorous evaluation was conducted using seven distinct benchmark functions, and the results were juxtaposed with five renowned algorithms from existing literature. Quantitatively, the QFSA outperformed its counterparts in a majority of the tested scenarios, indicating its superior efficiency and reliability. In the subsequent phase, the utility of QFSA was explored in the realm of fault detection in underground power cables. An Artificial Neural Network (ANN) was devised to identify and categorize faults in these cables. By integrating QFSA with ANN, a hybrid model, QFSA-ANN, was developed to optimize the network’s structure. The dataset, curated from MATLAB simulations, comprised diverse fault types at varying distances. The ANN structure had two primary units: one for fault location and another for detection. These units were fed with nine input parameters, including phase- currents and voltages, current and voltage values from zero sequences, and voltage angles from negative sequences. The optimal architecture of the ANN was determined by varying the number of neurons in the first and second hidden layers and fine-tuning the learning rate. To assert the efficacy of the QFSA-ANN model, it was tested under multiple fault conditions. A comparative analysis with established methods in the literature further accentuated its robustness in terms of fault detection and location accuracy. this research not only augments the field of search algorithms with QFSA but also showcases its practical application in enhancing fault detection in power distribution systems. Quantitative metrics, detailed in the main article, solidify the claim of QFSA-ANN’s superiority over conventional methods.

Early in the 20th century, as a result of technological advancements, the importance of digital marketing significantly increased as the necessity for digital customer experience, promotion, and distribution emerged. Since the year 1988, in the case when the term “Digital Marketing” first appeared, the business sector has undergone drastic growth, moving from small startups to massive corporations on a global scale. The marketer must navigate a chaotic environment caused by the vast volume of generated data. Decision-makers must contend with the fact that user data is dynamic and changes every day. Smart applications must be used within enterprises to better evaluate, classify, enhance, and target audiences. Customers who are tech-savvy are pushing businesses to make bigger financial investments and use cutting-edge technologies. It was only natural that marketing and trade could be one of the areas to move to such development, which helps to move to the speed of spread, advertisements, along with other things to facilitate things for reaching and winning customers. In this study, we utilized machine learning (ML) algorithms (Decision tree (DT), K-Nearest Neighbor (KNN), CatBoost, and Random Forest (RF) (for classifying data in customers to move to development. Improve the ability to forecast customer behavior so one can gain more business from them more quickly and easily. With the use of the aforementioned dataset, the suggested system was put to the test. The results show that the system can accurately predict if a customer will buy something or not; the random forest (RF) had an accuracy of 0.97, DT had an accuracy of 0. 95, KNN had an accuracy of 0. 91, while the CatBoost algorithm had the execution time 15.04 of seconds, and gave the best result of highest f1_score and accuracy (0.91, 0. 98) respectively. Finally, the study’s future goals involve being created a web page, thereby helping many banking institutions with speed and forecast accuracy. Using more techniques of feature selection in conjunction with the marketing dataset to improve diagnosis.

The occurrence of Sub-Synchronous Resonance (SSR) phenomena can be attributed to the interaction that takes place between wind turbine generators and series-compensated transmission lines. The Doubly-Fed Induction Generator (DFIG) is widely recognized as a prevalent generator form employed in wind energy conversion systems. The present paper commences with an extensive exposition on modal analysis techniques employed in a series of compensated wind farms featuring Doubly Fed Induction Generators (DFIGs). The system model encompasses various components, including the aerodynamics of a wind turbine, an induction generator characterized by a sixth-order model, a second-order two-mass shaft system, a series compensated transmission line described by a fourth-order model, controllers for the Rotor-Side Converter (RSC) and the Grid-Side Converter (GSC) represented by an eighth-order model, and a first-order DC-link model. The technique of eigenvalue-based SSR analysis is extensively utilized in various academic and research domains. The eigenvalue technique depends on the initial conditions of state variables to yield an accurate outcome. The non-iterative approach, previously employed for the computation of initial values of the state variables, has exhibited issues with convergence, lack of accuracy, and excessive computational time. The comparative study evaluates the time-domain simulation outcomes under different wind speeds and compensation levels, along side the eigenvalue analysis conducted using both the suggested and non-iterative methods. This comparative analysis is conducted to illustrate the proposed approach efficacy and precision. The results indicate that the eigenvalue analysis conducted using the proposed technique exhibits more accuracy, as it aligns with the findings of the simulations across all of the investigated instances. The process of validation is executed with the MATLAB program. Within the context of the investigation, it has been found that increasing compensation levels while simultaneously decreasing wind speed leads to system instability. Therefore, modifying the compensation level by the current wind speed is advisable.

Clustering is a fundamental data analysis task that presents challenges. Choosing proper initialization centroid techniques is critical to the success of clustering algorithms, such as k-means. The current work investigates six established methods (random, Forgy, k-means++, PCA, hierarchical clustering, and naive sharding) and three innovative swarm intelligence-based approaches—Spider Monkey Optimization (SMO), Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)—for k-means clustering (SMOKM, WOAKM, and GWOKM). The results on ten well-known datasets strongly favor swarm intelligence-based techniques, with SMOKM consistently outperforming WOAKM and GWOKM. This finding provides critical insights into selecting and evaluating centroid techniques in k-means clustering. The current work is valuable because it provides guidance for those seeking optimal solutions for clustering diverse datasets. Swarm intelligence, especially SMOKM, effectively generates distinct and well-separated clusters, which is valuable in resource-constrained settings. The research also sheds light on the performance of traditional methods such as hierarchical clustering, PCA, and k-means++, which, while promising for specific datasets, consistently underperform swarm intelligence-based alternatives. In conclusion, the current work contributes essential insights into selecting and evaluating initialization centroid techniques for k-means clustering. It highlights the superiority of swarm intelligence, particularly SMOKM, and provides actionable guidance for addressing various clustering challenges.