Reviewerly/exHarmony huggingface dataset:
Ensembles of Convolutional Neural Networks and Transformers for Polyp Segmentation

In the realm of computer vision, semantic segmentation is the task of recognizing objects in images at the pixel level. This is done by performing a classification of each pixel. The task is complex and requires sophisticated skills and knowledge about the context to identify objects' boundaries. The importance of semantic segmentation in many domains is undisputed. In medical diagnostics, it simplifies the early detection of pathologies, thus mitigating the possible consequences. In this work, we provide a review of the literature on deep ensemble learning models for polyp segmentation and develop new ensembles based on convolutional neural networks and transformers. The development of an effective ensemble entails ensuring diversity between its components. To this end, we combined different models (HarDNet-MSEG, Polyp-PVT, and HSNet) trained with different data augmentation techniques, optimization methods, and learning rates, which we experimentally demonstrate to be useful to form a better ensemble. Most importantly, we introduce a new method to obtain the segmentation mask by averaging intermediate masks after the sigmoid layer. In our extensive experimental evaluation, the average performance of the proposed ensembles over five prominent datasets beat any other solution that we know of. Furthermore, the ensembles also performed better than the state-of-the-art on two of the five datasets, when individually considered, without having been specifically trained for them.
Enhanced marine predator algorithm for global optimization and engineering design problems

Marine predator algorithm adopts the policy of optimal encounter rate in biological interaction between predator and prey, inspired by the Lévy and Brownian motions commonly used in oceanic predators. However, the Marine predator algorithm faces problems of premature convergence, poor search capability, and stagnation in a local optimum. In order to tolerate these shortcomings, two different strategies are applied to Marine predator algorithm in this study. (1) Taylor-based optimal neighborhood strategy (TNS) provides a more effective update in obtaining optimal individuals, thus reducing the probability of local optimum falling and attempting to overcome premature convergence, (2) Asymmetric search space with dynamic option-based learning significantly increases the probability of the population achieving the global optimum, thus improving the exploitation ability of Marine predator algorithm. The new method introduced with these two strategies added to the Marine predator algorithm is called Marine Predator Algorithm Dynamic Opposition and Taylor neighborhood search (DOTMPA). DOTMPA’s convergence performance is compared with current metaheuristic optimization and different versions of MPA in the literature on CEC2017, CEC2019, and CEC2022 benchmark suites. The results are discussed with convergence curves and box-plot analyses. DOTMPA is tested in Cantilever beam design, Tension/compression spring design, Speed reducer, Car crashworthiness, and Industrial refrigeration system engineering problems. The results are compared with the studies in the literature. Experimental results show that the proposed algorithm has strong competitiveness in terms of convergence accuracy. In addition, the stability of DOTMPA in convergence to the global optimum with trajectory analysis is also mentioned. Finally, the proposed method is applied to three truss topology optimization problems (20-truss, 24-truss, and 72-truss). The experimental results, the applicability of the proposed algorithm in practical applications, and the benchmark problem make it a promising and competitive optimization algorithm for optimization problems.
Reversible blind image hiding algorithm based on compressive sensing and fusion mechanism

Most current existing image hiding schemes are non-blind, that is, the receiver needs both carrier image and its corresponding container image (i.e., carrier image containing secrets) to extract the hidden secret image. Besides, the existing visual image encryption algorithm suffers from key management and distribution issues. To solve these problems, a reversible blind image hiding algorithm using compressive sensing and fusion mechanism is presented by combining integer wavelet transformation. Besides, a novel mathematical model of key acquisition is established to generate initial keys based on the plain image and a public-key cryptography RSA. Furthermore, a novel nonlinear 4D chaotic map named NewCCM is designed. The test results also explain that the proposed algorithm can reach a good reconstruction effect to the secret plain image without dependence on its corresponding original carrier image, i.e., blind extraction operation is achieved. Moreover, the proposed algorithm performs and displays high security, strong robustness and high reconstruction quality. For example, the information entropy of the obtained cipher image can reach 7.997, the PSNR between carrier image and container image can achieve 33 dB and the NC value can round to 0.999 by a compression ratio set to be half.
ITran: A novel transformer-based approach for industrial anomaly detection and localization

Anomaly detection is currently an essential quality monitoring process in industrial production. It is often affected by factors such as under or over reconstruction of images and unclear criteria for feature distribution evaluation, thus making it challenging to improve detection performance. To solve the above problems, this paper proposes a novel transformer-based approach, Inductive Transformer (ITran) for industrial anomaly detection and localization, which utilizes a multi-layer pyramid structure and multi-level jump connections to extract multi-scale features of the data, putting the anomaly detection into the feature space and achieving more accurate industrial anomaly detection and localization results. It incorporates inductive bias and convolution operations into the Transformer which helps to break the myth of Transformer being "data hungry". Compared with the common Transformers, ITran significantly reduces the computational cost and memory usage and makes it work well on small datasets. In addition, we basically eliminate the effect of positional embedding on the proposed Transformer model. Sufficient experiments have been conducted to validate global anomaly detection on three datasets MNIST, Fashion-MNST and Cifar-10, as well as local anomaly detection on the industrial datasets MVTec AD, Concrete Crack Image and BTAD. The proposed ITran achieves outstanding results on all the above datasets.
Improving Solar PV Prediction Performance with RF-CatBoost Ensemble: A Robust and Complementary Approach

Although solar energy is renewable, environmental conditions can make it unpredictable, which makes it difficult to maintain a consistent supply of electricity. Accurate solar forecast techniques are essential to overcome this. Therefore, an efficient model for predicting solar irradiance and PV power output is presented in this study. The suggested model is especially designed for Agartala city in Tripura, India, where the installed capacity of Grid Interactive Solar Power is extremely low. The proposed model for solar irradiance prediction involves utilizing a combination of the Random Forest and CatBoost algorithms. The model is developed using 10 years of solar data and other relevant meteorological parameters with a 1-hour interval and assessed at various stages to make long-term monthly predictions. In addition, we compared this model to other existing models for solar irradiance prediction. The study showcased the efficacy of Random Forest and CatBoost algorithms when utilized individually and as an ensemble. The results demonstrate the effectiveness of the Random Forest and CatBoost ensemble, with an impressive accuracy improvement of 6% and an R2-score of 86%. Additionally, in terms of RMSE and MAE, the model demonstrates superior performance with lower values of 83.8466 and 45.4011, respectively confirms to the proposed model's viability and suitability for long-term solar radiation and PV power predictions.
Single image dehazing using extended local dark channel prior

Image dehazing is an important technique aimed at eliminating the haze in the atmosphere to enhance the image's visual quality. There are many applications where it has been used as a prepossessing step, such as in event detection. In recent years, the dark channel prior methodology has been recognised as an effective approach for eliminating haze from hazy images. However, the main drawback of the existing dark channel prior methodology is that it only considers a single colour channel of the RGB image with pixels having minimum intensity values. This non-uniform selection of the dark channel from a single channel eradicates the effect of the transmission across the different channels of the hazy image. Hence, the haze cannot be removed to a great extent using the existing method. Thus, to address the problem of non-uniform estimation of the dark channel by the existing dark channel prior method, we propose an approach where the dark channel will be computed from all three channels of an image by selecting the minimum intensity. The main advantage of using the proposed prior-based methodology for image dehazing over deep neural network-based models such as CNN or GANs is that training deep models requires a large amount of training data, thus resulting in a longer training time. Experimental outcomes exhibit that the proposed technique outperforms state-of-the-art methods on synthetic datasets as well as real-world hazy images. The findings demonstrate that the proposed technique obtains better accuracy as compared to the state-of-the-art methods and recent deep learning-based models over the D-HAZY, I-HAZE, O-HAZE and Middlebury databases.
A New English/Arabic Parallel Corpus for Phishing Emails

Phishing involves malicious activity whereby phishers, in the disguise of legitimate entities, obtain illegitimate access to the victims’ personal and private information, usually through emails. Currently, phishing attacks and threats are being handled effectively through the use of the latest phishing email detection solutions. Most current phishing detection systems assume phishing attacks to be in English, though attacks in other languages are growing. In particular, Arabic is a widely used language and therefore represents a vulnerable target. However, there is a significant shortage of corpora that can be used to develop Arabic phishing detection systems. This article presents the development of a new English-Arabic parallel phishing email corpus that has been developed from the anti-phishing share task text (IWSPA-AP 2018). The email content was to be translated, and the task had been allotted to 10 volunteers who had a university background and were English and Arabic language experts. To evaluate the effectiveness of the new corpus, we develop phishing email detection models using Term Frequency–Inverse Document Frequency and Multilayer Perceptron using 1,258 emails in Arabic and English that have equal ratios of legitimate and phishing emails. The experimental findings show that the accuracy reaches 96.82% for the Arabic dataset and 94.63% for the emails in English, providing some assurance of the potential value of the parallel corpus developed.
Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction

Arabic handwritten-text recognition applies an OCR technique and then a text-correction technique to extract the text within an image correctly. Deep learning is a current paradigm utilized in OCR techniques. However, no study investigated or critically analyzed recent deep-learning techniques used for Arabic handwritten OCR and text correction during the period of 2020–2023. This analysis fills this noticeable gap in the literature, uncovering recent developments and their limitations for researchers, practitioners, and interested readers. The results reveal that CNN-LSTM-CTC is the most suitable architecture among Transformer and GANs for OCR because it is less complex and can hold long textual dependencies. For OCR text correction, applying DL models to generated errors in datasets improved accuracy in many works. In conclusion, Arabic OCR has the potential to further apply several text-embedding models to correct the resultant text from the OCR, and there is a significant gap in studies investigating this problem. In addition, there is a need for more high-quality and domain-specific OCR Arabic handwritten datasets. Moreover, we recommend the practical development of a space for future trends in Arabic OCR applications, derived from current limitations in Arabic OCR works and from applications in other languages; this will involve a plethora of possibilities that have not been effectively researched at the time of writing.
Histories of the Postdigital

This chapter was written as a historical overview of postdigital thought for the two edited volumes about postdigital research, Postdigital Research: Genealogies, Challenges, and Future Perspectives and Constructing Postdigital Research: Method and Emancipation. The first version of this chapter, ‘History of the Postdigital: Invitation for Feedback’, was published in October 2022. During the next 5 months, the article was downloaded more than 1000 times, and had received feedback from five people: Sean Sturm, Eamon Costello, and three other authors who preferred to remain anonymous. In February 2023 I addressed all received feedback and prepared this version for publication. I also added an Epilogue that reproduces the original invitation for discussion and then briefly reproduces and addresses respondents’ comments.
Optimum Tilt Angle and Solar Radiation of Photovoltaic Modules for Gulf Collaboration Council Countries

This article focuses on the optimization of tilt angle for solar panels in the Gulf Cooperation Council (GCC) countries. The tilt angle is a crucial factor that affects the amount of solar radiation received by the solar panel. The study uses a mathematical model to calculate the optimal tilt angle based on the latitude and longitude of the location and compares the results with the PVWatts calculator. The importance of selecting the optimal tilt angle for maximizing solar energy production is emphasized. The study finds that the mathematical model and PVWatts calculator are in good agreement, except for the negative tilt angles calculated by the model. The article also provides monthly, seasonally, and yearly irradiance values for the GCC countries calculated using the PVWatts calculator. It suggests that changing the tilt angle 12 times during the year can enhance the power output by 10.9%. The results show that the monthly automated angle has a maximum in December and a minimum in June, with Oman having the highest maximum angle of 50.5 and the lowest minimum angle of -10.58. Kuwait has the highest maximum angle of 58.33 and the lowest minimum angle of -2.75. The results are verified by the PVWatts calculator, showing a good similarity with a percentage of error around 3%. The study highlights the importance of selecting the optimal tilt angle to achieve maximum solar energy production in the GCC countries. The mathematical model and PVWatts calculator can serve as tools to calculate the optimum tilt angle for solar panels.
Multi-vortex hyperchaotic systems based on memristors and their application to image encryption

In this paper, a new memristor-based fourth-order hyperchaotic circuit that can generate multiple vortex volumes is constructed with the aid of a cosine memristor proposed by scholars. Thanks to the properties of cosine memristors, we find new hyperchaotic systems with full even symmetry based on parameters. The new system can produce multi-vortex properties with infinite equilibrium points and exhibits extreme multi-stability phenomena with the coexistence of an infinite number of attractors. After further study of phase trajectory diagrams, Lyapunov exponential spectrum, bifurcation diagrams, intermittent chaos and transient phenomena, we find that there are complex state transfer phenomena such as period 1 windows, chaos, multiperiodic bifurcations, multi-periodic windows and hyperchaotics, which are constantly alternating. In terms of circuit implementation, Multisim was used to build the circuit and to realise the characteristics of the memristor and the simulation of the hyperchaotic circuit. Finally, a DNA encryption algorithm is designed based on memristor hyperchaotic system, with emphasis on adjacent pixel correlation, encryption histogram, key sensitivity, anti-cropping and anti-noise performance. The results show that the new system has a very large key space, strong key sensitivity, outstanding anti-cropping, and anti-noise performance. The proposed hyperchaotic system thus has high security performance when applied to image encryption.
Addressing feature selection and extreme learning machine tuning by diversity-oriented social network search: an application for phishing websites detection

Abstract Feature selection and hyper-parameters optimization (tuning) are two of the most important and challenging tasks in machine learning. To achieve satisfying performance, every machine learning model has to be adjusted for a specific problem, as the efficient universal approach does not exist. In addition, most of the data sets contain irrelevant and redundant features that can even have a negative influence on the model’s performance. Machine learning can be applied almost everywhere; however, due to the high risks involved with the growing number of malicious, phishing websites on the world wide web, feature selection and tuning are in this research addressed for this particular problem. Notwithstanding that many metaheuristics have been devised for both feature selection and machine learning tuning challenges, there is still much space for improvements. Therefore, the research exhibited in this manuscript tries to improve phishing website detection by tuning extreme learning model that utilizes the most relevant subset of phishing websites data sets features. To accomplish this goal, a novel diversity-oriented social network search algorithm has been developed and incorporated into a two-level cooperative framework. The proposed algorithm has been compared to six other cutting-edge metaheuristics algorithms, that were also implemented in the framework and tested under the same experimental conditions. All metaheuristics have been employed in level 1 of the devised framework to perform the feature selection task. The best-obtained subset of features has then been used as the input to the framework level 2, where all algorithms perform tuning of extreme learning machine. Tuning is referring to the number of neurons in the hidden layers and weights and biases initialization. For evaluation purposes, three phishing websites data sets of different sizes and the number of classes, retrieved from UCI and Kaggle repositories, were employed and all methods are compared in terms of classification error, separately for layers 1 and 2 over several independent runs, and detailed metrics of the final outcomes (output of layer 2), including precision, recall, f1 score, receiver operating characteristics and precision–recall area under the curves. Furthermore, an additional experiment is also conducted, where only layer 2 of the proposed framework is used, to establish metaheuristics performance for extreme machine learning tuning with all features, which represents a large-scale NP-hard global optimization challenge. Finally, according to the results of statistical tests, final research findings suggest that the proposed diversity-oriented social network search metaheuristics on average obtains better achievements than competitors for both challenges and all data sets. Finally, the SHapley Additive exPlanations analysis of the best-performing model was applied to determine the most influential features.
Exploring the Key Characteristics and Theoretical Framework for Research on the Metaverse

This study presents an insightful examination of the conceptual and practical facets of the Metaverse by establishing a novel theoretical framework underpinned by an empirical case study of the Sandbox platform. Anchored in the principles of legality, virtual-reality integration, technological affinity, and community-driven innovation, the paper elucidates the inherent characteristics and potentialities of the Metaverse. Through meticulous research, the paper investigates the antecedents and evolution of the Metaverse, postulating an open, decentralized, and self-regulating ecosystem predicated on user-generated content and engagement. Furthermore, an in-depth case study of the Sandbox elucidates the practical applications, challenges, and opportunities associated with the operationalization of the Metaverse. The study showcases how avant-garde technologies such as blockchain, virtual reality, and artificial intelligence are instrumental in fostering immersive experiences, safeguarding virtual asset ownership, and facilitating tailored services. Moreover, the paper accentuates the indispensable role of community engagement and continuous innovation in cultivating a flourishing Metaverse environment. The analysis exposes that the burgeoning development of the Metaverse is intrinsically linked to the amalgamation of the virtual and the tangible, extending the frontiers of the digital economy. While shedding light on the virtues of the Metaverse, the study recognizes its nascent state and encourages further scholarly inquiry to comprehend and navigate its complexities. This research contributes significantly to the academic and practical understanding of the Metaverse, serving as a cornerstone for future investigations and technological advancements in this paradigm-shifting domain.
Application of Artificial Intelligence for Fraudulent Banking Operations Recognition

This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID-19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of many charitable funds that criminals can use to deceive users. The present work focuses on machine learning algorithms as a tool well suited for analyzing and recognizing online banking transactions. The study’s scientific novelty is the development of machine learning models for identifying fraudulent banking transactions and techniques for preprocessing bank data for further comparison and selection of the best results. This paper also details various methods for improving detection accuracy, i.e., handling highly imbalanced datasets, feature transformation, and feature engineering. The proposed model, which is based on an artificial neural network, effectively improves the accuracy of fraudulent transaction detection. The results of the different algorithms are visualized, and the logistic regression algorithm performs the best, with an output AUC value of approximately 0.946. The stacked generalization shows a better AUC of 0.954. The recognition of banking fraud using artificial intelligence algorithms is a topical issue in our digital society.