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Energy-related CO2 emissions are one of the biggest concerns facing urban design today, increasing rapidly as cities grow. This study uses as inputs the GDP of the G8 nations (from 1990 to 2016) depending on the utilization of various energy sources, including coal, oil, natural gas, and renewable energy. Multilayer perceptrons (MLP) are combined with various nature-inspired optimization algorithms, such as Heap-Based Optimizer (HBO), Teaching-Learning-Based Optimization (TLBO), Whale Optimization Algorithm (WOA), Vortex Search algorithm (VS), and Earthworm Optimization Algorithm (EWA), to create a dependable predictive network that takes the complexity of the problem into account. Our key contributions lie in developing and comprehensively evaluating these hybrid models assessing their efficacy in capturing the intricate dynamics of carbon emissions. The study found that TLBO and VS outperform other algorithms in CO2 emission computation accuracy. TLBO has a higher training MSE (3.6778) and lower testing MSE (4.4673), suggesting larger squared errors on training data and lower testing MSE, suggesting less overfitting due to better generalization to the testing set. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides (AMPs) are a promising solution for treating antibiotic‐resistant pathogens. However, efficient generation of diverse AMPs without prior knowledge of peptide structures or sequence alignments remains a challenge. Here, ProT‐Diff is introduced, a modularized deep generative approach that combines a pretrained protein language model with a diffusion model for the de novo generation of AMPs sequences. ProT‐Diff generates thousands of AMPs with diverse lengths and structures within a few hours. After silico physicochemical screening, 45 peptides are selected for experimental validation. Forty‐four peptides showed antimicrobial activity against both gram‐positive or gram‐negative bacteria. Among broad‐spectrum peptides, AMP_2 exhibited potent antimicrobial activity, low hemolysis, and minimal cytotoxicity. An in vivo assessment demonstrated its effectiveness against a drug‐resistant E. coli strain in acute peritonitis. This study not only introduces a viable and user‐friendly strategy for de novo generation of antimicrobial peptides, but also provides potential antimicrobial drug candidates with excellent activity. It is believed that this study will facilitate the development of other peptide‐based drug candidates in the future, as well as proteins with tailored characteristics. [ABSTRACT FROM AUTHOR]

This study investigates the adoption of quantum computing (QC) technology using the diffusion of innovation (DOI) theory and provides an extensive literature review. We deployed structural equation modeling to analyze data from a survey conducted among 96 top managers in various industries from Canada, the US, and Europe, including IT‐based small and medium‐sized enterprises (SMEs) dealing with QC software development. Our survey analysis indicates that the complexity of QC systems and software is the main barrier to the future adoption of quantum computing. This research offers insights into how future quantum computers can impact the security and privacy of information, emphasizing the importance of privacy protection. In this context, the study contributes to the notion of privacy engineering in the complex context of QC. The study established important outlines and tools for shaping future QCs. Our study, backed by empirical evidence, underscores the significant impact of new technology on citizens', organizations', firms', and government‐private data. The results provide a clear message to policymakers, industry leaders, and developers: privacy engineering should be an integral part of technical development, and it's crucial to act before costs escalate. In this context, our study stands out as one of the few that use NLP and structural equation modeling to address privacy challenges in QC research through experimental research, offering practical solutions to real‐world problems. [ABSTRACT FROM AUTHOR]

Background: Dental care has been transformed by neural networks, introducing advanced methods for improving patient outcomes. By leveraging technological innovation, dental informatics aims to enhance treatment and diagnostic processes. Early diagnosis of dental problems is crucial, as it can substantially reduce dental disease incidence by ensuring timely and appropriate treatment. The use of artificial intelligence (AI) within dental informatics is a pivotal tool that has applications across all dental specialties. This systematic literature review aims to comprehensively summarize existing research on AI implementation in dentistry. It explores various techniques used for detecting oral features such as teeth, fillings, caries, prostheses, crowns, implants, and endodontic treatments. AI plays a vital role in the diagnosis of dental diseases by enabling precise and quick identification of issues that may be difficult to detect through traditional methods. Its ability to analyze large volumes of data enhances diagnostic accuracy and efficiency, leading to better patient outcomes. Methods: An extensive search was conducted across a number of databases, including Science Direct, PubMed (MEDLINE), arXiv.org, MDPI, Nature, Web of Science, Google Scholar, Scopus, and Wiley Online Library. Results: The studies included in this review employed a wide range of neural networks, showcasing their versatility in detecting the dental categories mentioned above. Additionally, the use of diverse datasets underscores the adaptability of these AI models to different clinical scenarios. This study highlights the compatibility, robustness, and heterogeneity among the reviewed studies. This indicates that AI technologies can be effectively integrated into current dental practices. The review also discusses potential challenges and future directions for AI in dentistry. It emphasizes the need for further research to optimize these technologies for broader clinical applications. Conclusions: By providing a detailed overview of AI's role in dentistry, this review aims to inform practitioners and researchers about the current capabilities and future potential of AI-driven dental care, ultimately contributing to improved patient outcomes and more efficient dental practices. [ABSTRACT FROM AUTHOR]

Ice avalanche (IA) has a strong concealment and sudden characteristics, which can cause severe disasters. The early identification of IA hidden danger is of great value for disaster prevention and mitigation. However, it is very difficult, and there is poor efficiency in identifying it by site investigation or manual remote sensing. So, an artificial intelligence method for the identification of IA hidden dangers using a deep learning model has been proposed, with the glacier area of the Yarlung Tsangpo River Gorge in Nyingchi selected for identification and validation. First, through engineering geological investigations, three key identification indices for IA hidden dangers are established, glacier source, slope angle, and cracks. Sentinel-2A satellite data, Google Earth, and ArcGIS are used to extract these indices and construct a feature dataset for the study and validation area. Next, key performance metrics, such as training accuracy, validation accuracy, test accuracy, and loss rates, are compared to assess the performance of the ResNet50 (Residual Neural Network 50) and VGG16 (Visual Geometry Group 16) models. The VGG16 model (96.09% training accuracy) is selected and optimized, using Early Stopping (ES) to prevent overfitting and L2 regularization techniques (L2) to add weight penalties, which constrained model complexity and enhanced simplicity and generalization, ultimately developing the ES-L2-VGG16 (Early Stopping—L2 Norm Regularization Techniques—Visual Geometry Group 16) model (98.61% training accuracy). Lastly, during the validation phase, the model is applied to the Yarlung Tsangpo River Gorge glacier area on the Tibetan Plateau (TP), identifying a total of 100 IA hidden danger areas, with average slopes ranging between 34° and 48°. The ES-L2-VGG16 model achieves an accuracy of 96% in identifying these hidden danger areas, ensuring the precise identification of IA dangers. This study offers a new intelligent technical method for identifying IA hidden danger, with clear advantages and promising application prospects. [ABSTRACT FROM AUTHOR]

We would like to extend our sincere thanks to us beloved parents who were the source of our inspiration and strength, who gave us all their giving and their continuous moral, spiritual, emotional and financial support for us whatever we say and do we will not fulfill their full right so thank you until the pens dry and the newspapers are full. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is a disruptive technology that is being used by smart tourist destinations (STDs) to develop new business models and marketing services to increase tourists' experiences and sales, revenue, productivity, and efficiency and STDs. However, the adoption of AI applications and platforms requires a high economic budget for STDs that want to integrate this digital tool into their future agenda and tourism development plans, especially when they set them up for marketing plans and operational processes. This iterative technology needs regular maintenance as well, leading to recurring costs and specialised crews in advanced technologies and marketing activities. This study aims to show the impact of AI advancements on STDs' tourism marketing to enhance the quality of services and illustrate their future agenda to improve tourists' experiences. A comprehensive literature review on AI technology and STDs has been conducted to illustrate new tourism marketing in their future agenda. Moreover, this study presents real examples of AI technology in a tourism context to better understand the potential of this digital tool. The findings of the current study support the idea that AI is a multipurpose tool that helps manage, monitor, and analyse sales information; revenue management; minimise prediction errors; streamline operations; and develop better marketing strategies, optimising economic resources, reducing marketing costs, and responding dynamically to changing needs for tourists and residents in STDs. Furthermore, the investment in AI technologies by STDs helps enhance the quality of products and services, and attract new investments, which benefit the regional economies and population's quality of life. This study is the first to address the use of AI to improve tourist marketing in STDs, which is its primary uniqueness. Also, this study identifies new opportunities and initiatives through AI that can be developed to help tourism marketing in STDs. [ABSTRACT FROM AUTHOR]

Copyright of Egyptian Journal of Information Sciences is the property of Beni Suef University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Geological disasters such as landslide, debris flow and collapse are major natural disasters faced by both China and the world, which seriously threaten people's lives, property security and the socio-economic development. Although the method of using the paradigm of traditional mathematical statistics and physical model to predict the low-probability events of geological disasters have been developed for decades, the difficulty of accurate prediction still remains significant, which is recognized as a major and urgent scientific challenge in the field of Earth science. Artificial intelligence is an important driving force for a new round of scientific and technological revolution and industrial transformation. However, how to systematically establish the AI prediction paradigm for low-probability events of geological disasters and deeply coupled with the physical mechanisms of geological disaster evolution and AI learning models still remains as a scientific bottleneck at the intersection of Earth science and information science. In order to clarify the latest research progress of AI prediction of geological disasters such as landslide, collapse and debris flow, this paper first quantifies the current status of global geological disasters and the urgency of prediction, and then summarizes the overall methodology of AI prediction of geological disasters. In particular, prediction feature selection, data set collection and AI prediction models have been detailly reviewed. Moreover, this review discussed the approaches in establishing the physical-informed AI model for higher accurate, robust, and explainable prediction performance. Subsequently, this paper summarizes the recent research achievements of AI prediction for landslide, collapse, and debris flow. Based on these progresses, we also analyzed the existing problems in the field of AI prediction of geological disasters, and indicated the key directions of AI prediction of geological disasters in the future. This review work is believed to be a critical guidance for future intelligent prediction on the severe geological disasters. [ABSTRACT FROM AUTHOR]

The primary aim of this study was to propose a conceptual framework intended to leverage the capabilities of the Chat GPT artificial intelligence model to enhance the creative teaching proficiencies of secondary school mathematics teachers. A closed interview questionnaire designed by the researcher was administered to evaluate the proficiency levels of secondary mathematics teachers using a descriptive methodology. The study sample comprised 31 mathematics teachers. Furthermore, the researcher developed a proposed conceptual framework aimed at activating the Chat GPT model to foster creative teaching skills among secondary mathematics teachers. Analysis of the data revealed that teachers demonstrated a moderate level of proficiency in the planning dimension of creative teaching while their execution and evaluation competencies were comparatively less advanced. Moreover, the study confirmed the appropriateness of the proposed conceptual framework for augmenting the creative teaching aptitudes of secondary mathematics teachers emphasizing its relevance, significance and practical utility. According to teachers’ perspectives, this framework is appropriate in light of its relevance, importance and applicability. In a nutshell, this research contributes to the discourse on innovative pedagogical strategies by proposing a viable framework for the integration of artificial intelligence technologies into mathematics education. By doing so, it seeks to nurture creativity and efficacy among teachers within the secondary school potentially enhancing the quality of mathematics instruction and student learning outcomes. These findings underscore the importance of integrating regenerative AI ChatGPT into classrooms emphasizing its role in enhancing creative teaching skills and its practical applicability in educational contexts. [ABSTRACT FROM AUTHOR]

Abstract Background In total knee arthroplasty (TKA), the practical use of patient-specific instrumentation (PSI) has been reported previously with both advantage and disadvantage. The application of artificial intelligent (AI) forces overwhelmingly development of medical industries, while the impact of AI on PSI efficiency remains unknown. Thus, this study aimed to assess the efficiency of Intelligent-PSI (i-PSI) in TKA, compared with the conventional instrumentation-TKA (CI). Methods 102 late-stage OA patients who met inclusive criteria were recruited in this prospective randomized controlled trial and separated into two groups (i-PSI vs. CI). In both groups, an AI preoperative planning engine was applied for surgery decision making. In CI group, conventional instrumentation was applied for bony resection, while resection of i-PSI group was completed with i-PSI. A convolutional neural network was applied to automatically process computer tomography images and thus produced i-PSI. With the help of three-dimension printing, the workflow of production was largely simplified. AI-driven preoperative planning guided resection and alignment decisions. Resection measurement, perioperative radiography and perioperative clinical outcomes were analyzed to verify efficiency of i-PSI. Results In resection outcomes, smaller deviation of lateral and medial distal femoral resection were found in i-PSI group than CI group (P = 0.032 and 0.035), while no difference was found in other resection planes. In radiography outcomes, postoperative coronal alignments of i-PSI group, including postoperative Hip–knee–ankle axis (HKA) (P = 0.025), postoperative HKA outliners (P = 0.042), Femoral coronal alignment (FCA) (P = 0.019) and Joint line convergence angle (JLCA) (P = 0.043) showed closer to neutral position than CI group. Moreover, Femoral sagittal alignment (FSA) of i-PSI group showed closer to neutral position than CI group(P = 0.005). No difference was found in other alignments. In clinical outcomes, i-PSI group seemed to cost more surgical time than CI group (P = 0.027), while others showed no differences between the two groups. Conclusion Intelligent Patient-specific Instrumentation in TKA achieved simplified production flow than conventional PSI, while also showed more accurate resection, improved synthesis position and limb alignment than conventional instrumentation. Above all, this study proved that i-PSI being an applicable and promising tool in TKA.

Abstract Background This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. Methods The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model. Results Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively. Conclusions In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options.

Abstract This study aimed to establish a deep learning-based predictive model for the prognosis of idiopathic sudden sensorineural hearing loss (SSNHL). Data from 1108 patients with SSNHL between January 2015 and May 2023 were retrospectively analyzed. Patients underwent standardized treatment protocols including high-dose steroid therapy and hearing outcomes were assessed after three months using Siegel’s criteria and the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) classification. For predicting patient recovery, a two-layered classification process was implemented. Initially, a set of 22 Multilayer Perceptrons (MLP) networks was employed to categorize the patients. The outcomes from this initial categorization were subsequently relayed to a second-layer meta-classifier for final prognosis determination. The validity of this methodology was ascertained through a K-fold cross-validation procedure executed with 10 distinct splits. The prediction model for complete recovery, based on Siegel’s criteria, demonstrated an accuracy of 0.892 and area under the curve (AUC) of 0.922. For the class A prediction, according to AAO-HNS classification, the model showed an accuracy of 0.847 and AUC of 0.918. These results suggest that the model may have the potential to contribute to the establishment of tailored patient management strategies by predicting hearing recovery in patients with SSNHL.

Background: In total knee arthroplasty (TKA), the practical use of patient-specific instrumentation (PSI) has been reported previously with both advantage and disadvantage. The application of artificial intelligent (AI) forces overwhelmingly development of medical industries, while the impact of AI on PSI efficiency remains unknown. Thus, this study aimed to assess the efficiency of Intelligent-PSI (i-PSI) in TKA, compared with the conventional instrumentation-TKA (CI). Methods: 102 late-stage OA patients who met inclusive criteria were recruited in this prospective randomized controlled trial and separated into two groups (i-PSI vs. CI). In both groups, an AI preoperative planning engine was applied for surgery decision making. In CI group, conventional instrumentation was applied for bony resection, while resection of i-PSI group was completed with i-PSI. A convolutional neural network was applied to automatically process computer tomography images and thus produced i-PSI. With the help of three-dimension printing, the workflow of production was largely simplified. AI-driven preoperative planning guided resection and alignment decisions. Resection measurement, perioperative radiography and perioperative clinical outcomes were analyzed to verify efficiency of i-PSI. Results: In resection outcomes, smaller deviation of lateral and medial distal femoral resection were found in i-PSI group than CI group (P = 0.032 and 0.035), while no difference was found in other resection planes. In radiography outcomes, postoperative coronal alignments of i-PSI group, including postoperative Hip–knee–ankle axis (HKA) (P = 0.025), postoperative HKA outliners (P = 0.042), Femoral coronal alignment (FCA) (P = 0.019) and Joint line convergence angle (JLCA) (P = 0.043) showed closer to neutral position than CI group. Moreover, Femoral sagittal alignment (FSA) of i-PSI group showed closer to neutral position than CI group(P = 0.005). No difference was found in other alignments. In clinical outcomes, i-PSI group seemed to cost more surgical time than CI group (P = 0.027), while others showed no differences between the two groups. Conclusion: Intelligent Patient-specific Instrumentation in TKA achieved simplified production flow than conventional PSI, while also showed more accurate resection, improved synthesis position and limb alignment than conventional instrumentation. Above all, this study proved that i-PSI being an applicable and promising tool in TKA. [ABSTRACT FROM AUTHOR]

Solid-state fermentation (SSF) system involves solid, liquid, and gas phases, characterized by complex mass and heat transfer mechanisms and microbial complex interactions. The SSF processes for traditional Chinese fermented foods, such as vinegar, soy sauce, and baijiu primarily rely on experience, and most of the operations are replaced by auto machine now. However, there is still a lack of engineering in-depth study of the microbial process of SSF for complete process control. To meet the demands of smart manufacturing and green production, this paper emphasizes the engineering analysis of the mechanisms behind SSF. It reviews the progress in the engineering aspects of Chinese traditional SSF, including raw material pretreatment, process parameter detection, mathematical model construction, and equipment innovation. Additionally, it summarizes the challenges faced during intelligent upgrades and the opportunities brought by scientific and technological advancements, proposing future development directions. This review provides an overview of the SSF engineering aspects, offering a reference for the intelligent transformation and sustainable development of the Chinese traditional SSF food industry. [ABSTRACT FROM AUTHOR]

Background: This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients. Methods: The study collected a total of 1500 images obtained from panoramic radiographs from child patients between the ages of 5 and 14 years. YOLOv5, a convolutional neural network (CNN)-based object detection model, was used to automatically detect the calcification states of teeth. Images obtained from panoramic radiographs from child patients were trained and tested in the YOLOv5 algorithm. True-positive (TP), false-positive (FP), and false-negative (FN) ratios were calculated. A confusion matrix was used to evaluate the performance of the model. Results: Among the 146 test group images with 1022 labels, there were 828 TPs, 308 FPs, and 1 FN. The sensitivity, precision, and F1-score values of the detection model of the tooth stage development model were 0.99, 0.72, and 0.84, respectively. Conclusions: In conclusion, utilizing a deep learning-based approach for the detection of dental development on pediatric panoramic radiographs may facilitate a precise evaluation of the chronological correlation between tooth development stages and age. This can help clinicians make treatment decisions and aid dentists in finding more accurate treatment options. [ABSTRACT FROM AUTHOR]

Summary: Cellular networks are projected to deal with an immense rise in data traffic, as well as an enormous and diverse device, plus advanced use cases, in the nearest future; hence, future 5G networks are being developed to consist of not only 5G but also different radio access technologies (RATs) integrated. In addition to 5G, the user's device (UD) will be able to connect to the network via LTE, WiMAX, WiFi, Satellite and other technologies. On the other hand, Satellite has been suggested as a preferred network to support 5G use cases. However, achieving load balancing is essential to guarantee an equal amount of traffic distributed between different RATs in a heterogeneous wireless network; this would enable optimal utilisation of the radio resources and lower the likelihood of call blocking/dropping. This study presented an artificial intelligent‐based application in heterogeneous wireless networks and proposed an enhanced particle optimisation (EPSO) algorithm to solve the load balancing problem in 5G‐Satellite networks. The algorithm uses a call admission control strategy to admit users into the network to ensure that users are evenly distributed on the network. The proposed algorithm was compared with the Artificial Bee Colony and Simulated Annealing algorithm using three performance metrics: throughput, call blocking and fairness. Finally, based on the experimental findings, results outcomes were analysed and discussed. [ABSTRACT FROM AUTHOR]

This study aimed to establish a deep learning-based predictive model for the prognosis of idiopathic sudden sensorineural hearing loss (SSNHL). Data from 1108 patients with SSNHL between January 2015 and May 2023 were retrospectively analyzed. Patients underwent standardized treatment protocols including high-dose steroid therapy and hearing outcomes were assessed after three months using Siegel's criteria and the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) classification. For predicting patient recovery, a two-layered classification process was implemented. Initially, a set of 22 Multilayer Perceptrons (MLP) networks was employed to categorize the patients. The outcomes from this initial categorization were subsequently relayed to a second-layer meta-classifier for final prognosis determination. The validity of this methodology was ascertained through a K-fold cross-validation procedure executed with 10 distinct splits. The prediction model for complete recovery, based on Siegel's criteria, demonstrated an accuracy of 0.892 and area under the curve (AUC) of 0.922. For the class A prediction, according to AAO-HNS classification, the model showed an accuracy of 0.847 and AUC of 0.918. These results suggest that the model may have the potential to contribute to the establishment of tailored patient management strategies by predicting hearing recovery in patients with SSNHL. [ABSTRACT FROM AUTHOR]

Recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance on datasets and the ability to generalize across data. However, no model has achieved perfect performance in solving all problems, and the pros and cons of each approach remain unclear to those new to the field. Therefore, this paper aims to review deep learning algorithms that have been applied to solve molecular challenges in computational chemistry. We proposed a comprehensive categorization that encompasses two primary approaches; conventional deep learning and geometric deep learning models. This classification takes into account the distinct techniques employed by the algorithms within each approach. We present an up-to-date analysis of these algorithms, emphasizing their key features and open issues. This includes details of input descriptors, datasets used, open-source code availability, task solutions, and actual research applications, focusing on general applications rather than specific ones such as drug discovery. Furthermore, our report discusses trends and future directions in molecular algorithm design, including the input descriptors used for each deep learning model, GPU usage, training and forward processing time, model parameters, the most commonly used datasets, libraries, and optimization schemes. This information aids in identifying the most suitable algorithms for a given task. It also serves as a reference for the datasets and input data frequently used for each algorithm technique. In addition, it provides insights into the benefits and open issues of each technique, and supports the development of novel computational chemistry systems. [ABSTRACT FROM AUTHOR]

With the new revolution in data technology, many types of streaming data are automatically generated in our living environment. The vast amount of information carried by this streaming data demands decision support from new online learning algorithms. In this paper, we propose a new online learning algorithm to monitor changes in stream data/system status and provide automatic decision support using streaming data. The new online learning algorithm we propose consists of two components. Firstly, we adapt an existing Bayesian approach into an online learning algorithm for change detection, which serves as Component A. Then, we integrate this algorithm with a modified online learning method from repeated games (Play against Random Past), which forms Component B. Theoretical support for Component B is provided with a mathematical proof at the end. We demonstrate the algorithm's performance in this paper through simulations using both artificial data from a random process and data from the 2009 H1N1 pandemic. [ABSTRACT FROM AUTHOR]

The primary aim of this research is to present a thorough and all-encompassing examination of artificial intelligence (AI) methodologies employed in the detection of financial fraud. The present study employs a systematic literature review (SLR) that was conducted utilizing the PRISMA approach. A comprehensive search was undertaken on reputable academic databases including ScienceDirect, Scopus, Springer, and Emerald, yielding a total of 24 papers published throughout the timeframe of 2014 to 2023. These articles will, thereafter, undergo further analysis. The findings of this study demonstrate that the implementation of artificial intelligence (AI) techniques for detecting financial fraud yields favorable outcomes. Specifically, the AI approach proves to be effective in enhancing the precision and efficiency of fraud pattern identification, thereby making a substantial contribution in this domain. In contrast, the prevailing methodology employed in the realm of financial fraud detection is frequently centered around machine learning. Furthermore, a majority of the research encompassed a diverse range of industries, with particular emphasis on the financial industry as the primary domain for the implementation of artificial intelligence (AI) in the detection of financial fraud. [ABSTRACT FROM AUTHOR]

Android set-top boxes, often known as STBs, have emerged as a popular option for streaming and viewing digital content. Testing Android STBs, however, can be extremely difficult due to the complicated hardware and software configurations they require. In addition to being time-consuming and error-prone, manual testing can be exceedingly costly. Because of this, automation testing has emerged as a possible solution to the problems that have been identified. This article presents an overview of the current state of the art in automation testing for Android STBs from a technical perspective. This article highlights the difficulties typically connected with testing Android STBs, the advantages of automation testing, and the various tools and methods utilised for automation testing. Integrating AI and machine learning approaches are explored to demonstrate the potential for increasing efficiency and adaptability in Android STB automation testing. The findings contribute to the evolution of testing techniques for Android STBs by providing insights into tool selection, performance optimisation, and future approaches for intelligent automation. In addition, the article provides an overview of recent research conducted in this area and an analysis of the current and future directions that automation testing for Android STBs will take. [ABSTRACT FROM AUTHOR]

The 6G network, the next‐generation communication system, is envisaged to provide unprecedented experience through hyperconnectivity involving everything. The communication should hold artificial intelligence‐centric network infrastructures as interconnecting a swarm of machines. However, existing network systems use orthogonal modulation and costly error correction code; they are very sensitive to noise and rely on many processing layers. These schemes impose significant overhead on low‐power internet of things devices connected to noisy networks. Herein, a hyperdimensional network‐based system, called NetHD$N e t H D$, is proposed, which enables robust and efficient data communication/learning. NetHD$N e t H D$ exploits a redundant and holographic representation of hyperdimensional computing (HDC) to design highly robust data modulation, enabling two functionalities on transmitted data: 1) an iterative decoding method that translates the vector back to the original data without error correction mechanisms, or 2) a native hyperdimensional learning technique on transmitted data with no need for costly data decoding. A hardware accelerator that supports both data decoding and hyperdimensional learning using a unified accelerator is also developed. The evaluation shows that NetHD$N e t H D$ provides a bit error rate comparable to that of state‐of‐the‐art modulation schemes while achieving 9.4 ×$\times$ faster and 27.8 ×$\times$ higher energy efficiency compared to state‐of‐the‐art deep learning systems. [ABSTRACT FROM AUTHOR]

All Educational Institutions prioritize student attendance and Baze University is certainly one to uphold the standard. All students are required to maintain a minimum attendance rate of at least 75% in each class or lecture to be eligible to take the module's examination. With the advancement of technology, biometrics concepts have proven to be more efficient in monitoring attendance. We present a facial recognition-based prototype webapp for Baze university. This webapp aims to eradicate the cumbersome method of marking attendance that lecturers of Baze University have subscribed to. The main focus is the use of facial recognition techniques using the Dlib library of OpenCV. Images are read and stored in a database, compared with images captured from a webcam to analyze and record the best matches using nearest neighbor algorithm. [ABSTRACT FROM AUTHOR]

Energy-related CO2 emissions are one of the biggest concerns facing urban design today, increasing rapidly as cities grow. This study uses as inputs the GDP of the G8 nations (from 1990 to 2016) depending on the utilization of various energy sources, including coal, oil, natural gas, and renewable energy. Multilayer perceptrons (MLP) are combined with various nature-inspired optimization algorithms, such as Heap-Based Optimizer (HBO), Teaching-Learning-Based Optimization (TLBO), Whale Optimization Algorithm (WOA), Vortex Search algorithm (VS), and Earthworm Optimization Algorithm (EWA), to create a dependable predictive network that takes the complexity of the problem into account. Our key contributions lie in developing and comprehensively evaluating these hybrid models assessing their efficacy in capturing the intricate dynamics of carbon emissions. The study found that TLBO and VS outperform other algorithms in CO2 emission computation accuracy. TLBO has a higher training MSE (3.6778) and lower testing MSE (4.4673), suggesting larger squared errors on training data and lower testing MSE, suggesting less overfitting due to better generalization to the testing set.

Abstract Antimicrobial peptides (AMPs) are a promising solution for treating antibiotic‐resistant pathogens. However, efficient generation of diverse AMPs without prior knowledge of peptide structures or sequence alignments remains a challenge. Here, ProT‐Diff is introduced, a modularized deep generative approach that combines a pretrained protein language model with a diffusion model for the de novo generation of AMPs sequences. ProT‐Diff generates thousands of AMPs with diverse lengths and structures within a few hours. After silico physicochemical screening, 45 peptides are selected for experimental validation. Forty‐four peptides showed antimicrobial activity against both gram‐positive or gram‐negative bacteria. Among broad‐spectrum peptides, AMP_2 exhibited potent antimicrobial activity, low hemolysis, and minimal cytotoxicity. An in vivo assessment demonstrated its effectiveness against a drug‐resistant E. coli strain in acute peritonitis. This study not only introduces a viable and user‐friendly strategy for de novo generation of antimicrobial peptides, but also provides potential antimicrobial drug candidates with excellent activity. It is believed that this study will facilitate the development of other peptide‐based drug candidates in the future, as well as proteins with tailored characteristics.

Abstract Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, time-consuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases’ diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer’s diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall) (92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency.

The demand for energy in Jordan's residential buildings is increasing, resulting in significant discrepancies between predicted and actual energy usage. Accurately predicting household energy usage is crucial for sustainable building planning and effective energy management strategies. However, traditional energy models often need to pay more attention to the complexity of occupant behavior, leading to significant differences between expected and actual energy usage. To improve energy consumption forecast accuracy, a unique approach was proposed in this study, which combined Time-Use Survey (TUS) data with AI-driven algorithms in Building Information Modeling (BIM). The study conducted a time-use survey in 100 Irbid, Jordan residences to document detailed inhabitant behavior and daily activity patterns. The collected data was then used to train AI algorithms integrated into the BIM framework. This integration enables the BIM model to dynamically adapt and estimate energy usage based on real-time occupant behaviors and environmental conditions instead of relying solely on static architectural and mechanical inputs. Using this BIM model with AI significantly reduced the difference between expected and actual energy usage in the analyzed houses. The findings of this study support the usefulness of incorporating occupant behavioral data into energy prediction models. This approach provides more accurate energy consumption projections and highlights the importance of considering human aspects throughout the architectural design and energy planning stages. [ABSTRACT FROM AUTHOR]

ChatGPT is an innovative artificial intelligence designed to enhance human activities and serve as a potent tool for information retrieval. This study aimed to evaluate the performance and limitation of ChatGPT on fourth-year pharmacy student examination. This cross-sectional study was conducted on February 2023 at the Faculty of Pharmacy, Chiang Mai University, Thailand. The exam contained 16 multiple-choice questions and 2 short-answer questions, focusing on classification and medical management of shock and electrolyte disorders. Out of the 18 questions, ChatGPT provided 44% (8 out of 18) correct responses. In contrast, the students provided a higher accuracy rate with 66% (12 out of 18) correctly answered questions. The findings of this study underscore that while AI exhibits proficiency, it encounters limitations when confronted with specific queries derived from practical scenarios, on the contrary with pharmacy students who possess the liberty to explore and collaborate, mirroring real-world scenarios. Users must exercise caution regarding its reliability, and interpretations of AI-generated answers should be approached judiciously due to potential restrictions in multi-step analysis and reliance on outdated data. Future advancements in AI models, with refinements and tailored enhancements, offer the potential for improved performance. • AI showed a profound efficiency in educational and scientific sectors. • ChatGPT requires time to update its database, resulting in outdated information. • ChatGPT encounters limitations when confronted with specific practical scenarios. • Users should approach its responses with caution and reliability awareness. • One must recognize constraints in multi-step interpretation and complex queries. [ABSTRACT FROM AUTHOR]

This systematic review meticulously explores the transformative impact of digital technologies on the grid planning, grid operations, and energy market dynamics of power distribution grids. Utilizing a robust methodological framework, over 54,000 scholarly articles were analyzed to investigate the integration and effects of artificial intelligence, machine learning, optimization, the Internet of Things, and advanced metering infrastructure within these key subsections. The literature was categorized to show how these technologies contribute specifically to grid planning, operation, and market mechanisms. It was found that digitalization significantly enhances grid planning through improved forecasting accuracy and robust infrastructure design. In operations, these technologies enable real-time management and advanced fault detection, thereby enhancing reliability and operational efficiency. Moreover, in the market domain, they support more efficient energy trading and help in achieving regulatory compliance, thus fostering transparent and competitive markets. However, challenges such as data complexity and system integration are identified as critical hurdles that must be overcome to fully harness the potential of smart grid technologies. This review not only highlights the comprehensive benefits but also maps out the interdependencies among the planning, operation, and market strategies, underlining the critical role of digital technologies in advancing sustainable and resilient energy systems. [ABSTRACT FROM AUTHOR]

The article presents a novel artificial Intelligent based feature parameters extraction that utilizes Mixed descriptors for analyzing white blood cell images. The proposed approach involves integrating these matrices into an artificial intelligent and Mixed descriptor architecture called AI-MD. The performance of the AI-MD model is evaluated using four cross-validation methods and various evaluation metrics including loss and accuracy curves, F1-score, recall, and receiver operating characteristic curve. The study concludes with a comparison of the proposed method with other recognition approaches. In order to assess the effectiveness of our architecture, we utilized the four cross-validation method. Recognition accuracy was evaluated using the area under the curve (AUC) metric for specific classes for all based on the original image matrices. The AUC values attained for these classes were as follows: 99.49%, 99.75%, 98.60%, and 99.72% respectively. Our method yielded highly favorable outcomes, with an impressive AUC value of 93.93%. This result outperformed existing approaches, highlighting the significant potential of our method. Consequently, it underscores the necessity for additional research in the realm of screening methods within medical applications, we also validate this experimentation using dataset of UCI has suggested to classify the fetus into three classes: normal, suspicious, and pathological. [ABSTRACT FROM AUTHOR]