bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving lithium-ion battery reliability through neural network remaining useful life prediction
Authors: Zraibi Brahim , Mansouri Mohamed
Published: November 2024

The reliable performance of lithium-ion batteries is crucial for the safe and efficient operation of electrical systems, particularly in electric vehicles. To mitigate the risk of battery failure due to degradation, accurate forecasting of the remaining useful life (RUL) is imperative. In this study, we propose employing various recurrent neural network (RNN) methods, including RNN, gated recurrent unit (GRU), and long short-term memory (LSTM), to enhance RUL prediction accuracy for lithium-ion batteries. Our approach aims to provide reliable, accurate, and simple estimates of remaining battery life, facilitating effective management of electric vehicle power systems and minimizing the risk of failure. Performance evaluation metrics such as mean absolute error (MAE), R-squared (R²), mean absolute percentage error (MAPE), and root mean squared error (RMSE) are utilized to assess prediction accuracy. Experimental validation conducted using the NASA lithium-ion battery dataset demonstrates the superiority of LSTM in reducing prediction error and enhancing RUL prediction performance compared to alternative approaches. These findings underscore the potential of neural network methodologies in advancing battery management practices and ensuring the longevity and reliability of lithium-ion battery systems.
Exploring the dynamics of providing cognition using a computational model of cognitive insomnia
Authors: Rateb Roqia , M. Abualhaj Mosleh , Alsaaidah Adeeb , A. Alsharaiah Mohammad , Shorman Amaal , Jaber Thalji Nisrean
Published: November 2024

Insomnia is a common sleep-related neuropsychological disorder that can lead to a range of problems, including cognitive deficits, emotional distress, negative thoughts, and a sense of insufficient sleep. This study proposes a providing computational dynamic cognitive model (PCDCM) insight into providing cognitive mechanisms of insomnia and consequent cognitive deficits. Since the support providing is significantly dynamic and it includes substantial changes as demanding condition happen. From this perspective the underlying model covers integrating of both coping strategies, provision preferences and adaptation concepts. The model was found to produce realistic behavior that could clarify conditions for providing support to handle insomnia individuals, which was done by employing simulation experiments under various negative events, personality resources, altruistic attitude and personality attributes. Simulation results show that, a person with bonadaptation and either problem focused or emotion focused coping can provide different social support based on his personality resources, personality attributes, and knowledge level, whereas a person with maladaptation regardless the coping strategies cannot provide any type of social support. Moreover, person with close tie tends to provide instrumental, emotional, and companionship support than from weak tie. Finally, a mathematical analysis was used to examine the possible equilibria of the model. 
A novel AI model for the extraction and prediction of Alzheimer disease from electronic health record
Authors: V. Maju Sonam , Sirya Pushpam Gnana Prakasi Oliver
Published: November 2024

Dark data is an emerging concept, with its existence, identification, and utilization being key areas of research. This study examines various aspects and impacts of dark data in the healthcare domain and designs a model to extract essential clinical parameters for Alzheimer's from electronic health records (EHR). The novelty of dark data lies in its significant impact across sectors. In healthcare, even the smallest data points are crucial for diagnosis, prediction, and treatment. Thus, identifying and extracting dark data from medical data corpora enhances decision-making. In this research, a natural language processing (NLP) model is employed to extract clinical information related to Alzheimer's disease, and a machine learning algorithm is used for prediction. Named entity recognition (NER) with SpaCy is utilized to extract clinical departments from doctors' descriptions stored in EHRs. This NER model is trained on custom data containing processed EHR text and associated entity annotations. The extracted clinical departments can then be used for future Alzheimer's diagnosis via support vector machine (SVM) algorithms. Results show improved accuracy with the use of extracted dark data, highlighting its importance in predicting Alzheimer's disease. This research also explores the presence of dark data in various domains and proposes a dark data extraction model for the clinical domain using NLP.
Advancing airway management for ventilation optimization in critical healthcare with cloud computing and deep learning
Authors: Krishnamoorthi Suresh Kumar , Karthi Govindharaju , Radhika Moorthy , Rathinam Anantha Raman , Raju Ayalapogu Ratna , Pinjarkar Latika , Srinivasan Chelliah
Published: November 2024

Improving patient outcomes in critical care settings is significantly connected to effective ventilation control. This research introduces a new method for improving ventilation methods in critical healthcare utilizing a long short-term memory (LSTM) network hosted in the cloud. Ventilators, pulse oximeters, and capnography are just a few examples of medical equipment that input data into the system, which then uploads the data to the cloud for analysis. The LSTM network can learn from data patterns and correlations, drawing on respiratory parameters' time dynamics, to provide real-time suggestions and predictions for ventilation settings. The system aims to improve clinical results and reduce the risk of ventilator-induced lung damage by tailoring ventilation techniques according to each patient's requirements and by forecasting potential issues. Due to remote monitoring technology, medical professionals can quickly analyze their patient's conditions and act accordingly. The system allows for continuous improvement using iterative learning of more data and feedback. With the ability to optimize breathing and enhance patient care in critical healthcare situations, a hopeful development in airway management is needed.
Mobile application for distributing information to students at the Sciences and Humanities University
Authors: Condori-Obregon Patricia , Huallpa-Juarez Carlos , Palomino-Vidal Carlos
Published: November 2024

Currently, educational institutions around the world have implemented many standards and rules to ensure teaching quality. Many of these standards and rules are related to the use of technologies that provide students with services and facilities to learn. However, in Peru, a Latin American country, these standards and rules have been recently implemented, and as a result, information systems are required to guarantee teaching quality. This research exposes the implementation of a mobile application for distributing and managing information for students and teachers who require data about courses, grades, absences, and receive news about important university announcements. This work applied both research methods and Scrum methodologies together to demonstrate how the education process benefits from the use of technologies. As a result of these implementations, processes like finding academic information improved by an average of 50%. These results support that the implementation of mobile application technologies in educational environments is beneficial for guaranteeing process improvement and teaching quality.
Prediction of chronic diseases based on ML packages using spark MLlib
Authors: Oussous Aicha , Ez-Zahout Abderrahmane , Ziti Soumia
Published: November 2024

Heart disease, diabetes, and breast cancer pose significant global health challenges, and effectively addressing these chronic diseases necessitates a coordinated international effort. The integration of machine learning and predictive analytics offers promising solutions for tackling these issues. Our study presents a unified model that utilizes the random forest (RF) algorithm and SparkMLlib to predict these three diseases, testing the model on three distinct datasets and evaluating its performance using scientific metrics, including the receiver operating characteristic (ROC) curve, accuracy, precision, recall, and F1-score. Furthermore, we aim to investigate whether variations in medical data and contextual factors impact the results. The findings indicate that while the model shows strong overall performance, its effectiveness may differ for each disease due to factors such as data characteristics, disease-specific features, model behavior, and various biological and medical considerations; understanding these factors is essential for improving model performance and ensuring its appropriate use in clinical environments.
Educational impact and ethical considerations in using Chatbots in Academia
Authors: Ibrahim Dina M. , Al-harbi Njood K. , Al-Shargabi Amal A.
Published: November 2024

Chatbots are getting better every day due to the advancements in their capabilities in today’s technological age. This study aims to assess the efficacy of ChatGPT-4 and Gemini in producing scientific articles. Two types of prompts are given: direct questions and complete scenarios. Subsequently, we evaluate the educational and ethical aspects of the produced material by employing statistical analysis. We verify the credibility of references, detect any instances of plagiarism, and ensure the precision of the articles generated by the chatbot. In addition, we utilize topic modeling to assess the extent to which the content of the articles corresponds to the specified topic. According to the findings, Gemini outperformed ChatGPT-4, specifically in scenario questions, where it achieved an accuracy rate of 85%, while ChatGPT-4 only achieved 35% accuracy.
Modified-vehicle detection and localization model for autonomous vehicle traffic system
Authors: Juyal Amit , Sharma Sachin , Bhadula Shuchi
Published: November 2024

The modification of vehicles for financial gain is an evolving tendency observed in India. Recognizing and detecting of these modified illicit cars is an important but critical task in autonomous vehicles (AV). It is always possible for a cyclist or pedestrian to traverse obstacles or other fixed objects that appear in front of any moving vehicle. Vehicles that are autonomous or self-driving require a different system to quickly identify both stationary and moving objects. A deep learning model named you only look once version 5 (YOLOv5)-convolutional block attention module (CBAM) is proposed here for the Indian traffic system which is based on YOLOv5m. The proposed algorithm, YOLOv5-CBAM, has three major components. The first module, the backbone module is employed for feature extraction. The second module is to detect static as well as dynamic objects at the same time and the third CBAM module is adopted in the backbone and neck part, which mainly focuses on the more prominent features. Two cross stage partial (CSP) modules were used after every convolutional layer resulting in an additional head to the proposed model. Four head modules equipped with anchor boxes performed the final detection. For the present dataset, the proposed model showed 98.2% mean average precision (mAP), 98.4% precision, and 94.8% recall as compared to the original YOLOv5m.
Compressor performance prediction: gradient boosting regression model and sensitivity analysis
Authors: Liao Kuo-Chien , Wu Hom-Yu , Wen Hung-Ta , Sung Jui-Tang , Hidayat Muhamad , Wang Will Wei-Juen
Published: November 2024

This study introduces the use of gradient boosting regression (GBR) models to estimate the compressor performance of aero-engines. The model exhibits a mean absolute error (MAE) of 0.078, showcasing superior performance compared to previous studies. Through sensitivity analysis, optimal values for three key parameters were determined: 280 estimators, a max depth of 9, and a learning rate of 0.085. Furthermore, a comparison with a prior study revealed an impressive MAE value lower than 0.002, highlighting the GBR model’s success in accurately predicting compressor performance. This demonstrates the model’s effectiveness and predictive accuracy, making it a valuable tool for aero-engine compressor performance estimation.
Comparative analysis of machine and deep learning algorithms for semantic analysis in Iraqi dialect
Authors: Almufti Modher , Elamine Maryam , Belguith Lamia Hadrich
Published: November 2024

Text analytics, an essential component of artificial intelligence (AI) applications, plays a pivotal role in analyzing qualitative sentiments and responses in questionnaires, particularly for governmental and private organizations. Utilizing sentiment analysis enables a comprehensive understanding of people’s opinions, especially when expressed in lengthy texts in their native language, with minimal constraints. This study aims to identify the determinants of electronic service adoption among Iraqi citizens. A set of 1,695 questionnaires were distributed to Iraqi citizens; obtained 1,234 responses that were increased via data augmentation to 1,393 comments. Four machine learning (ML) and three deep learning (DL) algorithms Na¨ıve Bayes (NB), K-nearest neighboror machine (SVM), random forest (RF), as well as two variants of long-shortterm memory (LSTM) networks and convolutional neural networks (CNN) were employed to classify qualitative feedback. Following rigorous training and testing, the NB classification algorithm exhibited the highest accuracy, achieving 82.89%.
Systematic review for attack tactics, privacy, and safety models in big data systems
Authors: Chaymae Majdoubi , Youssef Gahi , Saida El Mendili
Published: November 2024

This systematic review explores cyberattack tactics, privacy concerns, and safety measures within big data systems, focusing on the critical challenges of maintaining data security in today's complex digital environments. The review begins by categorizing various cyberattacks, laying the groundwork for understanding the threats to big data. It identifies key vulnerabilities that compromise privacy and safety, and examines the ethical implications of these issues. The role of artificial intelligence in enhancing security defenses is highlighted as a crucial aspect of mitigating these threats. Additionally, a comparative assessment of regulatory frameworks such as GDPR, NIST, and ISO 27001 is provided, emphasizing the importance of legal and compliance considerations in data protection. The review concludes by proposing a structured approach to cyberattack detection and processing, advocating for strategies that address both technical vulnerabilities and regulatory requirements, followed by a critical discussion on the usability of previous methods for mobile security, highlighting adaptability and performance, discussing explainability and Gen AI adoption. This work offers valuable insights for researchers, practitioners, and policymakers, contributing to the ongoing discourse on cybersecurity in the big data era.
A joint learning classification for intent detection and slot filling with domain-adapted embeddings
Authors: Muhammad Yusuf Idris , Salim Naomie , Zainal Anazida
Published: November 2024

For dialogue systems to function effectively, accurate natural language understanding is vital, relying on precise intent recognition and slot filling to ensure smooth and meaningful interactions. Previous studies have primarily focused on addressing each subtask individually. However, it has been discovered that these subtasks are interconnected and achieving better results requires solving them together. One drawback of the joint learning model is its inability to apply learned patterns to unseen data, which stems from a lack of large, annotated data. Recent approaches have shown that using pretrained embeddings for effective text representation can help address the issue of generalization. However, pretrained embeddings are merely trained on corpus that typically consist of commonly discussed matters, which might not necessarily contain domain specific vocabularies for the task at hand. To address this issue, the paper presents a joint model for intent detection and slot filling, harnessing pretrained embeddings and domain specific embeddings using canonical correlation analysis to enhance the model performance. The proposed model consists of convolutional neural network along with bidirectional long short-term memory (BiLSTM) for efficient joint learning classification. The results of the experiment show that the proposed model performs better than the baseline models.
Research trends in spatial modeling of PM2.5 concentration using machine learning: a bibliometric review
Authors: Wahyuni Retno Tri , Hanafi Dirman , Tomari M. Razali , Sihabudin Sahid Dadang Syarif
Published: November 2024

Spatial modeling is commonly used to map research variables, including particulate matter 2.5 (PM2.5) concentrations, in specific areas. The article that surveys publications on the application of machine learning in spatial modeling of PM2.5 using bibliometric methods has not been identified yet. This paper aims to analyze trends in applying machine learning in the spatial modeling of PM2.5 using bibliometric methods. The review was conducted on publications indexed in the Scopus database over the decade (2014–2023) comprising 335 articles. The analysis included co-authorship and co-occurrence using VOSviewer. From the two stages of analysis, it can be concluded that research on this topic has constantly increased over the past 10 years, with the highest productivity coming from researchers in China. This research topic is multidisciplinary, with most publications appearing in environmental science. The research also shows a very high collaboration rate of 0.98. A deeper examination of the keywords reveals the most commonly used machine learning techniques by researchers. The random forest method is the most frequently found in the analyzed documents, followed by deep learning, long short-term memory (LSTM), extreme gradient boosting (XGBoost), and ensemble model.
Multi-class chronic lung disease classification based on guided backpropagation convolutional neural network using chest X-ray images
Authors: Raj Rakesh Selva , Madalu Palakshamurthy Pavan Kumar , Rangaswamy Bidarakere Eswarappa
Published: November 2024

Clinical diagnosis is crucial as chronic lung disease is a leading cause of mortality worldwide. Chest X-ray imaging is essential for the early and accurate diagnosis of lung diseases. However, due to the complexity of pathological abnormalities and detailed annotation, the computer-aided diagnosis of lung diseases is challenging. To overcome this challenge, this research proposes the guided backpropagation convolutional neural network (GBPCNN) for the classification of chronic lung disease into 14 classes, by adjusting the network’s weights in CNN layers. The GBP technique enhances result accuracy by pinpointing the regions in an input image. Initially, the chest X-ray radiography (CXR) dataset is collected for estimating the effectiveness of the classifier. After collecting the dataset, the pre-processing is performed by utilizing image denoising using gaussian filter and normalization techniques. Then, the pre-processed data is fed to the feature extraction process, and it is done by using EfficientNetB2. Finally, extracted features are provided to the classification process to categorize chronic lung disease into 14 classes. The experimental results show that the proposed GBPCNN method attains better results and it achieves the accuracy of 97.94% as compared to the existing approaches like MobileLungNetV2 and CX-Ultranet. These results highlight the potential of our approach for clinical applications.
Efficient deep learning approach for brain tumor detection and segmentation based on advanced CNN and U-Net
Authors: Baali Mehdi , Bourbia Nadjla , Messaoudi Kamel , Bourennane El-Bay
Published: November 2024

In this paper, we propose an innovative deep learning methodology dedicated to tumor detection and segmentation in medical images using convolutional neural networks (CNNs) and the U-Net architecture. The study emphasizes the importance of improving the quality and relevance of these features by employing advanced preprocessing methods. The subsequent development involves training a CNN model to achieve accurate tumor classification within the medical images. Among the various deep learning techniques proposed for medical image analysis, U-net-based models have gained significant popularity for multimodal medical image segmentation. However, due to the diverse shapes, sizes, and appearances of brain tumors, simple block architectures commonly used in segmentation tasks may not adequately capture the complexity of tumor boundaries and internal structures. The experimental results provide compelling evidence of the proposed approach's efficacy in accurately detecting and segmenting brain tumors. The results highlight the successful performance of the approach and its ability to achieve accurate tumor identification and segmentation.
Proposed strategies for lightning performance improvement of 35 kV distribution lines in Vietnam
Authors: Tung Tran Anh , Son Tran Thanh
Published: November 2024

Lightning strikes frequently cause power outages on 35 kV distribution lines in Vietnam. These lines was normally protected with a sparse density of surge arrester and typically do not make use of shield wire. On the other hand, a single rod, that is frequently used as the grounding electrode, is not suitable for some region with high value of soil resistivity. Therefore, it is of particular of interest to investigate the solutions to protect the medium voltage lines from the back-flashover due to lightning strikes by analyzing the impact of the grounding impedance, the surge arrester density and the installation of the shield wire. The finite element method and the electromagnetic transient program EMTP-RV are combined in this work to propose techniques for increasing the critical flashover current of 35 kV lines in Hanoi city. The obtained results showed that the installation of shield wires combined with high density of surge arrester and reduced grounding impedance value can achive a very significant improvement of the overall lightning performance of the line.
Two RC model and parameter estimation of lithium-ion battery
Authors: Joshi Girisha , Narayana Valluru Lakshmi , Prakash Khade Amol
Published: November 2024

Electric vehicles are the trend of this decade. Frequent high-power requirements of electric vehicles, make the batteries discharge at higher C rate. Discharging at a higher C rate will lead to higher heat production leading to destruction and explosion of the battery. To optimize the charging and discharging C rates considering both safety and performance factors, battery management system (BMS) is used as an eternal component of power source. To estimate the state of charge (SOC), which is an essential component of BMS, accurate battery modelling is required. Two RC model is one of the most used lithium-ion battery model, due to its simplicity and accuracy. The equivalent circuit parameters, resistances and capacitance do change with SOC and temperatures. This paper focuses on estimation of equivalent circuit parameters for a wide range of temperatures and SOCs ranging from -20 degree celsius to +25 degree celsius and 100 to 0 respectively. We have developed two RC model for Panasonic 18650PF and estimated the parameters of the model using hybrid pulse power characterization (HPPC) data. MATLAB based parameter estimator is used in determining the equivalent circuit parameters.
Analysis of LLC resonant converter performance with PIDD2 controller for electric vehicle application
Authors: K. Sathya , Guruswamy K. P.
Published: November 2024

The key uses of the latest developments is electric vehicles (EV’s). As a result, several researchers were drawn to EV’s control to propose appropriate controllers and predicted that control engineers face a challenge when it comes to regulating the LLC resonant converter output voltage. In this regard, the study proposes a PID Type modified controller for regulation of voltage across output in LLC resonant converter. The design and control procedure of this modified proportional integral derivative double derivative (PIDD2) is explained along with EDF modeling in LLC resonant converter. This work proposes to use two controllers to drive the voltage output of a resonant converter LLC to constantly track the desired value. Proportional integral derivative controller (PID) is the first, while the PIDD2 method is the foundation of the second. Every controller has undergone simulation testing and the results are compared based on how the evaluated controllers respond dynamically in accordance with settling time, rising time and overshoot.
Designing fuzzy membership functions using genetic algorithm with a new encoding method
Authors: Hamed Ali , Hireche Slimane , Bekri Abdelkader , Cheriet Ahmed
Published: November 2024

This article presents a new method for designing fuzzy membership functions using the genetic algorithm (GA) without the use of constraints. Conventional approaches to designing these functions often involve manual tuning or optimization techniques with limitations. However, this article introduces a constraint-free approach, as the GA requires all constraints to be met for a chromosome; if even one condition is not satisfied, the chromosome is discarded, regardless of its ideal values for other variables. Consequently, a high number of constraints, especially in the studied case, increases the likelihood of chromosome rejection, leading to a time-consuming design process and suboptimal results.
Breast cancer prediction using genetic algorithm and sand cat swarm optimization algorithm
Authors: Sangeetha Velu , Vaneeta Maniyambadi , Mamatha Arjuna , Shoba Muniya , Ramamurthy Deepa Sugatur , Sujatha Velusamy , Sujatha Shanmugam
Published: November 2024

Breast cancer is the second leading type of cancer, which is mainly found in women and which increases the death rate among women. Early detection and diagnosis of breast cancer can reduce its occurrence and the death rate. Unfortunately, even if cancer treatment is initiated quickly after diagnosis, cancer may relapse because cancer cells may continue to exist in the body, which is also a major problem faced by women who fear facing the same treatment twice. So, detecting cancer at its early stage and predicting the recurrence of it is a major issue in the medical field that needs to be solved. Machine learning (ML) algorithms such as support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and voting classifier (VC) are used for breast cancer prediction. Due to high-dimension data, the predicted results using Machine learning algorithms will increase the errors and decrease the accuracy. So, bioinspired algorithms such as the genetic algorithm (GA) and sand cat swarm optimization (SCSO) are used to reduce the data dimension. Convolutional neural network (CNN) is used for feature extraction from the image dataset. CNN algorithms are used for feature selection, which selects the important features for classification and prediction by applying 10 cross-validation methods. The proposed model using bioinspired optimization algorithms outcomes will yield high accuracy and the best solution.
Novel five-patch compact microstrip Yagi-alike antenna for Ka-band applications
Authors: Kumar Singh Raj , Mamta Kumari , Kumar Sinha Navin , Kumar Choudhary Vinay
Published: November 2024

This paper discusses the process of designing and fabricating a novel compact microstrip patch Yagi-like antenna having five-patch radiating element at operating frequency 31 GHz with a bandwidth of 1 GHz. The developed design aims to optimize the antenna performance. The overall dimension of the antenna being 17× 14 × 0.8 mm3, based on RT Duroid 5880 substrate having dielectric loss tangent of 0.0009 and relative permittivity 2.2. The effectiveness of the performance of proposed design was evaluated using the electromagnetic solver Ansoft high-frequency structure simulator (HFSS) and validated by the laboratory measurements on the antenna prototype. The measured results are consistent with the simulation prediction. The designed antenna achieved directional radiation and the performances with voltage standing wave ratio (VSWR) < 1.32, return loss -17 dB and gain of 6 dBi. The measured results are compared with those existing in literature. The proposed antenna design has proven very effective in terms of the intended design and parameters which make it suitable for satellite application and wireless communication.
SRCNN-based image transmission for autonomous vehicles in limited network areas
Authors: Afina Carmelya Anindya , Suryadi Satyawan Arief , Muhammad Suranegara Galura , Mirza Etnisa Haqiqi Mokhamamad , Susilawati Helfy , Alam Hamdani Nizar , Dani Prasetyo Adi Puput
Published: November 2024

High-quality images are crucial for navigation, obstacle detection, and environmental understanding, but transmitting high-resolution images over constrained networks presents significant challenges. This study introduces an image transmission system using super-resolution convolutional neural networks (SRCNN) to enhance image quality without increasing bandwidth requirements by transmitting low-resolution images and upscaling them with SRCNN. The first phase of the research involved data collection, in which information was acquired directly from an appropriate locus to produce training, validation, and testing datasets. The second, three SRCNN models (915, 935, and 955) were trained using such a training dataset. The last was an evaluation, in which model 915 showed quick learning and stable performance with initial high loss, while model 935 had rapid convergence but potential overfitting. Model 955 achieved high initial performance. Three SRCNN model configurations were tailored to the specific needs of autonomous electric vehicles operating in limited areas, such as the locus. Input image resolution ranged from 128×128 pixels to 256×256 pixels, while output resolution varied from 256×256 pixels to 512×512 pixels. These resolutions can be acceptable for efficient image transmission over IEEE 802.11ac, but on the long range (LoRa) network, it still produces some delay.
Implementation of augmented reality as a revolutionary approach in computer stores
Authors: Gunawan Dedi , Mutia Dawis Aisyah , Setiawan Ismail , Permatahati Ita , Ardhani Rahmad , Setiyanto Sigit
Published: November 2024

The adoption of augmented reality (AR) spans various fields, from education to business. Currently, many businesses utilize AR to boost customer engagement and enhance product understanding. This research focuses on developing and examining an AR application on a Solo computer store’s website to improve customer engagement. Results indicate that AR significantly enhances the shopping experience, deepens product comprehension, and increases website interactions. Features like 3D product visualization and detailed information enable customers to make more informed purchasing decisions. A questionnaire with 25 respondents revealed a high acceptance rate of the AR application, averaging 92%. Additionally, AR was shown to increase customer engagement, potentially boosting sales by up to 35%, reducing operating costs by 20%, and enhancing productivity by 15%. The study also found differing preferences across age groups: older respondents (40-70 years) favored traditional website features without AR and were less comfortable with markerless technology, whereas younger consumers (18-39 years) were more attracted to AR-enhanced websites. These insights offer valuable guidance for the Solo computer store to craft innovative marketing strategies tailored to the diverse preferences and needs of their customers.
Detect and envision of pandemic disease exposure using CNN
Authors: Rupa Ch. , Rama Prasad Kanakam Siva , Lakshmi Rajeswari Aremanda , Sambasiva Rao Elika , Sirajuddin Mohammad
Published: November 2024

COVID-19 has emerged as a pandemic, affecting millions globally with its high transmission rate, especially in colder climates. The virus's multiple mutations have made it progressively harder to detect and manage. Despite widespread awareness of preventive measures such as masks and sanitizers, early detection remains critical. Traditional methods like blood tests are time-consuming, and existing studies utilizing fuzzy K-means clustering, principal component analysis (PCA), stochastic discriminant analysis (SDA), decision trees (DT), and support vector machines (SVM) have faced limitations, including small datasets, insufficient accuracy, inadequate medical data, weak methodologies, and failure to consider primary symptoms. This work proposes a deep learning (DL) convolutional neural network (CNN) architecture utilizing CT scan images of the lungs for the rapid and accurate identification of COVID-19 infections. The approach leverages the Visual Geometry Group 16 (VGG16) model to extract significant features, such as size and color differences, from computed tomography (CT) scan images, facilitating a swift and precise diagnosis. The VGG16 model, implemented using the Keras library on top of TensorFlow, processes the preprocessed images through neural network layers to classify the images as COVID-19 positive or negative. The proposed model demonstrates a high accuracy rate of 94.12%, indicating that this method is both efficient and reliable for detecting COVID-19, offering a significant improvement over conventional diagnostic techniques and existing studies.
Evaluating low-cost internet of things and artificial intelligence in agriculture
Authors: Elhattab Kamal , Elatar Said
Published: November 2024

This article investigates the transformative impact of low-cost internet of things (IoT) solutions on the agricultural sector, with a particular emphasis on integrating artificial intelligence (AI) and machine learning (ML) technologies. The study aims to illustrate how affordable IoT technologies, when combined with advanced AI and ML capabilities, can serve as a significant asset for small and medium-sized farms. It addresses the economic and technical barriers these farms face in adopting such technologies, including high initial costs and the complexity of implementation. By conducting a comprehensive evaluation of existing IoT hardware and software, the research identifies and highlights innovative, cost-effective solutions that have the potential to drive significant advancements in agricultural practices. The findings underscore how these integrated technologies can enhance operational efficiency, increase productivity, and support sustainable agricultural development. Additionally, the paper explores the potential challenges and limitations of adopting these technologies, offering insights into how they can be mitigated. Overall, the study demonstrates that the convergence of low-cost IoT with AI and ML presents a valuable opportunity for modernizing agriculture and improving farm management.
Enhancing malware detection capabilities using deep learning with advanced hyperparameter tuning
Authors: El Mouhtadi Walid , Maleh Yassine , Mounir Soufyane
Published: November 2024

As the threat landscape evolves with sophisticated malware and advanced persistent threats (APTs), the need for effective detection solutions increases. Traditional methods, such as signature-based and heuristic analysis, struggle to keep up with rapidly changing malicious activities. While machine learning offers a promising approach, it often falls short due to the manual extraction and selection of features, leading to time-consuming and error-prone processes. This research introduces a novel malware detection solution leveraging deep learning and focusing on portable executable (PE) file analysis to address these weaknesses. By customizing the hyperparameters of artificial neural networks (ANN), convolutional neural networks (CNN), and recurrent neural networks (RNN), the proposed approach enhances detection capabilities. The primary objective is to overcome the limitations of traditional and machine learning methods by tailoring these deep learning algorithms. The methodology includes a comparative study to demonstrate the advantages of the customized approach over conventional methods. Key findings reveal the proposed solution’s superior performance, accuracy, and adaptability in combating evolving cyber threats. This research contributes to the development of robust and adaptive malware detection solutions.
Comparative study of pothole detection using deep learning on smartphone
Authors: Ulul Amri Achyar , Putra Kusuma Gede
Published: November 2024

Potholes present a significant problem in many countries, leading to vehicle damage and traffic accidents. These road imperfections pose safety risks and impose economic burdens. Despite existing detection methods using sensors and computer vision deep learning processed on PCs, a gap remains in deploying cost-effective, widely accessible solutions. This study aims to bridge this gap by developing deep learning models optimized for smartphones, reducing costs and enhancing deployment feasibility. We developed multiple models for pothole detection, utilizing transfer learning and Bayesian hyperparameter tuning to optimize detection accuracy and resource efficiency. Our evaluations focused on computationally light models such as YOLOv8 small, YOLOv8-nano, YOLOv7 tiny, and faster R-CNN MobileNetV3. In terms of detection accuracy, YOLOv8 small and YOLOv8 nano stood out, achieving average precisions (AP) of 83.5% and 82.5%, respectively. YOLOv8 nano proved the most efficient, offering high detection accuracy, a file size three times smaller than YOLOv8 small in TFLite format, and the fastest inference time of 0.72 seconds per image. This study highlights the potential of smartphones in urban pothole detection, contributing to improved road maintenance and urban policy.
Lesion Morphometry of the Pentaspline Pulsed Field Ablation Catheter: Understanding Catheter Pose, Rotation, and Dosing
Authors: Watanabe Keita , Nies Moritz , Reddy Vivek Y. , Koruth Jacob S.
Published: November 2024

BACKGROUND:

The pentaspline pulsed field ablation catheter achieves pulmonary vein isolation using 8 stacked, pose-specific applications with rotation. The morphology of pose-specific, single or double applications has not been described.

METHODS:

One or 2 applications were delivered to select veins and discrete atrial and ventricular sites in 9 swine. General anesthesia with neuromuscular paralysis ensured a stable position. Preablation, postablation, and serial intraprocedural mappings were performed to characterize electrical vein isolation and dynamic voltage changes. Upon euthanize at 2 days (7 of 9 swine), hearts were subject to pathological examination.

RESULTS:

Six superior vena cavae received single or double applications in basket pose without rotation. Despite incomplete dosing, acute electrical isolation occurred in 4 of 6 veins. None recovered conduction over 40 minutes, but all 3 isolated veins that were remapped at 2 days had reconnected. Lesions were linear (15–16 mm) with gaps and significantly wider with double versus single applications (8.2±2.8 versus 6.1±2.0 mm;
P
=0.02). Similar lesions, with acute isolation and subsequent reconnection, were observed in 3 of 4 ablated pulmonary veins. Double flower pose applications to the posterior atrium created wide (≈40×30 mm) confluent lesions, whereas single applications resulted in curvilinear lesions with variable gaps. Flower pose lesions (single or double) in the right ventricle were flower-shaped, linear with gaps, and with depths up to ≈5 mm.

CONCLUSIONS:

The pentaspline catheter in the basket pose creates perivenous linear lesions with gaps. Single applications in flower pose generate narrow linear lesions with variable gaps, whereas double applications result in wide, confluent lesions.
Out‐of‐Hospital Cardiac Arrest: Does a History of Myocarditis/Perimyocarditis Affect the Outcome? A Swedish Nationwide Study
Authors: Nadhir Sadek , Björkenstam Marie , Bobbio Emanuele , Eken Berkan , Ljungman Charlotta , Polte Christian , Rawshani Araz , Bollano Entela
Published: November 2024

Background

Acute myocarditis/perimyocarditis presents with a variable prognosis ranging from complete recovery to end‐stage heart failure (HF), sudden cardiac arrest, and death. The relationship between a prior history of myocarditis/perimyocarditis and outcomes in out‐of‐hospital cardiac arrest remains unclear.

Methods and Results

Using the SCRR (Swedish Cardiopulmonary Resuscitation Registry), we analyzed 54 568 cases of out‐of‐hospital cardiac arrest from 2010 to 2020 where cardiopulmonary resuscitation was attempted. Patients with a history of myocarditis/perimyocarditis were compared with those without in terms of characteristics and survival. Four hundred ninety‐eight patients (0.9%) had a history of myocarditis/perimyocarditis. These patients were predominantly men (73.8%), had an average age of 68 years, displayed a higher prevalence of cardiovascular comorbidities, and more frequently displayed shockable initial rhythms (28.7% versus 23.1%). Prior myocarditis/pericarditis did not correlate with worse short‐term (odds ratio [OR], 0.91 [95% CI, 0.61–1.33]) or long‐term survival (hazard ratio [HR], 1.01 [95% CI, 0.91–1.13]). Patients with myocarditis/perimyocarditis with concomitant HF showed worse long‐term survival in unadjusted analyses. Adjusted analyses confirmed that absence of HF was linked to improved short‐term survival (OR, 1.46 [95% CI, 1.32–1.62]), whereas prevalence of HF was a predictor for worsened long‐term survival (HR, 0.91 [95% CI, 0.91–0.95]) after out‐of‐hospital cardiac arrest. Analyses were adjusted for myocarditis/perimyocarditis, sex, age, HF, time to cardiopulmonary resuscitation start, and initial rhythm.

Conclusions

Prior myocarditis/perimyocarditis per se did not contribute to a worsened outcome following out‐of‐hospital cardiac arrest. However, the presence of concomitant HF was linked to unfavorable short‐ and long‐term outcomes.
Coğrafi Bilgi Sistemi Tabanlı Çok Kriterli Karar Verme Yöntemi ile Kaykay Parkı Yer Seçimi: Bolu Örneği
Authors: Mutlu Hakan Tahiri , Süvari Evren , Orhan Mehmet
Published: November 2024

Bu çalışmada, Coğrafi Bilgi Sistemi ve Çok Kriterli Karar Verme yöntemleri kullanılarak kaykay parkı yer seçimi süreci incelenmektedir. Çalışma kapsamında, Bolu ili örneği üzerinden Coğrafi Bilgi Sistemi yardımıyla yedi adet alternatif yer; belediye yöneticileri ve kaykay kullanıcılarından oluşan uzman ekip görüşüne başvurularak da kaykay parkında olması elzem olan yedi adet kriter belirlenmiştir. Uzman görüşlerinden elde edilen veriler, CRITIC ve ARAS yöntemleri ile analiz edilmiştir. Araştırmanın sonuçları, kaykay parkı yer seçiminde en etkili kriterlerin başında “diğer parklarla entegre olması” ve “alan büyüklüğü” geldiğini ortaya koymuştur. Diğer yandan, “ulaşım kolaylığı”, en az öneme sahip kriter olarak belirlenmiş; bu kriterler doğrultusunda en uygun alternatifin “Orman Parkı” olduğu ortaya çıkmıştır. Bu bulgular, benzer projelerin tasarımına ve yer seçimine rehberlik ederek, kaykay parklarıyla ilgili şehir planlaması ve spor tesisleri yönetimi alanlarında önemli bir kaynak sağlayabilir.
Assessment of urinary biomarkers of mycotoxin exposure in adults from Cameroon
Authors: Abia W.A. , Tchana A.N. , Djonabaye D. , Šarkanj B. , Mfopou E.Y. , Ezekiel C.N. , Sulyok M. , Turner P.C. , Elliott C.T. , Warth B. , Krska R. , Moundipa P.F.
Published: November 2024

In Cameroon, dietary staples are frequently contaminated with diverse toxic fungal metabolites, known as mycotoxins. Aflatoxins and fumonisins are a public health concern, particularly concerning cancer and/or early life stunting. Mycotoxin mixtures are predicted from food measures; and this study reports the levels and frequencies of urinary mycotoxin biomarkers in Cameroonian adults. A single first void urine sample was collected from 89 adults from Yaoundé, Cameroon. Urine samples were tested for eight distinct mycotoxins using measures of both parent compounds and/or their metabolites by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Altogether, seven distinct mycotoxins, aflatoxin, fumonisin, deoxynivalenol, zearalenone, nivalenol, ochratoxin A, and citrinin, (or their metabolites) were observed in urine samples. At least one mycotoxin was detected in all of the urine samples, 87 (98%) of which were above the limit of quantitation. Aflatoxin M1 was detected in 42% (n.d.-0.21 μg/l) of samples of which about a quarter additionally contained fumonisin B1. Of the remaining toxins deoxynivalenol (78%), zearalenone (99%), ochratoxin A (95%), nivalenol (53%), and citrinin (87%) were present in the samples. Alternariol was not detected in any sample. Mixtures of mycotoxins in the samples were frequently observed with 64 samples (72%) containing more than five mycotoxin exposure biomarkers. Estimates of intake exceeded the TDIs for fumonisin B1 (n = 4), deoxynivalenol (n = 1) and zearalenone (n = 2), no TDI is set for aflatoxin. This study reveals frequent co-exposure of Cameroonian individuals to a complex mixture of toxic and carcinogenic mycotoxins, with mixtures of aflatoxin and fumonisin being a particular priority from a public health standpoint.
Effect of edible insect powder incorporation on nutritional and sensory properties of food products: a systematic review
Authors: Barnabé M.L.F. , Abrantes L.C.S. , Fialho T.C. , Oliveira L.A. , Pereira S.M.S. , Dias K.A. , Vicente L.C.O.S. , Martins K.V.C. , Della Lucia C.M.
Published: November 2024

Society has an increasing demand for innovative food sources that are safe, nutritious, and, above all, more sustainable. In this scenario, edible insects emerge as a promising food alternative. However, there is still a scarcity of studies examining the effect of edible insect powder in formulating products for the food industry. This review analyzed studies that used powder obtained from various edible insects to elucidate its potential to enhance nutritional value in food products and to examine sensory and technological changes. Thirty-one papers that aimed to develop a new food product and conducted a sensory analysis were included. In most studies, an increase in protein and mineral content was observed, along with changes in sensory aspects such as color, taste, and texture, as well as a reduction in water activity. The results of this systematic review demonstrate that using edible insect powder as an ingredient can be considered a viable alternative to enhance the nutritional value of food products. However, this enrichment alters the sensory and technological characteristics of the final products.