bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling of Glugur Substation grounding systems using MATLAB graphical user interface
Authors: Roza Indra , Nugraha Yoga Tri , Rida Rizkha , Irwanto Muhammad , Othman Mohd. Azlishah
Published: November 2024

The grounding system in substations generally uses electrode rods, because electrodes can affect the effectiveness of fault current conduction, so the equipment will be safer. Considering the importance of the grounding system, the installed grounding system must be considered and maintained properly. One of them is the grounding found in Glugur. The main objective of this research is to comprehensively evaluate the substation grounding system by modeling the grounding system at the Glugur Substation using MATLAB graphical user interface (GUI). The grounding resistance follows a grid system with an area of 20×15=300 m² with specific resistance being clay using 100 rod electrodes. From the results of ground resistance simulation modeling using MATLAB GUI, it can be concluded as follows: for a certain resistance value, the number of electrodes for 100 Ωm is 3.55 Ω, for ground resistance with a constant depth and a varying number of 100 electrodes, it is 3.45 Ω, and for. The grounding resistance with a constant and varying number of 1,000 rods is obtained at 2.65 Ω. From these results, the modeling carried out is in accordance with the standards of electricity regulations in Indonesia.
HSPICE simulation and analysis of current reused operational transconductance amplifiers for biomedical applications
Authors: Chary Udari Gnaneshwara , Kishore Kakarla Hari
Published: November 2024

The proposed work focuses on the design of a current-reused biomedical amplifier; it is a microwatt-level electrocardiogram (ECG) analog circuit design that addresses low power consumption and noise efficiency. As implantable devices require unobtrusiveness and longevity, the current reuse technique in this circuit effectively enhances power and noise efficiencies. Using 90 nm technology enables efficient circuit implementation, yielding promising simulation results. At 100 Hz, the noise performance reaches 62.095 nV/√Hz, while the power consumption is only 8.3797 µW. These advancements are pivotal for next-generation implantable devices, ensuring reliable operation and reducing frequent battery replacements, improving patient convenience. Moreover, the high noise efficiency ensures that ECG signals are captured with high fidelity, crucial for accurate monitoring and diagnosis. This research addresses the challenges in implantable ECG analog circuit design and sets a benchmark for future developments. The techniques employed can be adapted for other bio signal monitoring devices, broadening the impact on healthcare technology. Ultimately, this advancement contributes to more efficient, reliable, and long-lasting medical devices, enhancing patient monitoring and healthcare on a broader scale.
Backstepping controller for speed loop of permanent magnet synchronous motors integrated with a time-varying disturbance load observer for Metro Nhon-Hanoi Station
Authors: Anh An Thi Hoai Thu , Thai Lam Quang , Minh Pham Duc
Published: November 2024

Urban rail systems offer the substantial potential for reducing environmental pollution, alleviating traffic congestion, ensuring safety, and maintaining punctuality. Nevertheless, the operation of urban rail demands substantial electrical energy, and saving energy solutions are crucial to exploiting the full advantages of electric trains. This paper proposes the replacement of traditional traction motors with permanent magnet synchronous motors (PMSMs) due to their superior efficiency, reduced power losses, and compact size compared to direct current (DC) motors or other asynchronous three-phase motors with equivalent power, developing a backstepping controller for the speed loop coupled with a load observer-time-varying disturbance (TVD). The simulation results were conducted in MATLAB/Simulink with parameters collected from the Nhon-Hanoi urban railway line, Vietnam, verifying the proposed algorithms' correctness and effectiveness.
Review of gait recognition systems: approaches and challenges
Authors: Mandlik Sachin B. , Labade Rekha , Chaudhari Sachin Vasant , Agarkar Balasaheb Shrirangao
Published: November 2024

Gait recognition (GR) has emerged as a significant biometric identification technique, leveraging an individual's walking pattern for various applications such as surveillance, forensic analysis, and person identification. Despite its non-intrusive nature, GR systems face challenges due to their sensitivity to pose variations, limiting functionality in real-world scenarios where people exhibit diverse walking styles and body orientations. This review paper aims to comprehensively discuss GR systems, focusing on approaches and challenges in designing accurate and robust systems capable of handling bodily variations. GR's prominence spans across domains including surveillance, security, healthcare, and human-computer interaction, positioning it as a versatile biometric modality complementary to the traditional methods like fingerprint and face recognition. The review offers an in-depth analysis of GR systems, detailing silhouette-based, model-based, and deep-learning approaches. Silhouette-based methods capture gait information by analyzing the outline and locomotion of a person’s silhouette, while model-based approaches utilize skeletal models to describe gait patterns. The paper elucidates the challenges and limitations of GR systems, encompassing factors such as walking conditions, clothing, viewpoint, and environmental influences. Additionally, it explores potential future directions in GR research, highlighting the technology’s ongoing evolution and integration into diverse applications. As a valuable resource, this review serves researchers, practitioners, and policymakers by providing insights into the current state of GR systems and avenues for further research and development. It underscores the importance of addressing challenges to enhance GR’s accuracy and robustness, ensuring its continued relevance in biometric identification across various domains.
ReRNet: recursive neural network for enhanced image correction in print-cam watermarking
Authors: Boujerfaoui Said , Douzi Hassan , Harba Rachid , Gourrame Khadija
Published: November 2024

Robust image watermarking that can resist camera shooting has gained considerable attention in recent years due to the need to protect sensitive printed information from being captured and reproduced without authorization. Indeed, the evolution of smartphones has made identity watermarking a feasible and convenient process. However, this process also introduces challenges like perspective distortions, which can significantly impair the effectiveness of watermark detection on freehandedly digitized images. To meet this challenge, ResNet50-based ensemble of randomized neural networks (ReRNet), a recursive convolutional neural network-based correction method, is presented for the print-cam process, specifically applied to identity images. Therefore, this paper proposes an improved Fourier watermarking method based on ReRNet to rectify perspective distortions. Experimental results validate the robustness of the enhanced scheme and demonstrate its superiority over existing methods, especially in handling perspective distortions encountered in the print-cam process.
Electrocardiogram features detection using stationary wavelet transform
Authors: Aqil Mounaim , Jbari Atman
Published: November 2024

The main objective of this paper is to provide a novel stationary wavelet transform (SWT) based method for electrocardiogram (ECG) feature detection. The proposed technique uses the detail coefficients of the ECG signal decomposition by SWT and the selection of the appropriate coefficient to detect a specific wave of the signal. Indeed, the temporal and frequency analysis of these coefficients allowed us to choose detail coefficient of level 2 (Cd2) to detect the R peaks. In contrast, the coefficient of level 3 (Cd3) is determined to extract the Q, S, P, and T waves from the ECG. The proposed method was tested on recordings from the apnea and Massachusetts Institute of Technology–Beth Israel hospital (MIT-BIH) databases. The performances obtained are excellent. Indeed, the technique presents a sensitivity of 99.83%, a predictivity of 99.72%, and an error rate of 0.44%. A further important advantage of the method is its ability to detect different waves even in the presence of baseline wander (BLW) of the ECG signal. This property makes it possible to bypass the filtering operation of BLW.
Enhanced global navigation satellite system signal processing using field programmable gate array and system-on-chip based software receivers
Authors: Kh Chetna Devi , Rao Malode Vishwanatha Panduranga
Published: November 2024

This paper presents a new approach to improving global navigation satellite system (GNSS) signal processing by using baseband processing techniques on field-programmable gate array (FPGA) platforms and a system-on-chip (SoC)-based GNSS software receiver. By leveraging the flexibility and computational power of FPGAs and the integration capabilities of SoC platforms, the method significantly enhances signal acquisition, tracking accuracy, and overall system performance. The integration of the ADFMCOMMS3-EBZ RF front end with the Zynq 7000 SoC board, along with high-speed parallel I/O and serial peripheral interface (SPI) for data management and configuration, enables efficient processing of high-speed signals. The study also explores wavelet transform techniques, such as the discrete wavelet transform (DWT), to improve filtering and noise reduction in GNSS signals. The results show that the proposed baseband processing algorithm for GNSS software-defined radio (SDR) reduces acquisition time and enhances tracking accuracy compared to traditional personal computer (PC)-based systems. Additionally, the SoC-based receiver is more energy-efficient and uses fewer resources. Comparative analysis shows that the proposed method provides more received samples, fewer dropped samples, and a lower data loss rate, confirming its effectiveness in boosting GNSS signal processing reliability and efficiency.
Enhancing single image dehazing with self-supervised convolutional neural network and dark channel prior integration
Authors: Hari Unnikrishnan , Bajulunisha Alla Bukshu , Pandey Pramod , Rexi Joseph Arul Michiline , Sujatha Velusamy , Raj Thankappan Saju , Velmurugan Athiyoor Kannan
Published: November 2024

The removal of noise from images holds great significance as clear and denoised images are vital for various applications. Recent research efforts have been concentrated on the dehazing of single images. While conventional methods and deep learning approaches have been employed for daytime images, learning-based techniques have shown impressive dehazing results, albeit often with increased complexity. This has led to the persistence of prior-based methods, despite their slightly lower performance. To address this issue, we propose a novel deep learning-based dehazing method utilizing a self-supervised convolutional neural network (CNN). This approach incorporates both the input hazy image and the dark channel prior. By leveraging an encoder, the combined information of the dark channel prior and haze image is encoded into a condensed latent representation. Subsequently, a decoder is employed to reconstruct the clean image using these latent features. Our experimental results demonstrate that our proposed algorithm significantly enhances image quality, as indicated by improved peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values. We perform both quantitative and qualitative comparisons with recently published techniques, showcasing the efficacy of our approach.
Evaluating the effectiveness of machine learning methods for keyword coverage using semantic data analysis
Authors: Shaushenova Anargul , Bayegizova Aigulim , Baidrakhmanova Gulnaz , Abuova Zhanargul , Kassymova Akmaral , Bakirova Dana , Golenko Yekaterina
Published: November 2024

This article presents a comprehensive comparative analysis of two advanced hybrid machine learning approaches for keyword extraction: bidirectional encoder representations from transformers (BERT) combined with autoencoder (AE) and term frequency-inverse document frequency (TF-IDF) combined with autoencoder. The research targets the task of semantic analysis in text data to evaluate the effectiveness of these methods in ensuring adequate keyword coverage across diverse text corpora. The study delves into the architecture and operational principles of each method, with a particular focus on the integration with autoencoders to enhance the semantic integrity and relevance of the extracted keywords. The experimental section provides a detailed performance analysis of both methods on various text datasets, highlighting how the structure and semantic richness of the source data influence the outcomes. The evaluation methodology includes precision, recall, and F1-score metrics. The paper discusses the advantages and disadvantages of each approach and their suitability for specific keyword extraction tasks. The findings offer valuable insights for the scientific community, aiding in the selection of the most appropriate text processing method for applications requiring deep semantic understanding and high accuracy in information extraction.
Ant lion and ant colony optimization integrated ensemble machine learning model for effective cancer diagnosis
Authors: Panda Pinakshi , Bisoy Sukant Kishoro , Panigrahi Amrutanshu , Pati Abhilash
Published: November 2024

Statistics from reputable sources, including the World Health Organization (WHO), demonstrate that cancer is a leading cause of death globally, accounting for millions of deaths each year. When it comes to the early identification of cancer, machine learning (ML) is crucial. To analyze complex data and identify minute patterns that may indicate the presence of cancer, it employs robust computational approaches. Improving patient outcomes relies on early cancer detection since it paves the way for faster treatment and intervention, which might lead to better prognoses and higher survival rates. To choose features, this study intends to build an ML-based ensemble model utilizing ant colony optimization (ACO) and ant lion optimization (ALO). Next, ML classifiers are used as the initial predictions' basis learners. The last forecast is the result of combining two ensemble methods: voting and averaging classifiers. Four distinct cancer microarray datasets are used to assess the approach. With an accuracy of 99.08% on the Lung cancer dataset, the voting ensemble classifier outperforms the others, according to the empirical analysis.
Estimation of harmonic impedance and resonance in power systems
Authors: Alashaary Haitham Ali , Al Shaba'an Ghadeer Nyazi , Abu Shehab Wael Fawzi , Ali Shehab Abdulwadood
Published: November 2024

Since power systems are designed to work at the fundamental frequency, the presence of other frequencies from various sources may induce series and parallel resonances, leading to damage. The behavior of the power system in the presence of harmonics becomes evident with knowledge of harmonic impedance. Measurement offers the most accurate means of estimating harmonic impedance. However, when precise data of the power system parameters are available, highly satisfactory results can be achieved through calculation methods, particularly regarding loads, which are unknown and always change. This paper presents a study on estimating harmonic impedance using the Electromagnetic Transients Program Alternative Transient Program Draw (EMTP-ATPDraw) program, applied to an authentic network of Petrovice line 67, 22/0.4 kV, located in the Czech Republic. Hypothetically, the network was subjected to harmonic injection from a source (3rd, 5th, 7th, 9th, and 11th harmonics), and the harmonic impedance was calculated for three different variants: individual harmonics, all harmonics, and all except the 9th harmonic. The results show that the presence of the 9th harmonic can lead to a parallel resonance. This study is the first to employ EMTP-ATPDraw for programming this network. It gives the possibility to create a network database for different operating conditions, offering an asset for future project planning.
Wireless sensor networks based efficient drip irrigation monitoring systems
Authors: Ashok Karthik Sagar , Nagendrappa Basavaraj Gangasamudra , Anjaneyalu Mohan Bangalore , Nandihal Priya , Reddy Veena Narayana , Syed Liyakathunisa , Noor Ayman
Published: November 2024

Cotton has profound significance in the textile industry due to its versatility, comfort and ease of care. But the main problem with conventional cotton farming is that it uses more water. These issues are made more difficult by conventional irrigation methods, such as drip irrigation. To address this problem researchers are using traditional farming techniques with advanced wireless sensor network (WSN) protocols to resolve catastrophic issues, such as pipe bursts or blocked emitters which are detected early to save the water. This paper introduces efficient WSN architecture using priority-based directed information sharing (DIS) protocol for efficient utilization of water. The proposed architecture was implemented using TinyOS sensor network (TOSSIM) simulators. Exceptional quality of service (QoS) is achieved using new routing protocol exclusively for catastrophic failures. The proposed architecture is compared with standard protocols such as topology geographic greedy forwarding (TPGF), link carrier sense avoidance (LinkCSA) and tiny carrier sense avoidance (TinyCSA). Due to implementation optimized priority, DIS latency has been reduced from 11.3% to 11.02% and packet delivery ratio (PDR) is enhanced by 35% to 78% concerning benchmark protocols. The experimental results proves drastic improvement in PDR and delay performance as compared to the existing WSN protocol.
Denigration analysis of Twitter data using cyclic learning rate based long short-term memory
Authors: Rajendra Suhas Bharadwaj , Kuzhalvaimozhi Sampath , Prasad Vedavathi Nagendra
Published: November 2024

Technological innovation has given rise to a new form of bullying, often leading to significant harm to one's reputation within social circles. When a single person becomes target to animosity and harassment in a cyberbullying incident, it is termed as denigration. Many different cyberbullying detection techniques are carried out to counter this, concentrating on word-based data and user account features only. The main objective of this research is to enhance the learning rate of long short-term memory (LSTM) using cyclic learning rate (CLR). Therefore, in this research, cyberbullying in social media is detected by developing a framework based on LSTM-CLR which is more stable for enhancing classification accuracy without the need for multiple trials and modifications. The effectiveness of the suggested LSTM-CLR is assessed for identifying cyberbullying using Twitter data. The attained results show that the proposed LSTM-CLR obtains 82% accuracy, 80% precision, 83% recall and 81% F-measure in the classification of cyberbullying tweets, which is superior when compared with the existing multilayer perceptron (MLP) and bidirectional encoder representations from transformers (BERT) models.
Analysis of big data from New York taxi trip 2023: revenue prediction using ordinary least squares solution and limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithms
Authors: Rhouas Sara , El Hami Norelislam
Published: November 2024

This study explores the prediction of taxi trip fares using two linear regression methods: normal equations (ordinary least squares solution (OLS)) and limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). Utilizing a dataset of New York City yellow taxi trips from 2023, the analysis involves data cleaning, feature engineering, and model training. The data consists of over 12 million records, managed, and processed that involves configuring the Spark driver and executor memory to efficiently process the Parquet-format data stored on hadoop distributed file system (HDFS). Key features influencing fare amount, such as passenger count, trip distance, fare amount, and tip amount, were analyzed for correlation. Models were trained on an 80-20 train-test split, and their performance was evaluated using root-mean-square error (RMSE) and mean squared error (MSE). Results show that both methods provide comparable accuracy, with slight differences in coefficients and training time. Additionally, vendor performance metrics, including total trips, average trip distance, fare amount, and tip amount, were analyzed to reveal trends and inform strategic decisions for fleet management. This comprehensive analysis demonstrates the efficacy of linear regression techniques in predicting taxi fares and offers valuable insights for optimizing taxi operations.
Enhancing online exam security: encryption and authentication in Jordanian and international universities
Authors: Al-Ghonmein Ali M. , Alemami Yahia , Al-Moghrabi Khaldun G. , Atiewi Saleh
Published: November 2024

In today's educational landscape, the online examination system has become crucial, particularly due to the challenges posed by the coronavirus disease 2019 (COVID-19) pandemic. Despite its advantages in expediting result dissemination and reducing resource consumption, online examinations face significant security threats like leakage, cheating, fraud, and hacking, which hinder their widespread adoption. This paper addresses these security concerns by proposing integrating advanced security algorithms and biometric devices. It presents a comprehensive literature review on existing online examination systems, focusing on their security mechanisms, and compares these findings with a proposed framework. Additionally, a questionnaire was administered across Jordanian governmental and private universities to explore strategies for safeguarding computerized tests through encryption and authentication methods. The results reveal that Jordanian institutions lack adequate security safeguards and procedural standards. Key recommendations include encrypting the question bank stored in databases and employing biometric identification techniques to enhance the security and effectiveness of student verification. The proposed framework aims to improve the overall security, speed, and secrecy of the online examination process, addressing the critical gaps identified in current systems. This research contributes to developing more secure and reliable online examination systems in higher education.
Federated public key infrastructure management for secure internet of things interoperability
Authors: Abdulkader Omar Ahmed , Ikram Muhammad Jawad
Published: November 2024

Proliferating the internet of things (IoT) across all industry fields offers numerous possibilities for invention. It also multiplied the issues of ensuring uniform interoperability among a wide range of devices and platforms. The focus of this paper is to propose an approach for enhancing the security of IoT networks. This study investigated the possible efficacy of employing a federal public key infrastructure (PKI) structure system to serve IoT-based ecosystems. To achieve this goal, we have developed an elaborate experimental framework incorporating different trust models, security protocols, privacy enhancements, and performance metrics that demonstrate the practical benefits of this kind of federated system. One of the contributions of this study is an experimental model that mimics a real IoT ecosystem. It entails many IoT devices, installing vital PKI elements, and the development of safe information transmission channels. Measurements such as latency pointed out the feasibility of various IoT concepts, including such short response time as 2.8 ms for vehicular IoT (V2X). Measures for interoperability ranged with V2X having a 96.4% success, indicating the strength of the standards within that segment. This study reveals the benefits of a federated PKI management system for solving issues of IoT interconnectedness.
Flooding distributed denial of service detection in software-defined networking using k-means and naïve Bayes
Authors: Yzzogh Hicham , Benaboud Hafssa
Published: November 2024

Software-defined networking (SDN) is a network architecture that enables the separation of the control plane and data plane, facilitating centralized management of the network. While centralized control offers numerous benefits, it also comes with certain drawbacks. Flooding distributed denial of service (DDoS) attacks pose a significant threat in SDN environments. These attacks involve overwhelming a target system with a large volume of packets, aiming to disrupt its functionality. In this paper, we propose a new approach for detecting DDoS attacks based on multiple k-means models and the naive Bayes algorithm. Our methodology involves training multiple k-means models to cluster each data point within every column of the dataset, where each column represents a feature. This process results in a new dataset with the same shape, containing only clusters, except the column containing the target variable (labels). These clusters are then used as input by naïve Bayes to perform binary classification. We assessed our approach using the InSDN and CIC-DDoS2017 datasets. The results underscore the impressive accuracy of our model, achieving 99.9839% on the InSDN dataset and 99.7030% on the CIC-DDoS2017 dataset. This performance was achieved by optimizing the desired number of clusters.
An innovative and efficient approach for searching and selecting web services operations
Authors: Rekkal Sara , Rekkal Kahina
Published: November 2024

The marketing of web services on the internet continues to increase, resulting in an increasing number of web services and, therefore, operations offering equivalent functionalities. As a consequence, finding an appropriate web service (operation) for a particular task has become a difficult challenge, taking a lot of time and leading to an insufficient selection of relevant services. This work aims to propose a new approach facilitating the search and localization of relevant web services (operations) in an acceptable time while ensuring the totality of the response. This approach is divided into three crucial phases. The first step involves collecting web services from various universal description, discovery, and integration (UDDI) registries and different domains and forming specialized sub-registries. The second phase involves the extraction of operations from various services, followed by a similarity study whose goal is the formation of clusters of similar operations. The third phase processes user requests by identifying the desired features. A list of operations is then provided to the client, including the non-functional properties, from which they select the one that best meets their needs and begin to invoke it.
Arabic fake news detection using hybrid contextual features
Authors: Turki Hussain Mohammed , Daoud Essam Al , Samara Ghassan , Alazaidah Raed , Qasem Mais Haj , Aljaidi Mohammad , Abuowaida Suhaila , Alshdaifat Nawaf
Published: November 2024

Technology has advanced and social media users have grown dramatically in the last decade. Because social media makes information easily accessible, some people or organizations distribute false news for political or commercial gain. This news may influence elections and attitudes. Even though English fake news is widely detected and limited, Arabic fake news is hard to recognize owing to a lack of study and data collection. Wara Arabic bidirectional encoder representations from transformers (WaraBERT), a hybrid feature extraction approach, combines word level tokenization with two Arabic bidirectional encoder representations from transformers (AraBERT) variants to provide varied features. The study also discusses eliminating stopwords, punctuations, and tanween markings from Arabic data. This study employed two datasets. The first, Arabic fake news dataset (AFND), has 606,912 records. Second dataset Arabic news (AraNews) has 123,219 entries. WaraBERT-V1 obtained 93.83% AFND accuracy using the bidirectional long short-term memory (BiLSTM) model. However, the WaraBERT-V2 technique obtained 81.25%, improving detection accuracy above previous researchers for the AraNews dataset. These findings show that WaraBERT outperforms word list techniques (WLT), term frequency-inverse document frequency (TF-IDF), and AraBERT on both datasets.
Investigating challenges in Agile software development: a cross-country comparative analysis
Authors: Fissalma Hanifa , Ferdinansyah Alex , Purwandari Betty
Published: November 2024

Agile software development has been a significant methodology in software engineering for over two decades, offering enhanced adaptability to software requirement changes, improved delivery, and better quality. However, prevalent misunderstandings in Agile implementation have limited its benefits. Hence, this study investigates the challenges faced during Agile implementation. Initial analysis was carried out from cases in Indonesia, a leading country in the software industry within the Asia-Pacific region. Using Kitchenham’s systematic literature review (SLR) methodology, fourteen distinct obstacles were identified from a database of research reports on Agile software development in Indonesia. Subsequent interviews with Agile experts in the country were conducted to validate the SLR findings. The results emphasize the critical need for a top-down strategic approach, the active participation of senior management, and the essential roles of competent scrum masters or Agile coaches. A comparative analysis with reports from other developing countries Saudi Arabia, India, Malaysia, Brazil and developed countries the UK, Belgium, Singapore, and the USA reveals common challenges. They highlight the imperative for proactive upper management leadership to steer successful Agile adoptions, particularly in organizations with entrenched top-down practices and complex hierarchical systems.
Design of a road marking violation detection system at railway level crossings
Authors: Susilawati Helfy , Nurpadillah Sifa , Sediono Wahju , Nurdin Agung Ihwan
Published: November 2024

When a train passed through a railway-level crossing, a common phenomenon was that many vehicles attempted to overtake others by crossing into lanes designated for oncoming traffic, resulting in both roads becoming congested with motorized vehicles. At that time, no system was in place to enforce penalties for violating road markings at level crossings. Therefore, a system capable of detecting such violations when trains pass through was needed. The designed system utilized a Raspberry Pi 4, a webcam, and an ultrasonic sensor. The single shot detector (SSD) method was employed for vehicle classification. The optical character recognition (OCR) method was used for character recognition on license plates. The research involved object detection at level crossings using varied objects (cars and motorcycles) with license plates categorized into two types: white background plates with black numbers and black background plates with white numbers. Based on the research results, turning on the webcam when the bar opened and closed using an ultrasonic sensor got an average error of 0.573% and 0.582%. The system could distinguish objects with an average recognition delay of 0.554 seconds and 0.702 seconds for car and motorbike objects. Regarding number plate detection, the success rate of character recognition stood at 64.45%.
Comparison of machine learning algorithms to identify and prevent low back injury
Authors: Paulino Christian Ovalle , Correa Jorge Huamani
Published: November 2024

With the advancement of technology, remote work and virtual classes have become increasingly common, leading to prolonged periods in front of computers and, consequently, to discomfort and even lower back pain. This study compares machine learning algorithms to identify and prevent low back pain, a common health problem. A predictive model for early diagnosis and prevention of these injuries was developed using datasets from open data repositories. Six machine learning models were used to train the data. Results showed that logistic regression was the most effective model, with performance curves of 70%, 90%, and 99%. Performance metrics indicated 86% accuracy, 85% recall, and 86% F1-score. Accuracy of 70%, recall of 71%, and F1-score of 63% reflect the robust ability of the model to address the problem. In addition, an intuitive interface was implemented using Gradio Software to improve data visualization.
Tomato plant disease prediction system with a new framework SSMAN using advanced deep learning techniques
Authors: Sivalingam Saravanan Madderi , Badabagni Lakshmi Devi
Published: November 2024

Agriculture plays a pivotal role in India's economy, and the timely detection of plant infections is essential to safeguard crops and prevent further spread of diseases. The conventional approach involves manual inspection of plant leaves to identify the specific type of disease, a task typically carried out by farmers or plant pathologists. In previous studies, you only look once (YOLO) and faster region-based convolutional neural network (R-CNN), machine learning algorithms were applied to datasets for detecting objects on tomato leaves which includes a total of images 2403 and got accuracies of 86 and 82 percent. In this paper, a deep convolutional neural network (DCNN) model proposed with a new framework separate, shift, and merge based AlexNet50 algorithm (SSMAN) is used to predict the disease at an earlier stage with higher accuracy. Among various pre-trained deep models, AlexNet emerges as the top performer, achieving the highest accuracy in disease classification. SSMAN can address anomalies in images by employing a class decomposition approach to scrutinize class boundaries. AlexNet exhibits a notable accuracy of 98.30% in successfully identifying tomato leaf diseases from images, with pre-trained new framework, superior to the original AlexNet architecture as well as traditional classification methods with other algorithms.
Enhancing sentiment analysis through deep layer integration with long short-term memory networks
Authors: Dubey Parul , Dubey Pushkar , Gehani Hitesh
Published: November 2024

This involves studying one of the most important parts of natural language processing (NLP): sentiment, or whether a thing that makes a sentence is neutral, positive, or negative. This paper presents an enhanced long short-term memory (LSTM) network for the sentiment analysis task using an additional deep layer to capture sublevel patterns from the word input. So, the process that we followed in our approach is that we cleaned the data, preprocessed it, built the model, trained the model, and finally tested it. The novelty here lies in the additional layer in the architecture of LSTM model, which improves the model performance. We added a deep layer with the intention of improving accuracy and generalizing the model. The results of the experiment are analyzed using recall, F1-score, and accuracy, which in turn show that the deep-layered LSTM model gives us a better prediction. The LSTM model outperformed the baseline in terms of accuracy, recall, and f1-score. The deep layer's forecast accuracy increased dramatically once it was trained to capture intricate sequences. However, the improved model overfitted, necessitating additional regularization and hyperparameter adjustment. In this paper, we have discussed the advantages and disadvantages of using deep layers in LSTM networks and their application to developing models for deep learning with better-performing sentiment analysis.
Berkeley wavelet transform and improved YOLOv7-based classification technique for brain tumor severity prediction
Authors: Bahadure Nilesh Bhaskarrao , Routray Sidheswar , Patni Jagdish Chandra , Raju Nagrajan , Patil Prasenjeet Damodar
Published: November 2024

Abnormality in brain tissues is a life-threatening illness in humans Un-bias to gender and age if it is unrecognized and untreated within time, will lead to severe complications and extreme conditions. The brain tumor is mainly influenced by a variety of unpredicted and unavoidable reasons. Its evaluation, spread pattern, and identification involves complex assignment. Its early grading and the proper classification ensure effective treatment. The proposed work attempts to extract and classify the tumor region using an automatic classification system for magnetic resonance imaging (MRI) brain tumors. A deep learning convolutional neural network-based architecture YOLO is employed to classify and detect the tumor from brain MR images. The proposed method resulted in superior segmentation, and classification performance in terms of subjective visualization and objective metrics as compared to state of art approaches. The proposed YOLO-based method collectively achieved 98.89% classification accuracy on the BRAINIX and Kaggle datasets.
Innovative power sharing and secondary controls for meshed microgrids
Authors: Ben Hassi Youssef Amine Ait , Hennane Youssef , Berdai Abdelmajid
Published: November 2024

In alternating current (AC) microgrids, the prevalent approach for controlling the power distribution between generators and loads is droop control. This decentralized technique ensures accurate power sharing; however, its utility is restricted by significant drawbacks. Notably, in scenarios involving dissimilar power sources, mismatched impedance lines, or meshed microgrids, conventional droop control fails to ensure effective reactive power sharing among inverters, often leading to notable circulating currents. Hence, the primary objective of this paper is twofold: firstly, to examine limitations inherent to conventional droop control; secondly, to introduce a robust power-sharing methodology for AC microgrids. This novel approach is specifically designed to achieve consistent sharing of active and reactive power across meshed topology microgrids. The technique considers the presence of distributed power loads and the dynamic nature of the topology. Despite the attainment of satisfactory active and reactive power sharing, deviations in voltage and frequency occasionally manifest. To address this issue, a supplementary control mechanism is proposed as a third phase. This secondary control method focuses on reinstating the microgrid's voltage and frequency to rated values, all while upholding the precision of power sharing. The efficacy of this multi-stage methodology is rigorously validated through simulations using MATLAB/Simulink and practical experimentations.
Hybrid deep learning for estimation of state-of-health in lithium-ion batteries
Authors: Cahyani Denis Eka , Gumilar Langlang , Afandi Arif Nur , Wibawa Aji Prasetya , Junoh Ahmad Kadri
Published: November 2024

Lithium-ion (li-ion) batteries have a high energy density and a long cycle life. Lithium-ion batteries have a finite lifespan, and their energy storage capacity diminishes with use. In order to properly plan battery maintenance, the state of health (SoH) of lithium-ion batteries is crucial. This study aims to combine two deep learning techniques (hybrid deep learning), namely convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), for SoH estimation in li-ion batteries. This study contrasts hybrid deep learning methods to single deep learning models so that the most suitable model for accurately measuring the SoH in lithium-ion batteries can be determined. In comparison to other methodologies, CNN-BiLSTM yields the best results. The CNN-BiLSTM algorithm yields RMSE, mean square error (MSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) in the following order: 0.00916, 0.000084, 0.0048, and 0.00603. This indicates that CNN-BiLSTM, as a hybrid deep learning model, is able to calculate the approximate capacity of the lithium-ion battery more accurately than other methods.
Primeiras Edições de Machado de Assis na Biblioteca Brasiliana Guita e José Mindlin
Authors:   Universidade de São Paulo, Guimarães Hélio de Seixas , Lebensztayn Ieda ,   Universidade de São Paulo, Schoeps Luciana Antonini ,   Universidade de São Paulo
Published: November 2024

A conjunção entre as obras de Machado de Assis e uma preciosa coleção das primeiras edições de seus livros resultou neste volume que está em suas mãos. Se “a alegria é a prova dos nove”, como escreveu Oswald de Andrade em famoso manifesto, ela é também a marca dos livros reunidos ao longo de décadas pelo bibliófilo José Mindlin e sua esposa, Guita. “Je ne fais rien sans gayeté” (Não faço nada sem alegria) é a frase de Michel de Montaigne inscrita nos ex-libris que acompanham cada exemplar das 49 raridades reunidas na Machadiana da bbm, guardada na Universidade de São Paulo. Neste livro-catálogo fartamente ilustrado e documentado, cada exemplar é apresentado ao leitor em detalhe. O conjunto revela a diversidade e a longevidade da trajetória de Machado de Assis, que compreende obras-primas como Memórias Póstumas de Brás Cubas e Dom Casmurro, e títulos hoje pouco conhecidos, que surpreenderão e alegrarão qualquer amante da literatura e dos livros.
Resenha de Almeida Napoleão Mendes de Gramática Latina. 2024. Rio de Janeiro: CDB.
Authors: Vasconcellos Paulo Sérgio de
Published: November 2024

I offer in this text a review of Napoleão Mendes de Almeida's "Latin grammar", reprinted in 2024. I think that one of the social activities of classicists if to analyse critically didatic material that is on the market in order to contribute to its improvement in next reeditions. In this process, a critical review may help self-taught people  and beginners in the learning of Latin language by providing them significant information about a book that is a well-kown editorial success,
Mavacamten in Patients With Hypertrophic Cardiomyopathy Referred for Septal Reduction: Week 128 Results from VALOR-HCM
Authors: Desai Milind Y. , Wolski Kathy , Owens Anjali , Geske Jeffrey B. , Saberi Sara , Wang Andrew , Sherrid Mark , Cremer Paul C. , Lakdawala Neal K. , Tower-Rader Albree , Fermin David , Naidu Srihari S. , Smedira Nicholas G. , Schaff Hartzell , Gong Zhiqun , Mudarris Lana , Lampl Kathy , Sehnert Amy J. , Nissen Steven E.
Published: November 2024

Background: In severely symptomatic patients with obstructive hypertrophic cardiomyopathy (HCM), VALOR-HCM trial (Study to Evaluate Mavacamten in Adults With Symptomatic Obstructive HCM Who Are Eligible for Septal Reduction Therapy [URL: https://clinicaltrials.gov; Unique identifier: NCT04349072]) reported that mavacamten reduced the short-term need for septal reduction therapy (SRT). The current report examined the longer-term effect of mavacamten through end of treatment at week 128.

Methods: A double-blind randomized placebo-controlled multicenter trial at 19 sites in the United States included symptomatic obstructive HCM patients referred for SRT (enrollment July 2020 through October 2021). The group initially randomized to mavacamten continued the drug for 128 weeks and the placebo to mavacamten group from week 16 to 128 (112-week exposure). Dose titrations were performed using echocardiographic left ventricular outflow tract gradient and left ventricular ejection fraction measurements. The principal end point was proportion of patients proceeding with SRT or remaining guideline-eligible at week 128.

Results: At week 128, 17 of 108 (15.7%) patients in the total study sample met the composite end point (7 underwent SRT, 1 was SRT-eligible, and 9 SRT-status unevaluable). Additionally, 87 of 108 (80.5%) patients demonstrated ≥1 New York Heart Association class improvement by week 128, and 52 of 108 (48.1%) demonstrated ≥2, with a sustained reduction in resting and Valsalva left ventricular outflow tract gradients of 38.2 mm Hg and 59.4 mm Hg, respectively. Ninety-five of 108 (88%) patients transitioned to commercial mavacamten. Overall, 15 of 108 (13.8%) patients (5.41 per 100 patient-years) had an left ventricular ejection fraction <50% (2 with left ventricular ejection fraction ≤30%; 1 death). Of these, 12 of 15 (80%) continued treatment. New-onset atrial fibrillation occurred in 11 (10.2%) patients (4.55 per 100 patient-years).

Conclusions: In severely symptomatic obstructive HCM patients, sustained freedom from SRT was observed at 128 weeks, with nearly 90% patients remaining on long-term mavacamten.
The Bible and the Mission: Mission of Japanese Missionaries in Korea under Japanese Imperialism, 1896–1921
Authors: You Grace
Published: November 2024

This article examines the contrasting ways in which Korean Christians and Japanese missionaries interpreted the Bible during the Japanese occupation of Korea (1896–1921). Drawing on primary sources from both Japanese and Korean perspectives, the article highlights how biblical interpretation became a site of both ideological struggle and resistance. While Japanese missionaries often employed biblical narratives to legitimize Japanese imperialism and discourage Korean nationalism, Korean Christians found solace and inspiration in the Old Testament’s themes of liberation, drawing parallels between their own situation and the Israelites’ struggle for freedom. The Japanese government's prohibition on reading the Old Testament further exacerbated these contrasting interpretations, with Japanese missionaries generally supporting the ban and Korean Christians finding ways to resist it. By analysing these divergent interpretations, the article illuminates the complex interplay between mission, Bible, and imperialism in early twentieth-century colonial context of Korea. The article argues that the Korean case study offers a powerful historical example of how important it is that missionaries not only defend their own use of the Bible, but also remain open to the contextual interpretations of others.
Macromarketing Pedagogy at the 2024 Conference
Authors: Stanton Julie V. , Shapiro Stanley J.
Published: November 2024

This commentary summarizes the presentations made during three Macromarketing Pedagogy sessions at the 2024 Macromarketing Conference held in Helsinki, Finland, in June. Presentations focused on (1) broad business school challenges in teaching macromarketing and sustainability; (2) the value of specific tools for a broad set of marketing courses; and (3) illustration of tools being applied to specific marketing courses. Macromarketers are encourage to read the full abstracts or papers on the Macromarketing Society's website.
Modeling and Analysis of Mixed Traffic Networks with Human-Driven and Autonomous Vehicles
Authors: Xu Qing , Chen Chaoyi , Chang Xueyang , Cao Dongpu , Cai Mengchi , Wang Jiawei , Li Keqiang , Wang Jianqiang
Published: November 2024

The emergence of connected and automated vehicles (CAV) indicates improved traffic mobility in future traffic transportation systems. This study addresses the research gap in macroscopic traffic modeling of mixed traffic networks where CAV and human-driven vehicles coexist. CAV behavior is explicitly included in the proposed traffic network model, and the vehicle number non-conservation problem is overcome by describing the approaching and departure vehicle number in discrete time. The proposed model is verified in typical CAV cooperation scenarios. The performance of CAV coordination is analyzed in road, intersection and network scenario. Total travel time of the vehicles in the network is proved to be reduced when coordination is applied. Simulation results validate the accuracy of the proposed model and the effectiveness of the proposed algorithm.