bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimized automated testing: test case generation and maintenance using latent semantic analysis-based TextRank and particle swarm optimization algorithms
Authors: Swathi Baswaraju , Kolisetty Soma Sekhar
Published: June 2024

Software development would have to include automated testing to ensure the finished product and performs as intended. However, the process of Test Case Generation and Maintenance can be time-consuming and error-prone, especially when manual methods are used. This research proposes a new approach to improve the efficiency and accuracy of automated testing using latent semantic analysis (LSA)-based TextRank (TR) and particle swarm optimization (PSO) algorithms. The study aims to evaluate the effectiveness of these algorithms in generating and optimizing test cases based on requirements analysis. To retrieve key information from the criteria, methods including text classification (TC), named entity recognition (NER), and sentiment analysis (SA) are used to evaluate the text. Test cases are then generated using LSA-based TR for text summarization and PSO for optimization. The aim of this work is to identify any limitations that need to be addressed and to evaluate the overall efficiency and accuracy of automated testing (AT) using proposed algorithms. The results of this research are expected to have important implications for the software industry, helps to improve the overall efficiency and accuracy of AT. The findings could guide future research that led to the creation of more advanced and effective tools for AT.
Object-based image retrieval and detection for surveillance video
Authors: Jagtap Swati , Chopade Nilkanth B.
Published: June 2024

With technological advancement worldwide, the video surveillance market is growing drastically in a versatile field. Monitoring, browsing, and retrieving a specific object in a long video becomes difficult due to the enormous amount of data produced by the surveillance camera. With limitations on human resources and browsing time, there is a need for a new video analytics model to handle more complex tasks, such as object detection and query retrieval. The current approach involves techniques like unsupervised segmentation, multiscale segmentation, and feature-based descriptions. However, these methods often encounter extensive space and time consumption challenges. A solution has been developed for retrieving targeted objects from surveillance videos via user queries, employing a graphical interface for input. Extracting relevant frames based on user-entered text queries is enabled through using YOLOv8 for object detection. Users interact through a graphical user interface deployed on a Jetson Xavier Development board. The system's outcome is a time-efficient and highly accurate automated model for object detection and query retrieval, eliminating human errors associated with manually locating objects in videos upon user queries.
Enhancement of detection accuracy for preventing iris presentation attack
Authors: Venkatesh Priyanka , Shyam Gopal Krishna , Alam Sumbul
Published: June 2024

A system that recognizes the iris is susceptible to presentation attacks (PAs), in which a malicious party shows artefacts such as printed eyeballs, patterned contact lenses, or cosmetics to obscure their personal identity or manipulate someone else’s identity. In this study, we suggest the dual channel DenseNet presentation attack detection (DC-DenseNetPAD), an iris PA detector based on convolutional neural network architecture that is dependable and effective and is known as DenseNet. It displays generalizability across PA datasets, sensors, and artifacts. The efficiency of the suggested iris PA detection technique has been supported by tests performed on a popular dataset which is openly accessible (LivDet-2017 and LivDet-2015). The proposed technique outperforms state-of-the-art techniques with a true detection rate of 99.16% on LivDet-2017 and 98.40% on LivDet-2015. It is an improvement over the existing techniques using the LivDet-2017 dataset. We employ Grad-CAM as well as t-SNE plots to visualize intermediate feature distributions and fixation heatmaps in order to demonstrate how well DC-DenseNetPAD performs.
Exploring the research trends and development of augmented reality and virtual reality in ASEAN countries: a bibliometric study
Authors: Hariyanto Didik , Rafiq Arif Ainur , Gunawan Teddy Surya , Quynh Nguyen Vu
Published: June 2024

This review of Association of Southeast Asian Nations (ASEAN) augmented reality (AR) and virtual reality (VR) studies uses bibliometric analysis and VOSviewer mapping. This study looks at an extensive set of Scopus articles from reliable sources to determine who contributes to ASEAN AR and VR research, the themes, how people work together, and how people cite each other. A study of bibliographies shows that the number of ASEAN AR and VR research articles has grown significantly since 2010. It also talks about important ASEAN study institutions, authors, and countries. The study themes are shown visually on VOSviewer mapping, showing how AR and VR can be used in healthcare, travel, gaming, and business. Co-authorship and reference networks shed light on how people work together on research projects and how ideas move within and outside of ASEAN. This organized review of ASEAN AR and VR research helps researchers, policymakers, and business stakeholders understand the current situation, find research gaps, and work together. The results can change research, resource use, and policy changes to encourage the growth and use of AR and VR technologies in ASEAN. It can lead to more innovation, economic development, and positive social effects.
Cardiovascular disease risk factors prediction using deep learning convolutional neural networks
Authors: Almatari Mohammad , Abuhaija Belal , Alloubani Aladeen , Haddad Firas , M. Jaradat Ghaith , Qawqzeh Yousef , Alsmadi Mutasem Khalil , Ali Alghamdi Fahad , Saad Alqurni Jehad , Alodat Lena , Dong Linyinxue
Published: June 2024

Heart disease remains a leading cause of mortality worldwide, prompting healthcare researchers to leverage analytical tools for comprehensive data analysis. This study focuses on exploring crucial parameters and employing deep learning (DL) techniques to enhance understanding and prediction of cardiovascular disease (CVD) risk factors. Utilizing SPSS and Weka tools, a cross-sectional and correlational design was employed to analyze extensive medical datasets. Binomial regression analysis revealed significant associations between age (𝑝 = 0.004) and body mass index (𝑝 = 0.002) with CVD development, highlighting their importance as risk factors. Leveraging Weka's DL algorithms, a predictive model was constructed to classify CVD causes. Particularly, convolutional neural networks (CNN) showcased remarkable accuracy, reaching 98.64%. The findings underscore the elevated risk of CVD among university students and employees in Saudi Arabia, emphasizing the need for heightened awareness and preventive measures, including dietary improvements and increased physical activity. This study underscores the importance of further research to enhance CVD risk perception among students and individuals in similar settings.
Prediction of novel malware using hybrid convolution neural network and long short-term memory approach
Authors: Pachhala Nagababu , Jothilakshmi Subbaiyan , Battula Bhanu Prakash
Published: June 2024

The rapid evolution of network communication technologies has led to the emergence of new forms of malware and cybercrimes, posing significant threats to user safety, network infrastructure integrity, and data privacy. Despite efforts to develop advanced algorithms for detecting malicious activity, constructing models that are both accurate and reliable remains a challenge, especially in handling vast and dynamically shifting data patterns. The prevalent bag-of-words (BOW) method, while widely used, falls short in capturing crucial spatial and sequence information vital for detecting malware patterns. To address this challenge, the work presented in this paper proposes hybrid convolution neural network-long short-term memory network (CNN-LSTM) combination models, leveraging CNN's spatial information extraction and LSTM's temporal modeling capabilities. Focused on predicting the infiltration of malicious software into personal computers, the proposed hybrid CNN-LSTM model considers factors such as location, firmware version, operating system, and anti-virus software. The proposed models undergo training and evaluation using Microsoft's malware dataset, demonstrating superior performance compared to traditional CNN and LSTM models. The CNN-LSTM model achieves an impressive accuracy of 95% on the Microsoft malware dataset, highlighting its effectiveness in malware detection.
Building extraction from remote sensing imagery: advanced squeeze-and-excitation residual network based methodology
Authors: Ait El Asri Smail , Negabi Ismail , El Adib Samir , Raissouni Naoufal
Published: June 2024

Extracting buildings from remote sensing imagery (RSI) is an essential task in a wide range of applications, such as urban and monitoring. Deep learning has emerged as a powerful tool for this purpose, and in this research, we propose an advanced building extraction method based on SE-ResNet18 and SE-ResNet34 architectures. These models were selected through a rigorous comparative analysis of various deep learning models, including variations of residual networks (ResNet), squeeze-and-excitation residual networks (SE-ResNet), and visual geometry group (VGG), for their high performance in all metrics and their computational efficiency. Our proposed methodology outperformed all other models under consideration by a significant margin, demonstrating its robustness and efficiency. It achieved superior results with less computational effort and time, a testament to its potential as a powerful tool for semantic segmentation tasks in remote sensing applications. An extensive comparative evaluation involving a wide range of state-of-the-art works further validated our method’s effectiveness. Our method achieved an unparalleled intersection over union (IoU) score of 88.51%, indicative of its exceptional accuracy in identifying and segmenting buildings within the Wuhan University (WHU) building dataset. The overall performance of our method, which offers an excellent balance between high performance and computational efficiency, makes it a compelling choice for researchers and practitioners in the field.
Diagnosis of patients with chronic heart failure implementing wavelet transform and machine learning techniques
Authors: Arizmendi Carlos , Reinemer Jhon , Gonzalez Hernando , Giraldo Beatriz F
Published: June 2024

Chronic heart failure (CHF) is a significant public health concern due to its increasing prevalence, high number of hospital admissions, and associated mortality. Its prevalence is progressively increasing due to the aging of the population and the decrease in mortality from acute myocardial infarction, among other medical advancements. Consequently, the incidence of CHF predominantly affects older age groups, doubling its prevalence every decade, becoming one of the main causes of mortality in patients older than 65 years. The main objective of this study is to apply machine learning based techniques to determine the best models to classify patients with chronic heart failure through their respiratory pattern. These patterns have been characterized from time series such as inspiratory and expiratory times, breathing duration, and tidal volume obtained from the respiratory flow signal. Based on the behavior of the respiratory pattern, CHF patients were classified into patients with non-periodic breathing, with periodic breathing, and with Cheyene-Stokes respiration (CSR). Time-frequency and statistical techniques have been implemented to analyze these features, and then various classification methods have been applied to define the optimal model with the best accuracy rates. These models could help to better understand the evolution of this disease and in early diagnosis.
Multi-agent cloud based license plate recognition system
Authors: Ben Laoula El Mehdi , Elfahim Omar , El Midaoui Marouane , Youssfi Mohamed , Bouattane Omar
Published: June 2024

This paper presents a multi-agent license plate recognition system, specifically designed to address the diverse and challenging nature of license plates. Utilizing a multi-agent architecture with agents operating in individual Docker containers and orchestrated by Kubernetes, the system demonstrates remarkable adaptability and scalability. It leverages advanced neural networks, trained on a comprehensive dataset, to accurately identify various license plate types under dynamic conditions. The system’s efficacy is showcased through its three-layered approach, encompassing data collection, processing, and result compilation, significantly outperforming traditional license plate recognition (LPR) systems. This innovation not only marks a technological leap in license plate recognition but also offers strategic solutions for enhancing traffic management and smart city infrastructure globally.
Machine learning-based clothing recommendation system for women: case study of Lady's confecciones
Authors: Maestre-Matos Leydis , Manjarres-Rivera Manuel , Robles-Algarín Carlos , Navarro-Meneses Jose
Published: June 2024

This paper presents a clothing recommendation system for women based on their body type, aiming to facilitate the purchasing process on the online sales channel of the company Lady's Confecciones located in the city of Santa Marta, Colombia. For this process, a user interface was designed to function in two ways: using a prediction model that takes as inputs a photograph of the user and their height, and a manual mode that receives the measurements of bust, hip and waist. The prediction model implemented the OpenCV library and the skinned multi-person linear (SMPL) model to process images and predict body shape and pose. Five body types were considered: triangle, apple, rectangle, hourglass and inverted triangle, differentiated by bust, waist and hip measurements, according to the conditions provided by the company. The system was able to predict the body measurements of the female participants with a maximum Pearson correlation coefficient of 0.97. For predicting body type, the best results were obtained for the rectangle body shape, with an accuracy of 92.31%.
Student performance classification: a comparison of feature selection methods based on online learning activities
Authors: Alias Muhamad Aqif Hadi , Abdul Aziz Mohd Azri , Hambali Najidah , Taib Mohd Nasir
Published: June 2024

The classification of student performance involves categorizing students' performance using input data such as demographic information and examination results. However, our study introduces a novel approach by emphasizing students' online learning activities as a rich data source. To avoid misinterpretation during the classification, we therefore presented a study comparing several feature selection (FS) methods combined with artificial neural network (ANN), for classifying students’ performance based on their online learning activities. At first, we focused on tackling the issue of missing values by implementing data cleaning using variance threshold. Feature selection techniques were implemented which encompass both filter-based (information gain, chi-square, Pearson correlation) and wrapper-based, sequential selection (forward and backward) techniques. In the classification stage, multi-layer perceptron (MLP) was used with the default hyperparameters and 5-fold cross-validation along with synthetic minority oversampling technique (SMOTE) were also applied to each method. We evaluated each feature selection method's performance using key metrics: accuracy, precision, recall, and F1-score. The outcomes highlighted information gain and sequential selection (forward and backward) as the top-performing methods, all achieving 100% accuracy. This research underscores the potential of leveraging online learning activities for robust student performance classification within the specified constraints.
Big data anonymization using Spark for enhanced privacy protection
Authors: Graba Abdelmadjid Guessoum , Toumouh Adil
Published: June 2024

This article introduces an advanced solution for anonymizing large-scale sensitive data, addressing the limitations of traditional approaches when applied to vast datasets. By leveraging the Spark distributed computing framework, we propose a method that parallelizes the data anonymization process, enhancing efficiency and scalability. Utilizing Spark's resilient distributed datasets (RDD), the approach integrates two primary operations, Map_RDD and ReduceByKey_RDD, to execute the anonymization tasks. Our comprehensive experimental evaluation demonstrates our solution's effectiveness and improved performance in preserving data privacy while balancing data utility and confidentiality. A significant contribution of our study is the development of a wide array of solutions for data owners, particularly notable for a 500 MB dataset at an anonymity level of K=100, where our methodology produces 832 unique solutions. This study also opens avenues for future research in applying different privacy models within the Spark ecosystem, such as l-diversity and t-closeness.
A bibliometric analysis of the landscape of measuring technology maturity in the enterprise internet of things
Authors: Solis Pino Andrés Felipe , Ruiz Pablo H. , Mon Alicia , Collazos Cesar Alberto
Published: June 2024

The internet of things (IoT) is an emerging technology that has taken great relevance in the current socioeconomic context, especially in the business environment, due to its ability to generate competitive advantages. Its adoption presents challenges, such as understanding the value proposition, staff training, and ensuring connectivity and compatibility. In addition, it is crucial to establish the technological maturity of the IoT in enterprises to determine their current state and take steps to address these challenges. In this study, a bibliometric analysis of 431 articles from different scientific databases was performed using Bibliometrix and VOSviewer tools to determine the current state of the domain. The results indicate that the field is booming, with an annual growth rate of 22.58%. Its conceptual structure is composed of the IoT implemented in different contexts, in conjunction with the influence of sister technologies such as big data and blockchain, suggesting limited specificity in establishing the maturity of the enterprise IoT. Countries such as China and Brazil were found to be at the forefront in the area. A promising aspect is establishing standardized ways to measure technological maturity and provide guidelines for improving internet of things adoption.
Enhancing 5G network performance through effective resource management with network slicing
Authors: Suganthi Nagarajan , Narasimhan Ganesh Enthrakandi , Guruva Reddy Elangovan , Balakumar Vijayaraman , Ilakkiya Thangam , Varadarajan Mageshkumar Naarayanasamy , Ramesh Babu Venkatachalam
Published: June 2024

The immense growth of mobile networks leads to versatile applications and new demands. The improved concert, transferability, flexibility, and performance of innovative network services are applied in diversified fields. More unique networking concepts are incorporated into state-of-the-art mobile technologies to expand these dynamic features further. This paper presents a novel system architecture of slicing and pairing networks with intra-layer and inter-layer functionalities in 5th generation (5G) mobile networks. The radio access network layer slices and the core network layer slices are paired up using the network slicing pairing functionalities. The physical network elements of such network slices will be logically assigned entities called softwarization of the network. Such a novel system architecture called network sliced softwarization of 5G mobile networks (NSS-5G) has shown better performances in terms of end-to-end delay, total throughput, and resource utilization when compared to traditional mobile networks. Thus, effective resource management is achieved using NSS-5G. This study will pave the way for future softwarization of heterogeneous mobile applications.
Design a smart platform translating Arabic sign language to English language
Authors: Alamri Maha , Lajmi Sonia
Published: June 2024

Sign language is the only means of communication for deaf and hearing-disabled people in their communities. It uses body language and gestures, such as hand shapes and facial expressions, to convey a message. It is important to note that sign language is specific to the region; that is, Arabic sign language (ArSL) is different from English sign language. Therefore, this research proposes a way to improve the translation of ArSL using a new artificial intelligence (AI) architecture. Specifically, a convolutional neural network (CNN) based on fine-tuning of the SSD-ResNet50 V1 FPN is applied to build a real-time ArSL recognition and translation system with fast and accurate results. The proposed AI architecture can provide translation of sign language in real-time to enhance communication in the deaf community. We achieved an average F-score of 86% and an average accuracy of 94%.
Lightweight log-monitoring-based mitigation tool against WLAN attacks
Authors: Saifan Ramzi , Radi Mohammad , Al-Dabbagh Hamsa , Mansour Badr
Published: June 2024

Wireless network attacks are some of the most common network security threats dealt with daily. Their ease of execution and effectiveness make them commonplace within most public networks. The goal of this paper is to develop a tool which provides defenses against these attacks, one which can also generate the attacks to test its own effectiveness in defending against them. The research involved the design, testing, and implementation of attacks/defenses tool, which benefits from a user-friendly user interface that simplifies the testing process. The attacks were generated using existing tools, linked to one central interface. The defense methodology was script-based and created entirely from scratch. It was also linked to a single interface which continuously monitors logs to detect and prevent attacks in an efficient timely manner. The results showed that the proposed defenses to the studied wireless attacks were effective at mitigation, or outright prevention. They were also more lightweight than existing solutions, making them more appealing for less powerful hardware.
R-based neural networks decision model for water air cooler system
Authors: Mohammed Haidas , Soumia Kerrache
Published: June 2024

Water air cooler is a process that uses an evaporative system. In its first generation, there was no temperature sensor, and its fan was turned by an electric motor in an open loop system with a fixed speed. As well, the water’s flow and level in the tank are governed by a mechanical system, which is generally a floating ball attached to a shaft. In order to ameliorate this classical system with more advantages and performances, many ideas are included in subsequent generations such the integration of embedded systems, intelligent control and components of manufacturing materials. In this paper, the current study aims to integrate an intelligent system, which is the neural networks by using R language to give a smart decision model to command relays switching dedicated to control the electric motors, where the first one is tied up with a fan and the other to an electro-pump. The HC-SR04 ultrasonic and DHT11 sensors supervise the two desired parameters control, water level in the tank and the outside temperature successively.
DNA computing and meta-heuristic-based algorithm for big data task scheduling in cloud computing
Authors: Gandhimathinathan Visalaxi , Alagesan Muthukumaravel
Published: June 2024

With the advent of cloud computing, there is a need to enhance both the methods and algorithms of big data workloads for task scheduling. Due to the global spread of services with changing task load circumstances and different cloud client demands, big data task scheduling in cloud systems is a time-consuming process. The proposed approach emphasises the necessity for efficient big data task scheduling in the cloud computing, which exacerbate data processing. Virtual machines frequently utilise all three types of physical resources: CPU, memory, and storage. Big data task scheduling is one of the most important implications of cloud computing application resource management, and this research work meticulously offers a task scheduling technique for advancing cloud computing.
Adjusted TextRank for keyword extraction in petrochemical project correspondence documents
Authors: Atmoko Indri , Yulianti Evi , Jiwanggi Meganingrum Arista
Published: June 2024

A large petrochemical construction project is typically executed by multiple parties, all bound by contract agreement. During the execution phase, issues and problems may arise because the work details are not clearly specified in the contractual agreement. These issues are formally communicated and documented through written correspondence letters. By identifying important keywords within these formal letters, a comprehensive narrative of the project, including its associated issues, can be identified and analyzed. In this research, we introduce an adjusted TextRank algorithm that integrates external features from the Indonesian FastText language model and term frequency-inverse document frequency (TF-IDF) scores to identify important keywords within a dataset of correspondence letters of petrochemical projects. This enhancement involves refining phrase detection, semantic relationship estimation between words, and part-of-speech (POS) identification for words or phrases. Our results show that the proposed adjustments result in improved evaluation scores compared to the baseline standard TextRank and standard TF-IDF, respectively by 24.1% and 25% in terms of F-1 scores.
Development and modification Sobel edge detection in tuberculosis X-ray images
Authors: Devita Retno , Fitri Iskandar , Yuhandri Yuhandri , Yani Finny Fitry
Published: June 2024

Tuberculosis (TB), a major global health threat caused by mycobacterium tuberculosis, claims lives across all age groups, underscoring the urgent need for accurate diagnostic methods. Traditional TB diagnosis using X-ray images faces challenges in detection accuracy, highlighting a critical problem in medical imaging. Addressing this, our study investigates the use of image processing techniques-specifically, a dataset of 112 TB X-ray images-employing pre-processing, segmentation, edge detection, and feature extraction methods. Central to our method is the adoption of a modified Sobel edge detection technique, named modification and extended magnitude gradient (MEMG), designed to enhance TB identification from X-ray images. The effectiveness of MEMG is rigorously evaluated against the gray-level co-occurrence matrix (GLCM) parameters, contrast, and correlation, where it demonstrably surpasses the standard Sobel detection, amplifying the contrast value by over 50% and achieving a correlation value nearing 1. Consequently, the MEMG method significantly improves the clarity and detail of TB-related anomalies in X-ray images, facilitating more precise TB detection. This study concludes that leveraging the MEMG technique in TB diagnosis presents a substantial advancement over conventional methods, promising a more reliable tool for combating this global health menace.
Tailoring therapies: a frontier approach to pancreatic cancer with AI-driven multiomics profiling
Authors: Jawahar Janiel , Rajendran Paramasivan Selvi
Published: June 2024

Pancreatic cancer is often diagnosed at an advanced stage when treatment options are limited. Being one of the deadliest cancers that mandates longer medication and treatment phases, there is an inevitable need to have the knowledge of drug response of anti-pancreatic cancer drugs before it is recommended for a patient. AI-driven drug response prediction has proven potential to personalize treatment strategies, improve therapeutic outcomes, and reduce adverse effects and treatment costs for cancer patients. In this research work, we have accounted for the use of different drug descriptors and their core structures known as scaffolds along with three cell line features, chromatin profiling, reverse phase protein array, and metabolomics data to build a feature engineered dataset for drug response prediction tested on various computational learning models. The 53 unique drugs against 18 unique pancreatic cancer cell lines were taken as the raw dataset. The initial dataset having a large dimension was feature selected using an ensemble method derived from five different techniques. The dataset was evaluated on various computational methods and an accuracy of 89% was achieved using the TabNet architecture. Furthermore, the common scaffolds that were persistently found among the drugs that possess high IC50-valued drug clusters were also recorded.
Design of a chatbot in a mobile application for managing payments and controlling activities in a fast school organization
Authors: Medina Gustavo Teves , Cano Lengua Miguel Angel , Medrano Hugo Villaverde
Published: June 2024

The fast school (FS) educational organization, like many contemporary educational institutions, faces challenges in efficient payment management and rigorous control of activities. Technology, particularly through mobile applications, has shown to be a potential solution to these problems, allowing institutions to stay at the forefront and provide optimized services to their educational community. Therefore, this research work focuses on how a chatbot, integrated into a mobile application, can improve payment management and control of activities in the FS educational organization. Through a detailed study on current trends in educational technology, the design and development of a chatbot adapted to the specific needs of the organization is presented. This chatbot not only facilitates payment processes, offering immediate responses and managing transactions, but also allows for more efficient control of academic and extracurricular activities, improving the experience of its users. In conclusion, the integration of chatbots in mobile applications is presented as a viable and promising solution to face and overcome management challenges in modern educational environments, providing adaptive and user-centered tools that enhance the operational efficiency of institutions. This work is developed with the Scrum methodology and presents a security gateway validated by a digital token.
Advancing cryptography: a novel hybrid cipher design merging Feistel and SPN structures
Authors: Venkataramanna Ramya Kothur , Hosur Sriram Manjunatha Reddy , Reddy Bharathi Chowda
Published: June 2024

In the dynamic field of cryptography, lightweight ciphers play a pivotal role in overcoming resource constraints in modern applications. This paper introduces a lightweight cryptographic algorithm by seamlessly merging the proven characteristics of the Feistel cipher CLEFIA with the advanced substitution-permutation network (SPN) framework of RECTANGLE for key generation. The algorithm incorporates a specially optimized feather S-box, balancing efficiency and security in both CLEFIA and RECTANGLE components. The RECTANGLE key generation, vital for the proposed lightweight technique, enhances overall cryptographic security and efficiency. Meticulous consideration of resource limitations maintains the algorithm's lightweight nature, making it well-suited for applications with restricted computational resources. To validate the efficacy of the lightweight algorithm, extensive evaluation on encrypted data is conducted using National Institute of Standards and Technology (NIST) tools, known for assessing cryptographic algorithm quality. Results reveal a high degree of randomness, indicative of robust resistance against cryptographic attacks. This manuscript provides a comprehensive examination of the lightweight algorithm, emphasizing key attributes, security enhancements, and successful integration of the optimized feather S-box. Rigorous testing, particularly NIST tool-based randomness analysis, offers empirical evidence of the algorithm's resilience against attacks, establishing its suitability for secure data encryption in resource-limited environments.
 
Touch-free tissue dispensing device
Authors: Mohd Zaki Nurul Shahira , Nik Dzulkefli Nik Nur Shaadah , Abdullah Rina , Ismail Syila Izawana , Omar Suziana
Published: June 2024

In this paper, an innovative solution for the everyday issue of tissue dispensing is presented. With billions of tissues distributed daily, the current dispensers often face challenges, as revealed in studies of frequently damaged units. The primary objective was to enhance this fundamental item, aiming to simplify users’ lives. The key innovation lies in granting users control over the tissue dispenser’s rolling mechanism. Introducing the Arduino UNO microcontroller-powered smart tissue dispenser. Operated by a stepper motor, the dispenser reacts to the user’s needs. Activation occurs when the infrared sensor detects hands, prompting the motor to release the appropriate amount of tissue. It’s like witnessing magic, yet it’s simply the ingenuity of technology at play. The software for the Arduino UNO, serving as the project’s controller, is compiled, and uploaded using the Arduino IDE. The performance of this automatic tissue dispenser indicates success in addressing common issues and facilitating effortless tissue retrieval.
Emotion recognition from Burmese speech based on fused features and deep learning method
Authors: Mar Lwin Lwin , Pa Win Pa
Published: June 2024

Burmese language is challenging for speech emotion classification. Moreover, it is lack of resource and few research was made in this topic. To solve the challenging problem, novel feature extraction for Burmese language is proposed. For lack of resource, Burmese speech emotion corpus called BMISEC is built. To support the challenging problem, the advantages of feature extractions are fused to create a robust feature. Four features are fused. Novel text-tone feature, local binary pattern, mel-frequency cepstral coefficient and discrete wavelet transform are fused. To progress the performance, deep learning method called DenseNet-Emotion is used for classification. Support vector machine is used in DenseNet’s classifier layer. To show the robustness of the proposed system, three types of experiments are made on Tensorflow framework. They are ablation study, experiments with three publicly available datasets and experiments with the previous research methods and they are compared with the proposed method. It is found that feature fusion is superior to only one feature in emotion recognition. BMISEC gets better performance than other datasets. Moreover, the proposed method gets the superior result than previous research methods. The proposed method gets the accuracy of 88.388% for 50 epochs.
A proposed semantic keywords search engine for Indonesian Qur’an translation based on word embedding
Authors: Trisnawati Liza , Binti Samsudin Noor Azah , Bin Ahmad Khalid Shamsul Kamal , Bin Ahmad Shaubari Ezak Fadzrin , Sukri Sukri , Indra Zul
Published: June 2024

Obtaining relevant information from the Holy Qur’an can be really challenging for people who cannot speak Arabic, such as the Indonesian people. One technology implementation which is commonly used to tackle this problem is to develop a search engine application for Al-Qur’an verses. This paper proposes a search engine based on semantic representation keywords for the Indonesian translation of the Al-Qur’an which consists of 3 phases i.e., data preparation, document representation, and search engine development. In the first stage, the Al-Qur’an dataset was built using the official translation of the Al-Qur’an from the Ministry of Religion and then enriched with the Wikipedia corpus. The second phase is document representation which produces feature vectors by utilizing the Word2Vec algorithm. Finally, the development of a search engine that can find the most relevant verses by calculating the cosine similarity between the document and the keywords. It was found that the proposed search engine succeeded in exceeding the performance of ordinary search engines by finding wider information due to the use of semantic keywords. Apart from that, the proposed search engine succeeded in maintaining the relevance of search results by achieving precision and recall levels of 98.7% and 97.3% respectively.
What is the importance of tenascins in the pathophysiology of various diseases?
Authors: Santos Larissa Cristina França , Santos Marjory Pacheco dos , Nascimento Cristian Rodrigues do , Cavalcante Raphael Lopes , Mendes Rodrigo , Tenório Pedro Pereira
Published: June 2024

Tenascins are a group of proteins that make up the extracellular matrix of different tissues. They are involved with signaling and structural functions in different organs, which justifies the development of studies on their relationship with diseases in different systems. Our objective was to shed light, through an in-depth review, of the way in which tenascins are involved in triggering various organic disorders. Each type of Tenascin is related to specific disorders. Tenascin X is involved in triggering collagen diseases, while types C and W stand out in situations of trauma, inflammation and tumors. Tenascin R is involved exclusively in the central nervous system and in neuronal migration and differentiation processes. Understanding the real role of these proteins is a valuable resource for understanding the pathophysiology of various diseases, in addition to providing research into new diagnostic methods and prospecting therapeutic targets for drug development.
ELA em associação ou componente do quadro clínico da síndrome de sjögren: um relato de caso
Authors: Bento Vanessa Furtado do Vale , Vieira Fernanda Aparecida Mateus , Silva Maria Fernanda Reis Nunes da , Trevisani Virginia Fernandes Moça
Published: June 2024

Introdução: A Síndrome de Sjögren (SS) é uma doença sistêmica inflamatória crônica, imunomediada, caracterizada pelo envolvimento das glândulas exócrinas, principalmente salivares e lacrimais. Entretanto, sua heterogeneidade permite também o acometimento extraglandular, podendo ocorrer alterações de origem neurológica, como o acometimento do neurônio motor, que mimetiza os sintomas da Esclerose Lateral Amiotrófica (ELA) dificultando o diagnóstico. Objetivos: Descrever e discutir um caso de uma paciente diagnosticada com SS com um comprometimento neurológico sugerindo-se um comprometimento sistêmico com possível associação a Esclerose Lateral Aminiotrófica. Metodologia: Estudo retrospectivo de prontuários e exames, após a aprovação do Comitê de Ética em Pesquisa. Foi feita uma busca sistematizada na literatura nas principais bases de dados PUBMED/ MEDLINE, LILACS e Cochrane Central utilizando a estratégia de busca “(Sjogren's syndrome) AND (motor neuron disease)” e “(amyotrophic lateral sclerosis) AND (Sjogren syndrome)” com filtro “full text”. Relato de Caso e Discussão: Paciente relatou perda da força na mão direita há um ano, xeroftalmia e xerostomia, e na investigação apresentou Anti-SSB/La e FAN positivos com biópsia glandular positiva para SS. Exame de condução nervosa inicial mostrou uma amplitude do potencial de ação diminuída e ondas F aumentadas no nervo ulnar direito. Evoluiu com comprometimento de membros inferiores, sem queixas sensitivas, apenas perda progressiva da força. Na literatura consultada, encontrou-se que apenas 11 artigos apresentaram perfil clínico compatível ao comprometimento motor isolado na SS e apenas outros 2 casos mostravam a possibilidade de associação da ELA com a síndrome, descrevendo quadros muito compatíveis, mas com desfecho desfavorável. Conclusão: A paciente apresenta um quadro raro, com poucos relatos na literatura, que dado a progressão sintomática, cogitou-se a ELA como componente do quadro clínico ou patologia associada. A escassez de informações gera dúvidas acerca do diagnóstico e tratamento mais adequado. Paciente segue irresponsiva aos tratamentos utilizados e o quadro vem evoluindo progressivamente.
Espelho de duas faces: a literatura indígena de Olívio Jekupé na produção de alteridades sobre o não-índio.
Authors: Rosa Francis Mary Soares Correia da
Published: June 2024

Este artigo objetiva analisar a obra Tekoa: conhecendo uma aldeia indígena do autor indígena Olívio Jekupé por meio do conceito de alteridade comparativista (Forget e Oliveira, 2001) e do conceito de livro-rizoma (Deleuze e Guattari, 2000). Procuramos desenvolver uma análise da caracterização da figura do não-índio presente em sua literatura, assim como a construção de um referencial dialógico para se compreender a produção de uma alteridade produzida pelos povos indígenas.
Música, participação e envolvimento de crianças com Trissomia 21 em atividades diárias
Authors: Oliveira Sarah Gregório Falcão de , Galvão Cláudia Regina Cabral
Published: June 2024

Children with Trisomy 21 generally have delayed psychomotor development and intellectual disabilities that can compromise the acquisition of motor and communication skills. The study is based on the hypothesis that music can be a beneficial auxiliary resource in Occupational Therapy sessions, due to its influence on the human body, and can be useful to promote participation. The objective of the research was to observe the effect of music on maintaining the child's attention, participation and involvement in different tasks in Occupational Therapy sessions, defined based on demands defined by the Canadian Occupational Performance Measure (COPM). This is a single case study with a 4-year-old child, diagnosed with Trisomy 21. After 12 sessions there was an improvement in the child's performance in the demands brought by the mother, as well as an improvement in their satisfaction in the activities carried out. The comparative analysis shows an increase of 5.6 points in the child's performance and an increase of 6.3 points in the mother's satisfaction in the reevaluation. Music in this study provided a positive result in the proposed activities, helping the child to maintain attention, increasing participation and independence in brushing teeth, getting dressed and drinking water from the glass.
Estação Marítima da Gamboa: entre a expansão ferroviária e os melhoramentos portuários no Rio de Janeiro Imperial (1876-1883)
Authors: Mantuano Thiago
Published: June 2024

No momento em que as ferrovias eram expandidas, a navegação a vapor generalizada e o funcionamento dos portos eram questionados em todo o país, dotar o Rio de Janeiro de uma infraestrutura capaz de integrar esses modais se tornou aspiração das classes dominantes. Apenas nesse contexto específico, a ideia de uma Estação Marítima na Corte do Império pôde se concretizar. Não havia precedentes para os esforços e recursos empenhados, tanto do governo imperial quanto da maior empresa brasileira – a Estrada de Ferro Dom Pedro II. Objetivo neste artigo demonstrar a elaboração da Estação Marítima da Gamboa enquanto solução unificada para contemplar mudanças complexas e dirimir problemas de origens distintas, suscitando uma ação avassaladora para a preparação e a execução de sua construção (1876-1883). A investigação foi realizada em fontes governamentais, da empresa e em publicações não relacionadas aos promotores da obra.
Una campaña “para los trabajadores, las mujeres y la juventud”: el Partido Comunista frente las elecciones de 1983 en Argentina
Authors: Bona Victoria
Published: June 2024

El 30 de octubre de 1983, la sociedad argentina acudió a las urnas de modo masivo. La campaña electoral se había presentado como un momento de ruptura con la dictadura instalada en 1976. El Partido Comunista de la Argentina (PCA), como los demás del arco político, presentó sus propias candidaturas constituyéndose como una alternativa para las categorías legislativas y para los ejecutivos provinciales, municipales y comunales. Dentro del abanico de las izquierdas, fue el más votado, aunque los resultados fueron insignificantes y sólo alcanzó concejalías en algunas localidades pequeñas. El artículo indaga en el proceso de búsqueda de la legalidad y la subsecuente campaña de afiliación que llevó adelante el PCA y reconstruye la promoción de una alternativa electoral que buscó interpelar a las y los trabajadores, la juventud y las mujeres de modo particular.
Saúde Mental na Atenção Básica: intersetorialidade no cuidado em saúde mental no Estado de São Paulo
Authors: Pupo Lígia Rivero , Rosa Tereza Etsuko da Costa , Morais Maria de Lima Salum e
Published: June 2024

This article aimed to analyze and discuss intersectoral articulations of primary health care services in the state of São Paulo, Brazil, in order to respond to needs linked to mental health promotion and to mental suffering care. With this purpose, a quantitative and descriptive cross-sectional study was carried out through a telephone survey in Primary Health Care (PC) services in the State. The data were analyzed according to the size of the municipality, types of primary care units, presence of a mental health professional in the unit and participation in Psychosocial Care Network meetings in their region. The results indicate that only 63.2% of basic care units have had some intersectoral coordination for the management of mental health situations. The most frequently mentioned articulation between units were with social assistance network equipments, followed by that with educational institutions. However, the nature of this articulation showed some fragility, insofar as it was more focused on referrals and guidance than on regular joint meetings. It is proposed the need for increase of more articulations with diversified equipment focused on psychosocial care, such as with sports units, culture and income generation groups. There is also a need for greater intentionality of management in the implementation of dialogical spaces between different sectors in order to achieve a more powerful and effective intersectoral articulation.
Key words: Mental health. Primary care. Health services. Intersectoral collaboration. Health survey.
Antihypertensive Medication Use Trajectories After Bariatric Surgery: A Matched Cohort Study
Authors: Passman Jesse E. , Wall-Wieler Elizabeth , Liu Yuki , Zheng Feibi , Cohen Jordana B.
Published: June 2024

BACKGROUND:

Metabolic and bariatric surgery (MBS) is the most effective and durable treatment for obesity. We aimed to compare the trajectories of antihypertensive medication (AHM) use among obese individuals treated and not treated with MBS.

METHODS:

Adults with a body mass index of ≥35 kg/m
2
were identified in the Merative Database (US employer-based claims database). Individuals treated with versus without MBS were matched 1:1 using baseline demographic and clinical characteristics as well as AHM utilization. Monthly AHM use was examined in the 3 years after the index date using generalized estimating equations. Subanalyses investigated rates of AHM discontinuation, AHM initiation, and apparent treatment-resistant hypertension.

RESULTS:

The primary cohort included 43 206 adults who underwent MBS matched with 43 206 who did not. Compared with no MBS, those treated with MBS had sustained, markedly lower rates of AHM use (31% versus 15% at 12 months; 32% versus 17% at 36 months). Among patients on AHM at baseline, 42% of patients treated with MBS versus 7% treated medically discontinued AHM use (
P
<0.01). The risk of apparent treatment-resistant hypertension was 3.41× higher (95% CI, 2.91–4.01;
P
<0.01) 2 years after the index date in patients who did not undergo MBS. Among those without hypertension treated with MBS versus no MBS, 7% versus 21% required AHM at 2 years.

CONCLUSIONS:

MBS is associated with lower rates of AHM use, higher rates of AHM discontinuation, and lower rates of AHM initiation among patients not taking AHM. These findings suggest that MBS is both an effective treatment and a preventative measure for hypertension.