bluuebunny/crossref_metadata_2025 huggingface dataset:
A Clinical Outcomes Data Archive for a Comprehensive Fetal Diagnosis and Treatment Center
Authors: Reynolds Thomas A. , Goldshore Matthew A. , Flohr Sabrina , Land Sierra , Mathew Leny , Gebb Juliana S. , Oliver Edward R. , Rintoul Natalie E. , Ades Anne M. , Foglia Elizabeth E. , Avitabile Catherine M. , Panitch Howard B. , Heuer Gregory G. , Howell Lori J. , Adzick N. Scott , Hedrick Holly L.
Published: October 2024

Introduction: Data on near- and long-term clinical outcomes are critical for the care of all maternal-fetal patients presenting to a fetal center. This is especially important since physiologic and neurodevelopmental attributes do not manifest until later childhood when multilevel (e.g., individual, family, policy) factors have a direct influence on health outcomes. Electronic health records (EHRs) create opportunity for efficient data collection. However, documentation structures are not designed for acquisition of key attributes, and changes over time and between-clinician differences can affect resultant output. Therefore, EHR derived datasets have limited ability to accurately characterize the clinical presentation and care trajectory of patients with congenital anomalies. In addition, in most systems, the fetus lacks a digital identity and requires relinking fetal attributes documented in the maternal chart to those from the pediatric EHR. This conundrum amplifies in the setting of multiple gestation, returning maternal patients, and pregnancies with fetal demise. Moreover, current data capture systems result in incomplete abstraction of variables that may confound, mediate, or moderate critical associations. Our objective was to develop and implement a prospective data capture platform to transform EHR data into an analytic-grade database for multipurpose use. Methods: A unified platform for longitudinal follow-up of maternal-child dyads cared for at our fetal center, named the Clinical Outcomes Data Archive (CODA), was constructed. CODA was designed using a data dictionary based on multidisciplinary and interprofessional expert input, a relational identity for each patient, fetus, and pregnancy, and a process by which EHR-sourced and chart-abstracted data are validated by a well-trained team. Descriptive analyses were performed for data acquired between July 2022 and July 2023, and a comparison of studies before and after implementation of CODA is presented. Conclusion: 5,394,106 data points were validated for 7,662 patients across 12 conditions. 2% of data points were found to be unreliable or undocumented. 91% of data points were sourced from the EHR. Eighty-five percent of condition-specific variables required manual chart abstraction. The study conducted with CODA was able to contribute to 18 other studies. CODA successfully merges EHR-sourced and manually abstracted documentation for longitudinal study of the maternal-child dyad.
Application of classification algorithms for smishing detection on mobile devices: literature review
Authors: Calero Sinche Dylan Faredh , Acuña Meléndez María , Ovalle Christian
Published: October 2024

Smishing is a form of phishing carried out via mobile devices to steal confidential information from victims. The number of smishing attacks has increased in recent years due to the large number of users acquiring these easy-to-use and functional devices. This literature review objective is to examine the techniques and methods used in smishing attacks using classification algorithms. To do so, we conducted a manual search process and selected 155 articles from Scopus and 29 articles from access to research for development and innovation (ARDI). Of these, 36 articles met the inclusion criteria. In addition, the algorithms most commonly used by the studies were random forest classification techniques, decision trees, and neural networks. These studies analyzed various machine learning models for detecting phishing and smishing messages. The attack simulation scenarios included generating web pages, sending fake links (URLs), and installing malicious applications. The analysis evaluated web pages and SMS messages using a database containing legitimate as well as smishing messages. Based on the results, it is suggested to combine these methods to improve detection performance, making it more robust and promising.
Multi platforms fake accounts detection based on federated learning
Authors: Azer Marina , H. Zayed Hala , A. Gadallah Mahmoud E. , Taha Mohamed
Published: October 2024

Identifying and mitigating fake profiles is an urgent issue during the age of widespread integration with social media platforms. this study addresses the challenge of fake profile detection on major social platforms-Facebook, Instagram, and X (Twitter). Employing a two-sided approach, it compares stacking model of machine learning algorithms with the federated learning. The research extends to four datasets, two Instagram datasets, one X dataset, and one Facebook dataset, reporting impressive accuracy metrics. Federated learning stands out for it is effectiveness in fake profile detection, prioritizing user data privacy. Results reveal Instagram fake/real dataset achieves 96% accuracy while Instagram human/bot dataset reaches 95% accuracy with federated learning. using the stacking model X’s fake/real dataset achieves 99.4% accuracy, and Facebook fake/real dataset reaches 99.8% accuracy using the same model. The study underscores the pivotal role of data privacy, positioning federated learning as an ethical choice. It compares the time efficiency of stacking and federated learning, with the former providing good performance in less time and the latter emphasizing data privacy but consuming more time. Results are benchmarked against related works, showcasing superior performance. The study contributes significantly to fake profile detection, offering adaptable solutions and insights.
A multi-core makespan model for parallel scientific workflow execution in cloud computational framework
Authors: Naaz Farha , Banu Sameena
Published: October 2024

Researchers have shown a lot of potential in optimizing cloud-based workload-scheduling over the past few years. However, executing scientific workloads inside the cloud is time-consuming and costly, making it inefficient from both a financial and productivity standpoint. As a result, there are many investigations conducted, with the general trend being to speed up the rate of processing and establish a cost-effective system, whereby customers are billed according to their actual use. In addition, energy-consumption is capable of being reduced, especially if the available resources are heterogeneous; however, few investigations have optimized multi-core with analyzing makespan parameters collectively to fulfill the quality of service (QoS) and service level agreement (SLA) of the workload task. In this research, we introduce an optimal scheduling for a heterogeneous distributed cloud computing environment called task aware makespan optimized scheduler (TAMOS) that guarantees requirements across the task levels of scientific workflows. The energy and time required to carry out specific workflows are significantly reduced by using this TAMOS strategy. The TAMOS framework was studied using the scientific workflows namely, inspiral and sipht. When compared to the conventional method of scheduling work, our methodology used less energy and makespan.
Deep learning approach for forensic facial reconstruction depends on unidentified skull
Authors: M. Mohammed Doaa , Elgendy Mostafa , Taha Mohamed
Published: October 2024

Facial reconstruction, or facial approximation, is an essential problem in a criminal investigation involving reconstructing a victim's face from his skull to determine the victim's identification at a crime scene. Facial approximation plays a crucial part when there is a lack of clues with investigators. Investigators utilize facial approximation to guess the victims' identities. This research attempted to use computer-aided face reconstruction rather than traditional approaches. Traditional methods of face reconstruction include the use of clay or gypsum. Traditional procedures necessitate forensic professionals to rebuild the victim's face. This research uses the convolution neural network skull part with sift (CNNSPS) model is employed to reconstruct facial features from a skull image utilizing public datasets CelebAMask-HQ and MUG500+. The proposed algorithm was tested on unidentified skull databases, and celebrity faces were used. The genuine datasets are not available, which is the key issue in this research.
Using pattern mining to determine fine climatic parameters for maize yield in Benin
Authors: Gloria Tahi Souand Peace , Ratheil Houndji Vinasetan , Gbêmêmali Hounmenou Castro , Glèlè Kakaï Romain
Published: October 2024

This study investigates the relationships between Benin's climate and maize production to develop an association rule algorithm for accurate yield prediction. The datasets utilized extend 26 years (1995 to 2020) and include climate and maize yield data from five districts with synoptic weather stations in two agroclimatic zones (Sudanian and Sudano-Guinean). Climate variables were combined with yield using "year" and "districts" to find the association rules. Several techniques were used to determine the correlation between weather parameters and maize yields: support vector machines, K nearest neighbor, artificial neural networks, decision trees, and recurrent neural networks. The most performed method was the decision tree (R2=0.998, mean squared error (MSE)=0.021, and mean absolute error (MAE)=0.0008). This model is difficult to understand, though the frequent pattern growth technique was then applied to the dataset to facilitate the discovery of the rules. The Sudano-Guinean zone exhibits high maize yields for medium minimum and maximum temperature values, rainfall, evapotranspiration, and humidity. In the Sudanian zone, medium minimum and maximum temperatures and maximum humidity levels are associated with high maize yields. The discovered association rules showed that optimizing maize output might be done dependably and effectively.
Enhancing legal research through knowledge-infused information retrieval for Vietnamese labor law
Authors: Pham Vuong , Huy Le Hoang , Phu Ngo Thinh , Nguyen Binh , Nguyen Diem , D. Nguyen Hien
Published: October 2024

The role of intelligent information retrieval systems in legal research optimization has become increasingly recognized. There are many methods for exhibiting advance mentsin the proficient retrieval of legal documents. However, those methods fail to tackle the specific challenges encountered in real-world labor law searches. This research breaks new ground in Vietnamese labor law retrieval by leveraging a comprehensive dataset of 300,000 documents a cross diverse cat egories (20 document types and 27 legal fields) to train and evaluate retrieval models specifically designed for Vietnamese labor law. Unlike previous approaches, this work goes beyond simple information retrieval. It also constructs question & answer (Q&A) dataset specifically tailored to this legal domain. Be sides, this study introduces a novel approach of in corporating a legal ontology built from the data set itself. This knowledge in fusion significantly improves retrieval performance across legal search tasks, as demon strated through rigorous experimentation. These advancements empower intelligent systems to grasp the intricate semantic nuances of Vietnamese labor law.
Plant factory with artificial light design, realization, and economic analysis
Authors: Homjan Jeerawan
Published: October 2024

It has been challenging to construct an autonomously controlled plant factory with artificial light (PFAL). It is also useful in engineering and bioscience research and education. The purpose of this research is to design and construct a micro-scale PFAL (µPFAL) with automatic environment control for a university project. Then, analyze the effectiveness of managing temperature, humidity, pH, EC, and CO2 on crop production, as well as the cost, and benefit of µPFAL. The µPFAL is made up of LED lighting, air condition, vertical cultivation, EC-pH regulation, a CO2 supply unit, and environmental control and monitoring. Control was provided via Arduino with PC monitor. For economic evaluation, cost-benefit analysis was used. The results of the control environment in µPFAL were achieved with a deviation of less than 2.5%. An Arduino-based environmental control system with a computer for monitoring was suited for university’s PFAL.Our µPFAL could produce 80.45 g/head fresh weight of green oak lettuce, the lettuce’s yield of 19 kg/m2/y. The payback period of µPFAL is 3.28 years, net present value of 82,543.30 THB, an internal rate of return of 24% and the B/C ratio of 1.22. Future research should include solar energy to assist µPFAL in meeting its sustainable goal.
Optimizing the position of photovoltaic solar tracker panels with artificial intelligence using MATLAB Simulink
Authors: Linelson Ricardo , Rinanda Saputri Fahmy
Published: October 2024

This research aims to apply an artificial intelligence (AI) system to control the position of photovoltaic (PV) panels to maximize the use of solar energy using the solar tracker. The implementation of AI algorithms to achieve optimal panel orientation, considering factors such as sunlight intensity and sun position is also discussed. The simulation results using matrix laboratory (MATLAB) Simulink can be observed on the scope, displaying the position control graph of the solar panel from sunrise to sunset. By employing proportional integral derivative (PID) control, the error is likely to be minimal, ensuring that the panel will continue to follow the sun until it sets at the maximum point of 4:00 PM. After that, the panel can be adjusted back or reset to the initial position at 6:00 AM for the following day. In a full-day simulation, the solar panel will follow the sun's movement from sunset to sunrise. At the basic level, sunrise occurs in the first hour at position 1.0, which is 6:00 AM in the minimum point at the bottom left corner of the curve, and sunset occurs in the afternoon at position 5.25, which is 4:00 PM at the maximum point in the top right corner of the curve.
Internet of Things and Blockchain Integration for Security and Privacy
Authors: Kumar Sumita , Vidhate Amarsinh
Published: October 2024

The Internet of Things (IoT) can be defined as a network of intelligent objects where physical objects are equipped with electronic and network components to enable connectivity. These smart objects are embedded with sensors that enable them to monitor, sense, and gather data pertaining to their surroundings, including the environment and human activities. The applications of IoT, both existing and forthcoming, show great promise in terms of enhancing convenience, efficiency, and automation in our daily lives. However, for the widespread adoption and effective implementation of The Internet of Things, addressing concerns related to security, authentication, privacy, and recovery from potential attacks is crucial. To achieve end-to-end security in Internet of Things environments, it is imperative to make necessary modifications to the architecture of The Internet of Things applications. In this paper, we propose assessment parameters and a layered framework that integrates The Internet of Things and blockchain to enhance
The use of augmented reality in assessing and training children with attention deficit hyperactivity disorder
Authors: Joseph Jesla , M. Vinay
Published: October 2024

Attention deficit hyperactivity disorder (ADHD) is a serious issue that must be addressed in the modern world. Treatment for ADHD is challenging because it is costly, has adverse effects, might not be successful, and is not considered an emergency. The reason that ADHD is hard to manage is because it causes people-especially children-to make impulsive decisions that hinder their ability to succeed in school, the workplace, and other areas of life. As an alternative approach, neurofeedback therapy or play therapy, which relies on real-time feedback of an individual's brainwave activity typically collected through electroencephalogram (EEG), has demonstrated promising outcomes in the treatment of mental disorders and enhancing cognitive capabilities. On the other hand, prolonged exposure to repetitive feedback might result in lower engagement since people may become disinterested in the process and find it difficult to continue participating. An extensive assessment on the use of augmented reality (AR) in the context of pediatric ADHD has been carried out, with an emphasis on the benefits of creating games specifically for kids with ADHD. By using AR technology in a group of children, the goal of this study was to investigate the basic characteristics of AR systems that aid in the identification and treatment of ADHD in children.
ConciseCarNet: convolutional neural network for parking space classification
Authors: Ramli Marwan , Rahman Sayuti , Bayu Syah Rahmad
Published: October 2024

The car is a mode of transportation that brings numerous benefits to the community. As a result, the growth of vehicles is increasing, which has a negative impact. Some of the negative impacts include noise, air pollution, traffic congestion, and the need for parking spaces. Drivers that drive around looking for parking places increase the negative impact as well as boredom and even worry for the driver. Therefore, the driver needs this information on the availability of parking spaces. A convolutional neural network (CNN) using a camera is one of the best methods that can be used to solve this problem. We built a more efficient CNN architecture for classifying parking spaces, which was named ConciseCarNet. ConciseCarNet uses 33 and 11 convolution filters, which cause fewer parameters than previous architectures. ConciseCarNet has two branches, each with a different branch structure. This branch is designed to generate additional feature variations, which will help improve the accuracy. Based on testing, the accuracy of ConciseCarNet2x outperforms the accuracy of mAlexnet, Carnet, EfficientParkingNet, and you look once (YOLO)+MobilNet architectures, which is 99.37%. ConciseCarNet has fewer parameters, file sizes, and floating point operations (FLOPs) compared to other architectures.
A fuzzy logic-genetic algorithm for full truckload transportation problem
Authors: EL Bouyahyiouy Karim , EL Hariz Zahira , Bellabdaoui Adil
Published: October 2024

This work addresses a full truckload commodity selection and multiple depot vehicle routing problem with time windows (FTSMDVRPTW). The goal of the problem is to design a set of selective truck routes that maximize overall profit subject to time window constraints. Each truck route is an arrangement of full truckload transportation commodities that begins at a departure point and ends at an arrival point. It is unnecessary to serve all commodities; only those that provide a higher profit are chosen. We introduce a meta-heuristic based on a combination of fuzzy logic controller (FLC) and genetic algorithm (GA) to solve the FTSMDVRPTW, where the crossover and mutation rates are adjusted during the GA’s evolutionary process using an FLC. We demonstrate the effectiveness and efficiency of the proposed FLC+GA through experimental results on randomly generated instances for the considered problem.
Guided imitation optimizer: a metaheuristic combining guided search and imitation search
Authors: Daru Kusuma Purba , Kallista Meta
Published: October 2024

This paper proposes a novel metaphor-free metaheuristic, namely the guided imitation optimizer (GIO). This metaheuristic combines the guided search and imitation-based search. There are five guided searches and three imitation based searches. Meanwhile, there are three references used in this metaheuristic: global finest, a randomly picked solution among the swarm, and a randomized solution within the search space. GIO is then evaluated by using 23 classic functions that consist of seven high dimension unimodal functions (HDUF), six high dimension multimodal functions (HDMF), and ten fixed dimension multimodal functions (FDMF). Through simulation, GIO is superior to golden search optimizer (GSO), grey wolf optimizer (GWO), puzzle optimization algorithm (POA), and coati optimization algorithm (COA) in handling most of these functions. GIO is the first finest in tackling seventeen functions and second finest in tackling six functions. Tight competition occurs between GIO and COA due to the performance of COA which becomes the second finest in handling most of these functions.
Optimized triangular observer based adaptive supertwisting sliding mode control for wind turbine system
Authors: El Bouassi Sanae , El Afou Youssef , Chalh Zakaria , Mellouli El Mehdi , Haidi Touria
Published: October 2024

This paper presents a modified adaptive supertwisting sliding mode controller (AST-SMC) that dynamically adjusts control settings without prior knowledge of uncertainty limits, thereby removing chattering and putting reliability first while maintaining the original benefits of sliding mode control (SMC). First, we model and build the wind turbine system using three different controllers: the AST-SMC, the supertwisting sliding mode controller (ST-SMC), and the first-order sliding mode controller (FOSMC). A second comparison is necessary. Only the rotor speed is available to the control law because of concealed state information, which makes use of the full system state. In order to minimize observing errors over time, an asymptotic observer triangle is used to estimate the unknown rotor acceleration. By improving AST-SMC's control law, particle swarm optimization finds the most effective controller. The stability of AST-SMC over a finite time is shown via the Lyapunov stability theorem. Based on simulation findings, it is proven to be more effective than traditional SMC in wind turbine system control. It excels in settling time, tracking accuracy, energy consumption, and control input smoothness.
Improving baseline reduction for emotion recognition based on electroencephalogram signals
Authors: Agus Wirawan I Made , Mahendra Darmawiguna I Gede , Nyoman Pascima Ida Bagus
Published: October 2024

The baseline reduction method has been widely used to define electroencephalogram (EEG) signal patterns. However, because the baseline signal in this approach contains artifacts, the baseline reduction approach cannot perform optimally. As a result, decreasing artifacts in the baseline signal is critical. The mean, Gaussian, and Savitzky-Golay filters will be compared in this study to minimize artifacts in the baseline signal. Three secondary datasets are utilized to evaluate these approaches' capacity to remove artifacts. These three strategies are also tested with the convolution neural network classification algorithms. When applied to the dataset for emotion analysis using physiological signals (DEAP) and a dataset for multimodal research of affect, personality traits, and mood on individuals and groups (AMIGOS) datasets, the mean filter can increase baseline reduction performance based on twenty-four test scenarios. On the data readiness for machine learning research (DREAMER) dataset, however, the Gaussian filter is preferable. The relative difference approach was employed in this study's baseline reduction process to generate EEG signal patterns that are easy to recognize throughout the classification phase, which impacts increasing accuracy.
Developing standard criteria for robotic process automation candidate process selection
Authors: Yadav Neelam , P. Panda Supriya
Published: October 2024

Robotic process automation (RPA) is a cutting-edge technology that provides software robots to repeat and mimic the repeatable tasks that a human user earlier performed. The use of software robots is encouraging because of their cost efficiency and easy implementation. Selecting and prioritizing a candidate process for automation is always challenging as all the business processes in an organization are not equally suitable for RPA implementation. Various studies have highlighted several criteria found in the literature for determining, prioritising, and selecting a business process for RPA. Nevertheless, there are no set standards for evaluating and analyzing a certain process or its tasks to determine whether they may be automated to use RPA. This paper aims to develop standard criteria and propose a consistent model to select and prioritize candidate process for RPA projects. To assess these criteria's applicability in the context of RPA, surveys among subject matter experts (SMEs) are used to validate them. Principal component analysis (PCA) and correlation are used to identify the top 20 criteria. Naïve Bayes algorithm is applied on the collected data for decision-making. The developed multi-criteria model exhibits strong precision and recall measures, with training and validation accuracy of 96% and 90%, respectively.
Improving the risk profile of Indonesian enterprise taxpayers using multilabel classification
Authors: Prasetyo Teguh , Susetyo Budi , Kurnia Anang
Published: October 2024

Optimizing tax revenues is difficult in Indonesia due to obstacles such as tax evasion and tax avoidance. It is closely related to an organization's compliance with tax regulations, known as the taxpayers risk profile. However, this mechanism does not accurately detect tax avoidance and tax evasion risks. To overcome this limitation, we use a multilabel classification machine learning method in this study, which classifies a single observation into one or more labels at once. The approach involves problem transformation (binary relevance and label powerset), algorithm adaptation (multilabel k-nearest neighbor (ML-kNN) and multilabel-adaptive resonance associative map (ML ARAM)), and ensemble (label space partitioning and random k-label sets with disjoint (RAkELd)). Based on the model performance comparisons, we discovered that the ML-ARAM method based on deep learning is the best, with an average F1-score of 95.5% and a hamming loss of 7.4%. We also examine the feature importance of the best model to reduce the dimensions of features so that we can identify the dominant factors that encourage a taxpayer entity to engage in tax avoidance or tax evasion. The findings of this study improve the accuracy of tax avoidance risk detection and tax evasion risk profiles using machine learning methods, ensuring maximum tax revenues in Indonesia.
Eligibility rate of applicant’s LinkedIn account: a naïve bayes classification and visualization
Authors: Fariza Abu Samah Khyrina Airin , Athirah Ahmad Nurul , Amilah Shari Anis , Fakhira Almarzuki Hana , Arafah Zuhri , Septem Riza Lala , Abdul Halim Amir Haikal
Published: October 2024

In the digital era, social media platforms like LinkedIn have become famous for recruitment, and recruiters widely use them to find potential employees. The recruitment process is crucial in organizations, as it involves selecting qualified applicants from a diverse pool. However, the screening process and manual recruitment process entail significant time, high costs, and potential bias. Consequently, it may cause recruiting unqualified applicants and may affect the organizations. Thus, this study aims to classify and generate a list of potential job applicants by analyzing seven attributes of their LinkedIn accounts: title, location, skills, education, language, certification, and years of experience. Data are collected from LinkedIn profiles and then undergo data pre-processing. The naive Bayes (NB) algorithm is implemented as the classification algorithm and sets the classification as “eligible” or “ineligible”. The NB model achieved an accuracy testing of 89.8%, indicating good performance in classifying potential job applicants. At the same time, we measure the similarity cosine score to set the mean of the eligibility. The classification results are visualized for the suitable applicants in descending rank, allowing users to choose the applicants’ classification status efficiently. For the system usability, we managed to get 90% from the recruitment expert.
DepXGBoot: Depression detection using a robust tuned extreme gradient boosting model generator
Authors: Ananthanagu U. , Agarwal Pooja
Published: October 2024

In terms of severity and prevalence, depression is the worst. Suicide rates have risen because of this and are on the rise universally. Consequently, effective diagnosis and therapy must reduce the impact of depression. There is often more than one factor at play when determining why someone has been diagnosed with depression. In addition to alcohol and substance abuse, other possible causes include problems with physical health, adverse reactions to medications, life-changing events, and social circumstances. In this paper, exploratory data analysis is conducted to understand the insights of the sensorimotor database depression comprising depressive experiences in individuals who are either unipolar or bipolar. This study proposes a robust tuned extreme gradient boosting model generator to automatically predict the state of depression. The performance is optimized by determining the best combination of hyperparameters for the extreme gradient boosting model. By harnessing the power of advanced machine learning methodologies, this study underscores comparative analysis and the importance of data-driven innovation in mental health and clinical practice. Future developments for the robust tuned extreme gradient boosting model’s application and study to forecast depression in the sensorimotor database depression can be used to track changes in depressed states over time by integrating it with longitudinal and multimodal data.
Predicting levels of legal case difficulties using machine learning
Authors: Sari Ilmiyati , Kosasih Rifki , Indarti Dina
Published: October 2024

Lawyers play a crucial role in the courtroom, assisting clients in their defense. Because of their lack of legal expertise, a person or organization facing legal issues requires professional aid. However, we need to know how much money will be spent on paying lawyers. The level of complexity in a case can be used to determine lawyer costs. Therefore, in this research, we propose employing machine learning methodologies, i.e., random forest classifiers and support vector machines (SVM), to determine the level of legal case difficulties. The novelty of this research is applying a machine learning approach in predicting the level of difficulty of legal cases. The data utilized consists of 990 records, which are divided into training and testing data in a 90:10 ratio. The term frequency-inverse document frequency (TF-IDF) approach was then utilized to perform text preprocessing. The text-preprocessing findings are utilized as input in the classification process. According to the research findings, an accuracy value of 85%, a value of weighted average precision is 88%, and a value of weighted average recall is 85%, for support vector machine. Using random forest, we achieve an accuracy value of 89%, a value of weighted average precision is 85.6%, and a value of weighted average recall is 80%.
A terrain data collection sensor box towards a better analysis of terrains conditions
Authors: Olivier Akansie Kouame Yann , C. Biradar Rajashekhar , Rajendra Karthik , D. Devanagavi Geetha
Published: October 2024

Autonomous mobile robots are increasingly used across various applications, relying on multiple sensors for environmental awareness and efficient task execution. Given the unpredictability of human environments, versatility is crucial for these robots. Their performance is largely determined by how they perceive their surroundings. This paper introduces a machine learning (ML) approach focusing on land conditions to enhance a robot’s locomotion. The authors propose a method to classify terrains for data collection, involving the design of an apparatus to gather field data. This design is validated by correlating collected data with the output of a standard ML model for terrain classification. Experiments show that the data from this apparatus improves the accuracy of the ML classifier, highlighting the importance of including such data in the dataset.
WEKA-based machine learning for traffic congestion prediction in Amman City
Authors: Arabiat Areen , Hassan Mohammad , Almomani Omar
Published: October 2024

Traffic congestion leads to wasted time, pollution, and increased fuel consumption. Traffic congestion prediction has become a developing research topic in recent years, particularly in the field of machine learning (ML). The evaluation of various traffic parameters is used to predict traffic congestion by relying on historical data. In this study, we will predict traffic congestion in Amman City, specifically at the 8th circle, using different ML classifiers. The 8th circle links four main streets: Westbound, Northbound, Eastbound, and Southbound. Datasets were collected from the greater Amman municipality hourly. The logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), and multi-layer perceptron (MLP) classifiers have been chosen to predict traffic congestion at each street linked with the 8th circle. The waikato environment for knowledge analysis (WEKA) data mining tool is used to evaluate chosen classifiers by determining accuracy, F-measure, sensitivity, and precision evaluation metrics. The results obtained from all experiments have demonstrated that SVM is the best classifier to predict traffic congestion. The accuracy of SVM to predict traffic congestion at Westbound Street, Northbound Street, Eastbound Street, and Southbound Street was 99.4%, 99.7%, 99.6%, and 99.1%, respectively.
Artionyms and machine learning: auto naming of the paintings
Authors: Altynova Anna , Kolycheva Valeria , Grigoriev Dmitry , Semenov Alexander
Published: October 2024

Image captioning is a question of great interest in a wide range of applications. In the art market there is a particularly acute shortage of specialized machine learning methods for accelerated and at the same time in-depth study of often too specific aspects of art. One of the main difficulties is caused by ambiguous names of art works, as well as clarifying (in practice, often complicating under- standing and perception) signatures of the authors to them. Although previous research has established that captioning of photos can be done with high efficacy, there is little published data about generation of captions for artistic paintings. In this research, we utilize a transformer architecture to generate an artionym for a given painting in author’s manner. We describe the model and report its performance on different art styles. We assess the model performance with an expert evaluation and image captioning metrics, and then discuss their capacity to analyze art-related names.
Machine Learning Prediction of Quality of Life: Insight from Property Crime and Tropical Climate Analysis
Authors: MD SAKIP SITI RASIDAH , Mohd Zukri Anis Zulaikha , Masrom Suraya
Published: October 2024

This study delves into predicting the quality of life, a widely researched concept spanning various domains, employing mixed methodologies and indicators. Common predictors of overall quality of life encompass health status, socioeconomic factors, subjective well-being, and environmental conditions. Consequently, this research investigates the potential of utilizing machine learning prediction models for quality-of-life prediction, focusing on two key indicators.Five machine learning algorithms—Generalized Linear Model, Random Forest, Decision Tree, Gradient Boosted Tree, and Support Vector Machine—are empirically compared. Their performance in predicting quality of life is assessed based on property crime and tropical climate (temperature) attributes. Despite an initially low correlation value, temperature yields valuable insights for specific algorithms, enhancing predictive accuracy. This underscores the intricate and nuanced impact of seemingly unrelated factors in machine learning. The Support Vector Machine emerges as the top-performing algorithm, followed by Random Forest and Decision Tree. This paper offers a foundational research framework to aid educators and researchers in exploring quality of life prediction in-depth, utilizing property crime and temperature as key factors.
Cloud radon data for earthquake magnitude prediction using machine learning
Authors: Pratama Thomas Oka , Sunarno Sunarno , Wijatna Agus Budhie , Haryono Eko
Published: October 2024

The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grundulu fault, the research employs Random Forest (RF), Extreme Gradient Boosting (XGB), Neural Network (NN), AdaBoost (AB), and Support Vector Machine (SVM) methods. Utilizing real-time radon gas concentration measurements, the study aims to refine earthquake magnitude prediction, crucial for disaster preparedness. The evaluation involves multiple metrics like Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Squared Error (MSE), Symmetric Mean Absolute Percentage Error (SMAPE), and cnSMAPE. XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.
3D visualization diagnostics for lung cancer detection
Authors: M. Mahmoud Rana , Elgendy Mostafa , Taha Mohamed
Published: October 2024

Lung cancer is a leading cause of cancer deaths worldwide with an estimated 2 million new cases and 1·76 million deaths yearly. Early detection can improve survival, and CT scans are a precise imaging technique to diagnose lung cancer. However, analyzing hundreds of 2D CT slices is challenging and can cause false alarms. 3D visualization of lung nodules can aid clinicians in detection and diagnosis. The MobileNet model integrates multi-view and multi-scale nodule features using depthwise separable convolutional layers. These layers split standard convolutions into depthwise and pointwise convolutions to reduce computational cost. Finally, the 3D pulmonary nodular models were created using a ray-casting volume rendering approach. Compared to other state-of-the-art deep neural networks, this factorization enables MobileNet to achieve a much lower computational cost while maintaining a decent degree of accuracy. The proposed approach was tested on an LIDC dataset of 986 nodules. Experiment findings reveal that MobileNet provides exceptional segmentation performance on the LIDC dataset, with an accuracy of 93.3%. The study demonstrates that the MobileNet detects and segments lung nodules somewhat better than other older technologies. As a result, the proposed system proposes an automated 3D lung cancer tumor visualization.
Enhancing text classification through novel deep learning sequential attention fusion architecture
Authors: Shilpa Shilpa , Soma Shridevi
Published: October 2024

Text classification is a pivotal task within natural language processing (NLP), aimed at assigning semantic labels to text sequences. Traditional methods of text representation often fall short in capturing intricacies in contextual information, relying heavily on manual feature extraction. To overcome these limitations, this research work presents the sequential attention fusion architecture (SAFA) to enhance the features extraction. SAFA combines deep long sort-term memory (LSTM) and multi-head attention mechanism (MHAM). This model efficiently preserves data, even for longer phrases, while enhancing local attribute understanding. Additionally, we introduce a unique attention mechanism that optimizes data preservation, a crucial element in text classification. The paper also outlines a comprehensive framework, incorporating convolutional layers and pooling techniques, designed to improve feature representation and enhance classification accuracy. The model's effectiveness is demonstrated through 2-dimensional convolution processes and advanced pooling, significantly improving prediction accuracy. This research not only contributes to the development of more accurate text classification models but also underscores the growing importance of NLP techniques.
Abnormality-aware bone fracture detection and classification using the triple context attention model
Authors: Sultana Tabassum Nahid , Parveen Asma
Published: October 2024

In this study, a novel approach is introduced for fracture detection in bone x-ray images, introducing the triple context attention model (TCAN) that combines concentrated extensive convolutional segments with an attention mechanism to enhance positional data. The TCAN model significantly improves fracture recognition accuracy while reducing model complexity. Leveraging a diverse dataset, consistently achieving high accuracy levels across various body parts. By addressing, mislabelling issues, and employing a visual attention network (VAN), to refine the model's performance. The TCAN model emerges as a robust, computationally efficient solution, offering a remarkable average accuracy of 97.86%. This study contributes valuable advancements to medical imaging and diagnostics, providing a highly effective tool for skeletal fracture detection.
Regularized Xception for facial expression recognition with extra training data and step decay learning rate
Authors: Azrien Elang Arkanaufa , Hartati Sri , Frisky Aufaclav Zatu Kusuma
Published: October 2024

Despite extensive research on facial expression recognition, achieving the highest level of accuracy remains challenging. The objective of this study is to enhance the accuracy of current models by adjusting the structure, the data used, and the training procedure. The incorporation of regularization into the Xception architecture, the augmentation of training data, and the utilization of step decay learning rate together address and surpass current constraints. A substantial improvement in accuracy is demonstrated by the assessment conducted on the facial expression recognition (FER2013) dataset, achieving a remarkable 94.34%. This study introduces potential avenues for enhancing facial expression recognition systems, specifically targeting the requirement for increased accuracy within this domain.
CryptoGAN: a new frontier in generative adversarial network-driven image encryption
Authors: Bhat Ranjith , Nanjundegowda Raghu
Published: October 2024

There is a growing need for an image encryption scheme, for huge amount of social media data or even the medical data to secure the privacy of the patients or the user. This study introduces a ground-breaking deep learning architecture named crypto generative adversarial networks (CryptoGAN), a novel architecture for generating cipher images. This architecture has the ability to generate both encrypted and decrypted images. The CryptoGAN system consists of an initial encryption network, a generative network that verifies the output against the desired domain, and a subsequent decryption phase. The generative adversarial networks (GAN) are utilised as the learning network to generate cipher images. This is achieved by training the neural network using images encrypted from a conventional image encryption scheme such as advanced encryption standards (AES), and learning from the resulting losses. This enhances security measures when dealing with a large dataset of photos. The assessment of the performance metrics of the encrypted image, including entropy, histogram, correlation plot, and vulnerability to assaults, demonstrates that the suggested generative network may get a higher level of security.
Transfer-learning based skin cancer diagnosis using fine-tuned AlexNet by Marine Predators Algorithm
Authors: Ibrahim Khaleel Maha , Lakizadeh Amir
Published: October 2024

Melanoma represents one of the most dangerous manifestations of skin cancer. According to statistics, 55% of patients with skin cancer have lost their lives as a result of this disease. Early diagnosis of this condition will significantly reduce mortality rates and save lives. In recent years, deep learning methods have shown promising results and captured the attention of researchers in this field. One common approach is the use of pre-trained deep neural networks. In this work, a pre-trained AlexNet networks, which are networks with specified architecture and weights is used to automatic skin melanoma diagnosis.  In the transfer learning phase, by reducing the learning rate, the pre-trained network is trained to recognize Skin cancer, which is called fine-tuning. In addition, Hyperparameters of the AlexNet network have been optimized by the Marine Predators Algorithm (MPA) to enhance the network performance. Experimental findings show the satisfactory efficiency of the presented approach, with an accuracy rate of 98.47%. The outcomes demonstrate the effectiveness of the suggested approach in contrast to alternative existing methods.
Automated diagnosis of brain tumor classification and segmentation of magnetic resonance imaging images
Authors: B. Muddaraju Chandrakala , Shrinivasa Shrinivasa , Narasimhamurthy Shobha , Sontakke Vaishali
Published: October 2024

Brain tumors are one of the most prevalent disorders of the central nervous system and are dangerous. For patients to receive the best treatment, early diagnosis is crucial. For radiologists to correctly detect brain tumor images, an automated approach is required. The identification procedure can be time-consuming and prone to mistakes. In this work, the issue of fully automated brain tumor classification and segmentation of magnetic resonance imaging (MRI) including meningioma, glioma, pituitary, and no tumor is taken into consideration. In this study, convolutional neural network (CNN) and mask region-based convolutional neural network (R-CNN) are proposed for classification and segmentation problems respectively. This study employed 3,200 images as a training set and the system achieved an accuracy of 96% for classifying the tumors and 94% accuracy in segmentation of tumors.
Deep ensemble architectures with heterogeneous approach for an efficient content-based image retrieval
Authors: Asokaraj Manimegalai , Kumar Josephine Prem , Ashwin Nanda
Published: October 2024

In the field of digital image processing, content-based image retrieval (CBIR) has become essential for searching images based on visual content characteristics like color, shape, and texture, rather than relying on text-based annotations. To address the increasing demands for efficiency and precision in CBIR systems, we introduce the HybridEnsembleNet methodology. HybridEnsembleNet combines deep learning algorithms with an asymmetric retrieval framework to optimize feature extraction and comparison in extensive image databases. This novel approach, specifically custom-made for CBIR, employs a lightweight query structure skilled at handling large-scale data under resource-constrained environments. The experiments were performed on the ROxford and RParis datasets. The deep learning component of HybridEnsembleNet significantly refines the accuracy of image matching and retrieval. RParis The ROxford dataset, specifically in the medium and hard difficulty benchmarks, demonstrates an enhancement of 5.53% and 10.44%, respectively. Similarly, the RParis dataset, under medium and hard benchmarks, exhibits improvements of 3.01% and 5.83%, showcasing superior performance compared to existing models. By overcoming the traditional limitations of CBIR systems in mean average precision (mAP) metrics, HybridEnsembleNet provides a scalable, efficient, and more accurate solution for retrieving relevant images from vast digital libraries.