bluuebunny/crossref_metadata_2025 huggingface dataset:
Review of cloud computing models in education and the unmet needs
Authors: Rezqallah Malkawi Aminah , Abu Bakar Muhamad Shahbani , Dahalin Zulkhairi
Published: October 2024

This article thoroughly examines recently proposed cloud computing (CC) models used within the higher educational institutions (HEI) field, scrutinizing their objectives, structures, and incorporated requirements. Each model's unique architecture and functionality are analyzed to understand their potential educational contributions. Beyond technical considerations, the study explores nuanced requirements essential for successful integration in educational settings. The review exposes diverse aims pursued by the models, such as enhanced scalability, collaborative learning, and resource management, emphasizing their capacity to reshape traditional educational paradigms. However, a notable gap emerges-the absence of cultural and requirement elicitation models within the frameworks. Despite growing cultural diversity and varied educational needs, most models lack components addressing cultural nuances and robust requirement elicitation. In conclusion, the paper identifies a pressing need for a transformative shift in developing CC models for education. The absence of dedicated cultural and requirement elicitation models challenges the holistic effectiveness of these frameworks. Future efforts should prioritize integrating culturally sensitive components and comprehensive requirement elicitation strategies to create adaptive, universally applicable, and inclusive CC educational environments. Addressing these gaps will pave the way for a nuanced and responsive integration of CC technologies in diverse educational settings.
Implementation of fuzzy logic approach for thalassemia screening in children
Authors: Redy Susanto Erliyan , Syarif Admi , Warsito Warsito , Nisa Berawi Khairun , Ayu Sangging Putu Ristyaning , Wantoro Agus
Published: October 2024

Thalassemia is one of the most dangerous blood disorders that can lead to severe complications. It is an inherited disease, usually detected after a child is two to four years old. Identification of thalassemia is a complex task, involving many variables. Doctors generally diagnose thalassemia by using a complete blood count (CBC) and high-performance liquid chromatography (HPLC) test results. However, HPLC tests are expensive and time consuming, hence the need for other methods to identify thalassemia. There are many studies on the application of artificial intelligence for medical applications. In this study, we developed a new fuzzy-based approach to identify thalassemia based on a patient’s blood laboratory results. First, we analyzed the CBC data for blood disorder prediction. Secondly, we adopt the test results of peripheral blood smear (PBS) to identify whether the person has thalassemia. We conducted several experiments using 30 (thirty) hospital patient data and the results were compared with the results provided by experts. The experimental results show that the system can determine blood disorders with 93% accuracy and 100% precision in thalassemia prediction. This system is very effective to help doctors in diagnosing thalassemia patients.
Unified and evolved approach based on neural network and deep learning methods for intrusion detection
Authors: Boukhalfa Alaeddine , El Attaoui Anas , Rhouas Sara , El Hami Norelislam
Published: October 2024

Currently, network security has become a major concern for all entities around the world. Attackers employ various methods to disrupt services, which requires new methods to stop them all in one way. Moreover, these intrusions can evolve and overcome security measures and devices, which pushes to use new evolving methods able to accompany the evolution of these threats, to block them. In our paper, we propose a new approach for intrusion detection, founded on neural network (NN) and deep learning (DL) methods. This approach is planned to not only identify threats, but also to develop a long-term memory of them, in order to detect new ones resembling these memorized attacks, and simultaneously, to provide a single way to stop all kinds of intrusions. To test our model, we have chosen the most recently employed methods in literature, NN and DL algorithms: feedforward neural network (FNN), convolutional neural network (CNN), and long short-term memory (LSTM), then we have applied them on network security layer-knowledge discovery in databases (NSL KDD) intrusions dataset. The results of experiments were impressive for all the algorithms, with maximum performances noted by LSTM, which affirms the efficacy of our proposed method for intrusion detection.
Hybrid improved fuzzy C-means and watershed segmentation to classify Alzheimer’s using deep learning
Authors: H. Ali Esraa , Sadek Sawsan , F. Makki Zaid
Published: October 2024

Brain damage and deficits in interactions among brain cells are the primary causes of dementia and Alzheimer’s disease (AD). Despite ongoing research, no effective medications have yet been developed for these conditions. Therefore, early detection is crucial for managing the progression of these disorders. In this study, we introduce a novel tool for detecting AD using non invasive medical tests, such as magnetic resonance imaging (MRI). Our method employs fuzzy C-means clustering to identify features that enhance image accuracy. The standard fuzzy C-means algorithm has been augmented with fuzzy components to improve clustering performance. This enhanced approach optimizes segmentation by extracting image information and utilizing a sliding window to calculate center coordinates and establish a stable group matrix. These critical features are subsequently integrated with a two-phase watershed segmentation process. The resulting segmented images are then used to train an optimal convolutional neural network (CNN) for AD classification. Our methodology demonstrated a 98.20% accuracy rate in the detection and classification of segmented MRI brain images, highlighting its efficacy in identifying disease types.
Journal homepage: http://ijai.iaescore.com Detecting fraudulent financial statement under imbalanced data using neural network
Authors: Tjahyadi Hendra , Efraim Young Yosua
Published: October 2024

In this paper a novel approach for detecting fraudulent financial statements by employing a combination of neural networks and synthetic minority over sampling technique (SMOTE) is introduced. This approach is designed to tackle the problem of imbalanced datasets prevalent in fraudulent cases, which if left unaddressed will hinder the model to accurately identify fraud. Three neural network models, each representing different fraud predictors as the input layer: 28 inputs raw financial data; 14 inputs financial ratios data; and 42 inputs combination both raw financial and financial ratios data are developed. Experimental validation using established research datasets is conducted to assess the performance of the proposed method. Performance metrics, namely area under the curve (AUC), precision, and sensitivity, are used for evaluation, comparing the proposed model against existing benchmark models found in literature. Results indicate that the proposed model achieves an AUC score of 70.6% and a precision score of 2.89%, in comparable to the existing models, with a sensitivity score of 83% outperforming all counterparts. The high sensitivity rate of the proposed model underscores its practical utility for auditors and regulators, as it minimizes the risk of false negatives, thereby enhancing confidence in fraud detection.
Enhancing video anomaly detection for human suspicious behavior through deep hybrid temporal spatial network
Authors: Sriram Kusuma , Purushotham Kiran
Published: October 2024

Abnormal behavior exhibited by individuals with particular intentions is common, and when such behavior occurs in public places, it can cause physical and mental harm to others. Considering the rise in the automated approach for anomaly detection in videos, accuracy becomes essential. Most existing models follow a deep learning architecture, which faces challenges due to variations in motion. This research work develops a deep learning based hybrid architecture with temporal and spatial features. The hybrid temporal spatial network (HTSNet) consists of two customized architectures: a graph neural network (GNN) and a convolutional neural network (CNN). HTSNet combined with a novel classifier to extract features and classify normal and abnormal behavior. The performance of HTSNet is rigorously evaluated using the University of California, San Diego-Pedestrian 1 (UCSD Ped1) dataset, a benchmark in computer vision research for anomaly detection in video surveillance. The effectiveness of HTSNet is demonstrated through a comparative analysis with current state-of-the-art methods, using the area under the curve (AUC) metric as a standard measure of performance. This paper contributes to the advancement of video surveillance technology, providing a robust framework for enhancing public safety and security in an increasingly digital world.
Obstructive sleep apnea detection based on electrocardiogram signal using one-dimensional convolutional neural network
Authors: Widadi Rahmat , Rizal Achmad , Hadiyoso Sugondo , Fauzi Hilman , Said Ziani
Published: October 2024

Obstructive sleep apnea (OSA) is a respiratory obstruction that occurs during sleep and is often known as snoring. OSA is often ignored even though it can cause cardiovascular problems. Early diagnosis is needed for prevention towards worse complications. OSA clinical diagnosis can use polysomnography (PSG) while the patient is sleeping. The PSG examination includes calculating total apnea plus hypopnea every hour during sleep. However, PSG examination tends to be high cost, takes a long time, and is impractical. Since OSA is related to breathing and heart activity, the electrocardiogram (ECG) examination is an alternative tool in OSA analysis. Therefore, this study proposes OSA detection on single lead ECG using one dimensional (1D)-convolutional neural network (CNN). The proposed CNN architecture consists of 4 convolutional layers, 4 pooling layers, 1 dropout layer, 1 flatten layers, 2 dropout layers, 1 dense layer with rectified linear unit (ReLU) activation function, and 1 dense layer with SoftMax activation function. The proposed method was then tested on the ECG sleep apnea dataset from PhysioNet. The proposed model produces an accuracy of 92.69% with the average pooling scenario. The proposed method is expected to help clinicians in diagnosing OSA based on ECG signals.
Seeding precision: a mask region based convolutional neural networks classification approach for the classification of paddy seeds
Authors: Nambiar Rajashree , Bhat Ranjith , Kumara Varuna
Published: October 2024

The generation of sufficient training data that is accurately labelled for a deep neural network involves a significant amount of effort and frequently constitutes a bottleneck in the implementation process. For the purpose of this research, we are training a neural network model to perform instance segmentation and classification of crop seeds for various rice cultivars. Synthetically constructed dataset is used here. The concept of domain randomization, which offers a productive alternative to the laborious process of data annotation, serves as the basis for our methodology. We make use of the domain randomization technique in order to produce synthetic data, and the mask region-based convolutional neural network (Mask R-CNN) architecture is utilized in order to train our neural network models. A cultivar name is used to designate the seeds, and they are differentiated from one another using colors that are comparable to those used in the actual dataset of paddy cultivars. Our mission focuses on the identification and categorization of rice paddy varieties within automatically generated photographs. Farmers are able to accurately sort crop seeds from a variety of rice cultivars with the use of this approach, which is particularly useful for phenotyping and optimizing yields in laboratory settings.
Pneumonia detection on x-ray image using improved depthwise separable convolutional neural networks
Authors: Nur Alam Islam , Zain Nabiilah Ghinaa , Angela Sihotang Erna Fransisca , Amirul Jabar Bakti
Published: October 2024

A single neural network model cannot capture intricate and diverse features due to its ability to learn only a finite set of patterns from the data. Additionally, training and utilising a single model can be computationally demanding. Experts propose incorporating multiple neural network models to address these constraints to extract complementary attributes. Previous research has highlighted challenges network models face, including difficulties in effectively capturing highly detailed spatial features, redundancy in network structure parameters, and restricted generalisation capabilities. This study introduces an innovative neural network architecture that combines the Xception module with the inverse residue structure to tackle these issues. Considering this, the paper presents a model for detecting pneumonia in X-ray images employing an improved depthwise separable convolutional network. This network architecture integrates the inverse residual structure from the MobileNetV2 model, using the rectified linear unit (ReLU) non-linear activation function throughout the entire network. The experimental results show an impressive recognition rate with a test accuracy of 97.24% on the chest x-ray dataset. This method can extract more profound and abstract image features while mitigating overfitting issues and enhancing the network's generalisation capacity.
Bio inspired technique for controlling angle of attack of aircraft
Authors: Bal Subhakanta , Swain Srinibash , Sarathi Khuntia Partha
Published: October 2024

This paper deals with the design of a proportional–integral (PI) controller for controlling the angle of attack of flight control system. For the first time teaching learning-based optimization (TLBO) algorithm is applied in this area to obtain the parameters of the proposed PI controller. The design problem is formulated as an optimization problem and TLBO is employed to optimize the parameters of the PI controller. The superiority of proposed approach is demonstrated by comparing the results with that of the conventional methods like genetic algorithm (GA) and particle swarm optimization (PSO). It is observed that TLBO optimized PI controller gives better dynamic performance in terms of settling time, overshoot, and undershoot as compared to GA and PSO based PI controllers. The various performance indices like mean square error (MSE), integral absolute error (IAE), and integral time absolute error (ITAE) are improved by using the TLBO soft computing techniques. Further, robustness of the system is studied by varying all the system parameters from −50% to +50% in step of 25%. Analysis also reveals that TLBO optimized PI controller gains are quite robust and need not be reset for wide variation in system parameters.
Image analysis for classifying coffee bean quality using a multi feature and machine learning approach
Authors: Septiarini Anindita , Hamdani Hamdani , Ery Burhandeny Aji , Nurcahyono Damar , Eka Priyatna Surya
Published: October 2024

Price and customer satisfaction depend on coffee bean quality. The coffee industry must analyze coffee bean quality. Global demand for robusta coffee is high. Coffee industry professionals mostly understand coffee bean quality. Thus, an image analysis using a computer vision-based approach for classifying robusta coffee bean quality is required. Image acquisition, region of interest (ROI) detection, pre-processing, segmentation, feature extraction, feature selection, and classification are covered in this study. A multi-feature derived based on color, shape, and texture features was employed in feature extraction, followed by feature selection using principal component analysis (PCA). Several machine-learning methods classified the coffee beans. The method performance was assessed using precision, recall, and accuracy. The selected features using the backpropagation neural network (BPNN) classifier outperformed others with 98.54% accuracy.
Driver inattention detection system using multi-task cascaded convolutional networks
Authors: Soultana Abdelfettah , Benabbou Faouzia , Sael Nawal , Bouhsissin Soukaina
Published: October 2024

Driver inattention has emerged as a critical concern impacting road safety, resulting in an alarming surge in accidents and fatalities. This research introduces a novel system for detecting inattention, structured across six levels: perception, facial feature extraction, tracking driver face, and driver secondary task using pre-trained deep learning models, inattention detection, risk estimation, and alert. The system is based on image processing captured from two strategically positioned cameras that simultaneously capture the driver’s activities while driving and their facial expressions. The second contribution concerns the driver facial features extraction using multi-task cascaded convolutional networks (MTCNN), and it is comparison with the histogram of gradient (HOG)-based frontal face detector, and haar feature based cascade classifier. The algorithms were compared based on their runtime efficiency, robustness in handling varying lighting conditions, and various head movements. The MTCNN achieves high performance, reaching accuracy levels ranging from 96.4% to 99.5% on two datasets including realistic driving scenarios: the DrivFace dataset and, the driver drowsiness dataset. The comparative analysis sheds light on the strengths and weaknesses of each algorithm, providing valuable insights for selecting the most suitable face detection algorithm to use in our system.
Proactive cervical cancer risk assessment using data-driven analytics
Authors: Sreelatha Sreelatha , Shivashetty Vrinda
Published: October 2024

This study introduces a sophisticated predictive model integrating clinical and lifestyle data addressing the critical public health challenge of cervical cancer, particularly in regions lacking routine screenings. Leveraging data driven analytics, the proposed model undergoes comprehensive preprocessing, including exploratory data analysis, missing value imputation, and feature extraction. Feature selection is carried out using the XGBoost classifier to ensure model efficacy. Data normalization and class balance via oversampling techniques are applied, with model validation conducted through stratified cross-validation. The optimized feature vector is then employed to train a LightGBM model. Utilizing a retrospective dataset of 858 patients from the Hospital Universitario de Caracas, Venezuela, comprising demographic, lifestyle, and medical history data, the LightGBM model achieves an impressive accuracy of 98%, outperforming similar existing approaches. The study outcome demonstrates the effectiveness of the proposed data modelling framework and feature selection, along with the choice of LightGBM as a suitable classifier. The proposed predictive framework can efficiently aid healthcare professionals in prioritizing high-risk patients for further evaluation and intervention.
Enhancing breast cancer diagnosis: a comparative analysis of feature selection techniques
Authors: Benghazouani Salsabila , Nouh Said , Zakrani Abdelali
Published: October 2024

Breast cancer is a significant contributor to female mortality, emphasizing the importance of early detection. Predicting breast cancer accurately remains a complex challenge within medical data analysis. Machine learning (ML) algorithms offer valuable assistance in decision-making and diagnosis using medical data. Numerous research studies highlight the effectiveness of ML techniques in improving breast cancer prediction. Feature selection plays a pivotal role in data preprocessing, eliminating irrelevant and redundant features to minimize feature count and improve classification accuracy. This study focuses on optimizing breast cancer diagnostics through feature selection methods, specifically genetic algorithms (GA) and particle swarm optimization (PSO). The research involves a comparative analysis of these methods and the application of a diverse set of ML classification techniques, including logistic regression (LR), support vector machine (SVM), decision tree (DT), and ensemble methods like random forest (RF), AdaBoost, and gradient boosting (GB), using a breast cancer dataset. The models' performance is subsequently evaluated using various performance metrics. The experimental findings illustrate that PSO achieved the highest average accuracy, reaching 99.6% when applied to AdaBoost, while GA attained an accuracy rate of 99.5% when employed with both AdaBoost and RF.
A machine learning based framework for breast cancer prediction using biomarkers
Authors: Vashist Apurva , Kumar Sagar Anil , Goyal Anjali
Published: October 2024

Breast cancer is the most frequent cancer in women and the second-leading cause of cancer-related deaths globally. The main problems in managing breast cancer are high heterogeneity and the formation of therapeutic resistance. White blood cells, omics and large Wisconsin diagnostic breast cancer datasets present the three-decade genomic revolution and advance the understanding of cellular function. The precision of cancer diagnosis has also increased over the past decades. High throughput sequencing, screening, and artificial intelligence technologies have significantly improved and increased the methodologies used for diagnosis, prognosis, and therapy. This paper follows several phases of breast cancer, studies datasets and evaluate many algorithms of machine learning (ML) used for analysis and feature selection i.e. k-means, similarity correlation, genetic algorithm, and principal component analysis, have been used to recognize the subset of proteins with the highest significance for breast cancer prediction by using different biomarkers. The best correlation, as determined by Pearson correlation, between copy number and protein is 0.014, and the accuracy achieved by the genetic algorithm is 93.5% using multi-omics datasets.
Machine learning algorithms for breast cancer analysis: performance and accuracy comparison
Authors: Ayanouz Soufyane , Anouar Abdelhakim Boudhir , Ben Ahmed Mohammed
Published: October 2024

Breast cancer, a leading cause of cancer mortality among women, necessitates early detection to improve survival rates. Traditional diagnostics face accuracy and speed limitations, prompting this study to explore machine learning for enhanced diagnostics. We applied bidirectional encoder representations from transformers (BERT), long short-term memory (LSTM), naive Bayes, support vector machine (SVM), and random forest to the Breast Cancer Wisconsin dataset, implementing a thorough methodology involving data preprocessing, feature extraction, and model validation. BERT led in accuracy at 92.5%, showcasing advanced algorithms' potential in medical diagnostics, with random forest 90.6%, SVM 89.3%, LSTM 88.7%, and naive Bayes 85.2%; also showing promising results. The study underscores the importance of incorporating machine learning, especially BERT, into clinical decision-making, potentially revolutionizing breast cancer diagnostics by improving accuracy and efficiency. We recommend healthcare practitioners integrate these algorithms into their diagnostic processes. Future research should reeefine these algorithms and extend their application to enhance patient care further.
Predicting hepatitis C infection with machine learning algorithms: a prospective study
Authors: Iparraguirre-Villanueva Orlando , Ornella Flores-Castañeda Rosalynn , Chero-Valdivieso Henry , Sierra-Liñan Fernando
Published: October 2024

Globally, chronic hepatitis C virus (HCV) infection affects millions of people and leads to a high number of deaths annually. In 2019, the World Health Organization (WHO) recorded around 290,000 deaths related to HCV, a virus transmitted mainly through blood that causes liver damage. The virus has infected more than 169 million people worldwide. This study aims to compare the performance of machine learning (ML) models for HCV detection. ML models such as logistic regression (LR), random forest (RF), decision tree (DT), and catBoost classifier (CATBC) were used. To carry out this task, a dataset of 615 patient records, and 14 variables were used. This research process was carried out in multiple phases, encompassing model understanding, data analysis and cleaning, ML model training, and subsequent model evaluation. The results revealed that the gradient boosting (GB) model stood out by achieving the best performance and highest accuracy, achieving a rate of 94% in HCV detection, this demonstrates outstanding performance compared to the other models such as LR, RF, k-nearest neighbor (KNN), DT, and CATBC, which obtained accuracy rates of 89%, 93%, 85%, 93%, 93%, and 92%, respectively. It can be concluded that the GB model stands out as the best algorithm for this task.
Network intrusion detection in big datasets using Spark environment and incremental learning
Authors: Elmoutaoukkil Abdelwahed , Hamlich Mohamed , Khatib Amine , Chriss Marouane
Published: October 2024

Internet of things (IoT) systems have experienced significant growth in data traffic, resulting in security and real-time processing issues. Intrusion detection systems (IDS) are currently an indispensable tool for self-protection against various attacks. However, IoT systems face serious challenges due to the functional diversity of attacks, resulting in detection methods with machine learning (ML) and limited static models generated by the linear discriminant analysis (LDA) algorithm. The process entails adjusting the model parameters in real time as new data arrives. This paper proposes a new method of an IDS based on the LDA algorithm with the incremental model. The model framework is trained and tested on the IoT intrusion dataset (UNSW-NB15) using the streaming linear discriminant analysis (SLDA) ML algorithm. Our approach increased model accuracy after each training, resulting in continuous model improvement. The comparison reveals that our dynamic model becomes more accurate after each batch and can detect new types of attacks.
Innovative machine learning approaches for prediction of hypoglycemia in patients with type 2 Diabetes
Authors: Ramnath Gaikwad Sachin , Devi Seeta , Shekhar Sameer , Dumbre Dipali
Published: October 2024

Medical data science advances using machine learning, which predicts glucose levels. A supervised machine learning technique is employed in which regression and classification methods are used to check the prediction performance. The unsupervised machine learning technique makes clusters based on variables' similarities. Furthermore, the prediction accuracy of conventional machine learning techniques is improved by proposing a transfer learning technique. Based on a median value of 67 mg/dL, the data set is divided into two groups: group 1 (BSL 57 mg/dL to 67 mg/dL) has 50.67% of the samples, and group 2 (with BSL 68 mg/dL to 79 mg/dL) has 49.33% of the samples. In regression analysis, 5-fold cross-validation is performed. The decision tree (DT) and gradient boosting (GB) individually provide a prediction accuracy of 18.2%. Regarding classification analysis, a 10-fold cross-validation configuration is used for training and testing the model. AdaBoost, GB, random forest, and neural network achieve an accuracy rate of 66.3% and an area under curve (AUC) score of 0.731. In unsupervised learning, the datasets are divided into three clusters. The clustering result is used in regression and classification models using transfer learning. The accuracy and precision of the AdaBoost and GB are as follows: 69.6%, 0.696 with f1 0.661 and 69.6%, 0.708 with f1 0.708, respectively.
Mortality prediction of COVID-19 patients using supervised machine learning
Authors: Khuluq Husnul , Astagiri Yusuf Prasandhya , Aryani Perwitasari Dyah , Nguyen Thang
Published: October 2024

Hospitalized patients with COVID-19 are at higher risk of mortality. Machine learning (ML) algorithms have been proposed as a possible strategy for predicting mortality rates among patients hospitalized with COVID-19. This study analyzed various ML algorithms and identified the best model to predict COVID-19 mortality based on demographic, clinical, and laboratory data collected at registration. Data from 4,314 eligible patients (3,384 survivors and 930 who died) was collected from the register of three hospitals in Yogyakarta province, Indonesia, based on the confirmed predictors. Next, ML algorithms were utilized to predict mortality. Finally, the confusion matrix was used to evaluate how effective the models performed. The best five predictors from 26 features were myocardial infarction, SpO2, neutrophil, D dimer, and creatinine. The results indicate that the random forest algorithm showed better performance than other ML algorithms in terms of accuracy, sensitivity, precision, specificity, and area under the curve (AUC), achieving values of 84.15%, 84.0%, 84.1%, 83.9%, and 90.02%, respectively. Implementing ML techniques can accurately predict the mortality rate associated with COVID-19. Therefore, this predictive model can help clinicians and hospitals predict COVID patients with a greater risk of death and effectively target more appropriate treatments.
A new texture descriptor for handwritten document writer identification
Authors: Lazrak Said , Sadiq Abbelalim , Semma Abdelillah , Hannad Yaˆacoub
Published: October 2024

Writer identification is a critical task in the realm of pattern classification, aimed at determining the authorship of a manuscript based on labeled handwriting sam- ples. This area has garnered considerable attention from researchers and has seen significant advancements in the last two decades, propelled by the inte- gration of novel computer vision and machine learning algorithms. Commonly, approaches within this field rely on calculating local texture descriptors of im- ages. In this work, we propose a novel local texture descriptor method, termed multi-points local binary patterns (MP-LBP), which is an enhancement of the traditional local binary patterns (LBP) descriptor. Our approach involves apply- ing the MP-LBP descriptor to patches surrounding Harris key points and aggre- gating the image descriptors into encoded vectors using the vector of locally ag- gregated descriptors (VLAD) encoding method. These vectors are subsequently classified by a ball tree classifier to associate the document with the most plau- sible writer. To assess the efficacy of our descriptor, we conducted evaluations on five publicly accessible handwritten databases: CVL, CERUG-EN, CERUG- CH, BFL, and IAM. The results of these tests provide insights into the perfor- mance of the MP-LBP descriptor in the context of writer identification.

 
Detecting student attention through electroencephalography signals: a comparative analysis of deep learning models
Authors: Lim Eng Lye , Murugesan Raja Kumar , Balakrishnan Sumathi
Published: October 2024

In the landscape of educational technology, understanding and optimizing student attention is important to enhance student’s learning experience. This study explores the potential of using electroencephalography (EEG) signals for discerning students' attention levels during educational tasks. With a cohort of 30 participants, EEG data were meticulously collected and subjected to robust preprocessing techniques, including independent component analysis (ICA) and principal component analysis (PCA). The research then employed different deep learning algorithm such as long short-term memory (LSTM), recurrent neural network (RNN), gated recurrent unit (GRU), multi-layer perceptron (MLP), and convolutional neural network (CNN) classifiers to predict students' attention. The results reveal notable variations in the classifiers' predictive performance. Our finding revealed that the LSTM model emerged as the top performer and achieved 96% of the accuracy. This study not only contributes to the advancement of attention detection in educational technology but also underscores the importance of preprocessing methodologies, such as ICA and PCA, in optimizing the performance of deep learning models for EEG-based applications.
Indonesian news article authorship attribution multilabel multiclass classification using IndoBERT
Authors: Saputra Karen Etania , Riccosan Riccosan
Published: October 2024

Recent developments in technology have made it easier to produce digital con- tent, especially textual articles. But, it has a negative impact in the form of a rising public skepticism of digital data due to plagiarism. Indonesia, one of the world’s most populous countries, is not resistant to this problem. To resolve it, the authorship attribution (AA) task must be executed. However, there has been little investigation on AA for Indonesian articles. As a result, this research applies the AA task to an Indonesian digital news articles dataset. Continuing the previous research, dataset modification was carried out to increase data com- plexity by adding a new class, namely the author’s gender, and also by balancing the distribution of data versus labels to minimize potential overfitting, and model hyper-parameter configurations were carried out to enhance the results gained. This research successfully applied the IndoBERT model to the Indonesian AA task, yielding results in the form of precision = 0.92, recall = 0.90, and F1-score = 0.91. These results indicate that the Indonesian AA task has a lot of potential for development since it identifies writing patterns that may benefit the forensic field, detect plagiarism, and analyze Indonesian texts.
Securing the internet of things frontier: a deep learning ensemble for cyber-attack detection in smart environments
Authors: Venkataraya Premalatha Deepa , Ramanujam Sukumar
Published: October 2024

This study presents a novel and innovative approach using deep learning (DL) ensemble technique to improve the security of internet of things (IoT) by identifying intricate cyber-attacks. By utilising advanced DL models like deep neural network (DNN) and long short-term memory (LSTM), our approach significantly enhances the accuracy of categorization compared to basic models. The initial binary classifier achieved an accuracy of 85.2%, while the multi-class classifier achieved an accuracy of 79.7%. Both classifiers continually enhanced, achieving accuracies of 99.34% and 98.26%, respectively, after 100 epochs. Real-time scenario evaluations showed that the average execution time per sample record was 0.9439 ms, confirming its efficiency. The DL ensemble exhibited improved performance in comparison to traditional models, indicating its potential for wider implementation in IoT security. The study not only emphasises significant improvements in accuracy, but also emphasises the method’s ability to perform well across many evaluation measures. This study presents a thorough and pragmatic method for identifying cyber-attacks in IoT settings. The stacked ensemble technique outperforms earlier models and fulfils real-time processing requirements, offering substantial advancements in IoT security. These findings enhance both the theoretical comprehension and practical application, establishing a novel benchmark for protecting intelligent IoT systems.
Sentiment analysis of student’s comments using long short-term memory with multi head attention
Authors: Bhagat Bhavana Prasanjeet , Dhande-Dandge Sheetal S. , Raut Sandeep R
Published: October 2024

Classroom teaching is a viable and effective approach for enhancing student learning and promoting engagement in the educational process. The opinions of students play a vital role in the evaluation of teachers. This paper presents a comprehensive overview of sentiment analysis techniques based on recent research and subsequently explores machine learning, i.e., ensemble classifiers, deep learning, long short-term memory (LSTM), convolutional neural network (CNN), LSTM with single attention, LSTM with multi-head attention, and feature extraction techniques (TFidfVector and Word2Vec), in the context of sentiment analysis over student opinion datasets, i.e., the Vietnamese student feedback corpus, as well as data collected from a final-year student's comment in 2023. Further, the Vietnamese student feedback corpus is translated to English and pre-processed with the proposed framework, which yields interesting facts about the capabilities and deficiencies of different methods. In this paper, we conducted experiments with ensemble classifiers, LSTM and CNN, LSTM with single attention, and LSTM with multi-head attention. We conclude that LSTM with multi-head attention produces an accuracy result of 95.57%, which outperform as compare to other three baseline methods and earlier studies.
Leveraging multimodal deep learning for natural disaster event classification and its damage severity analysis through social media posts
Authors: Kasturi Nivedita , Guruputra Totad Shashikumar , Ghosh Goldina
Published: October 2024

Accurate and timely information is essential for coordinating an effective disaster response. Traditional methods have struggled to efficiently categorize disaster events and assess damage severity due to the variety and complexity of data sources. Previous research has focused on specific tasks, such as information gathering or humanitarian assistance, but has not adequately addressed the assessment of disaster damage severity. This paper proposes a hybrid learning model to improve disaster event classification and damage severity identification. The model combines image and text data in a cooperative way, using ResNet50 to extract features from images and a LSTM with attention mechanism to learn sequences from text. This combination allows for a more contextual and informative representation of the input data. Compared to existing approaches, the proposed multimodal approach achieves significantly better results in disaster event classification. Apart from the proposed model also shows promising outcome for damage severity of disaster. These advancements are especially important for real-world applications such as disaster management and response coordination, where accuracy and reliability are essential. The comprehensive methodology and empirical results presented in this paper demonstrate the effectiveness and potential of using hybrid learning models to leverage multimodal data for unique and sophisticated analytical tasks in disaster scenarios
Federated inception-multi-head attention models for cyber-attacks detection
Authors: AL-Halboosi Imad Tareq , Mohamed Elbagoury Bassant , Amin El-Regaily Salsabil , M. El-Horbaty El-Sayed
Published: October 2024

With the proliferation of internet of things (IoT) devices, ensuring the security of these interconnected systems has become a critical concern. Cyberattacks targeting IoT devices pose significant threats to individuals and organizations due to the generation of vast amounts of data across many connected devices, which traditional centralized methods cannot solve. Federated learning (FL) could be a promising solution to mitigate privacy concerns associated with centralized approaches and address cybersecurity concerns. This paper uses FL and deep learning (DL) approaches to cybersecurity in IoT applications. The goal of cyber security is achieved by forming a federation of acquired and shared models at the head of the various participants. We use inception time and multi-head attention (CNN) algorithm based on FL to detect cyber-attacks and avoid data privacy leaks under two distribution modes, namely IID and Non-IID. In contrast, the FedAvg and FedMA algorithms aggregate local model updates. A global model is produced after several communication rounds between the IoT devices and the model parameter server. Cyber threats are simulated using edge-IIoT datasets. Experiment results show that the federated inception model's best global accuracy was 93, 91%, and 93, 49% using multi-head attention.
Enhancing intrusion detection in next-generation networks based on a multi-agent game-theoretic framework
Authors: Lakshminarayana Sai Krishna , Basarkod Prabhugoud I.
Published: October 2024

With cyber threats becoming increasingly sophisticated, existing intrusion detection systems (IDS) in next generation networks (NGNs) are subjected to more false-positives and struggles to offer robust security feature, highlighting a critical need for more adaptive and reliable threat detection mechanisms. This research introduces a novel IDS that leverages a dueling deep Q-network (DQN) a reinforcement learning algorithm within game-theoretic framework simulating a multi-agent adversarial learning scenario to address these challenges. By employing a customized OpenAI Gym environment for realistic threat simulation and advanced dueling DQN mechanisms for reduced overestimation bias, the proposed scheme significantly enhances the adaptability and accuracy of intrusion detection. Comparative analysis against current state-of-the-art methods reveals that the proposed system achieves superior performance, with accuracy and F1-score improvements to 95.02% and 94.68%, respectively. These results highlight the potential scope of the proposed adaptive IDS to provide a robust defense against the dynamic threat landscape in NGNs.
Transliteration and translation of Hindi language using integrated domain-based Auto-encoder
Authors: K Vathsala M , Lingareddy Sanjeev C.
Published: October 2024

The main objective of translation is to translate words' meanings from one language to another; in contrast, transliteration does not translate any contextual meanings between languages. Transliteration, as opposed to translation, just considers the individual letters that make up each word.  In this paper an Integrated deep neural network transliteration and translation model (NNTT) based autoencoder model is developed. The model is segmented into transliteration model and translation model; the transliteration involves the process of converting text from one script to another evaluated on the Dakshina dataset wherein Hindi typically uses a sequence-to-sequence model with an attention mechanism, the translation model is trained to translate text from one language to another. Translation models regularly use a sequence-to-sequence model performed on the WAT (Workshop on Asian Translation) 2021 dataset with an attention mechanism, similar to the one used in the transliteration model for Hindi. The proposed NNTT model merges the in-domain and out-domain frameworks to develop a training framework so that the information is transferred between the domains. The results evaluated show that the proposed model works effectively in comparison with the existing system for the Hindi language.
Malay phoneme-based subword news headline generator for low-resource language
Authors: Tsann Phua Yeong , Hooi Yew Kwang , Fadzil Hassan Mohd , Yok Wooi Matthew Teow
Published: October 2024

The booming of technology has significantly increased the amount of news articles for readers. The headline of news plays an essential role in attracting readers. Traditionally, crafting the news headline is a manual task at the news desk. The motivation of this paper is to address the issues faced in low resource languages, such as the Malay language. The main contribution of this paper is a new hybrid model based on extractive- and abstractive-based text summarization with the integration of a geographical linguistics model; a Malay phoneme-based subword embedding has been developed to solve the complex morphological issue in the Malay language-based computational linguistic applications. The experiment involves various sequence-to sequence (seq2seq) models to generate the Malay news headlines. Besides that, the out-of-vocabulary (OOV) is assessed in the models. From the experiment, the proposed hybrid text summarization model shows significant improvement over the baseline models above 11.00 in ROUGE-1, 4.00 ROUGE-2, and 11.00 in ROUGE-L. The proposed model can reduce the OOV rate to below 15%.
The integrated smart system to assist elderly at home
Authors: Wicaksono Handy , Santoso Petrus , Sugiarto Indar , Gondowardoyo Sean , Wijaya Andrew , Halim Jason , Hafizah Kamarudin Nazhatul
Published: October 2024

Some elders prefer to live independently rather than in a nursing home. However, they need to be assisted because of health and safety reasons. We developed an integrated system consisting of a smart home, a mobile robot, and a wearable device, using message queuing telemetry transport (MQTT) protocol to communicate with each other. The smart home can be monitored and controlled via a mobile application. We use the telepresence robot equipped with a light detection and ranging (LIDAR) sensor to perform simultaneous localization and mapping (SLAM) and autonomous navigation. The wearable device application is used to detect older adults' heart rate, the steps taken, and the burnt calories. We also add pose detection and location estimation using a depth camera powered by an efficient deep-learning algorithm. We also developed an Android application as a dashboard that monitors and controls all system components. Our system can facilitate communication between its various components.
Spectral splitting of speech signal using time varying recursive filters for binaural hearing aids
Authors: Chilakawad Aparna , N. Kulkarni Pandurangarao
Published: October 2024

Speech perception in noisy environments is reduced in human with sensorineural hearing loss (SNHL) due to masking. Moderate SNHL cannot be cured medically hence masking effects should be reduced to enhance speech perception. To reduce masking, processing delay and hardware complexity the present paper is proposed a scheme to partition the voice signal into two signals which are complementary to each other by using the filter-bank summation method (FBSM) with a set of time-varying recursive band pass filters. Performance of the filter is evaluated with following measures: perceptual evaluation of speech quality (PESQ), mean opinion score (MOS) for speech quality and modified rhyme test (MRT) for speech intelligibility. The test signals used for the evaluation of quality are a syllable and a word and for the evaluation of intelligibility 300 monosyllabic words are used. The results demonstrated an increase in the quality and intelligibility of processed speech in a noisy environment. As a result, there is an enhancement in perception of processed speech in a noisy environment.
Minimization of the circuit components with modified cascaded multilevel inverter topology
Authors: Singh Raghvendra Pratap , Nigam Prateek , Thakre Kishor
Published: October 2024

This article examines a modified multilevel inverter circuit that uses basic units connected in cascade. The suggested circuit can be used with an inverter that is symmetrical or asymmetric. The magnitude of the DC voltage source is determined using a variety of methods in order to generate a large number of voltage levels. For both symmetrical and asymmetrical configurations, the magnitude of two DC sources in basic units can be used. The DC voltage source's magnitude is the same for each unit in the symmetrical configuration. However, in an asymmetrical configuration, the value of the DC source for the fundamental units is inequitable, and their magnitudes are obtained using various techniques. Comparison study demonstrates that the suggested circuit requires minimum components, reduces power loss, and boosts inverter efficiency. Additionally, in comparison to modern topologies, the standing voltage across the switches is acceptable. To verify the effectiveness of the investigated topology, simulation results for 15, 17, 23, and 31-level inverters are analysed.