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The Bay of Bengal is a vital confluence of natural diversity and human lives, renowned for its vast area, strategic location and ecological significance. In the Bay of Bengal, the maritime environment and marine resources play a critical role in sustainable development and human rights, yet it faces significant problems due to maritime environmental deterioration and human rights violations. This research paper explores safeguarding Bangladesh’s maritime environment in the Bay of Bengal while also protecting human rights in light of enormous challenges, along with the convoluted connection between international and national laws and maritime environmental protection measures, highlighting inadequacies in their application for sustainable management of marine resources and human rights. This paper examines environmental deterioration caused by pollution, rising sea levels, climate change and maritime conflicts, focusing on the impact on coastal populations and investigating humanity’s dilemma. Protection gaps and barriers to legal enforcement are examined, exposing limits in realizing maritime human rights. Their rights to health, food security and water are jeopardized by climate change and unsustainable practices. In the current laws, a hiatus exists between legal principles and their practical application. This paper has an extensive proposal multifaceted approach and consolidating fragmented laws, defining jurisdictional borders, strengthening enforcement and harmonizing national legislation with international maritime environmental and human rights standards. This paper concludes that safeguarding the Bay of Bengal is not just an environmental imperative but also a human rights obligation. To ensure the Bay of Bengal thrives as an ecological richness of maritime activities and human rights, it is essential to align the balance in the field of protecting the maritime environment and human rights.

Educational Data Mining (EDM) is the process of extracting useful information and knowledge from educational data. EDM identifies patterns and trends from educational data, which can be used to improve academic curriculum, teaching and assessment methods, and students' academic performance. Thus, this study uses EDM techniques to analyze the performance of higher secondary students in Bangladesh. Three crucial categories, such as good, average, and poorly-performing students are considered for analysis. Four significant aspects of students' performance are emphasized for evaluation in this study. Firstly, predicting students' academic final examination performance in terms of internal college examination. Secondly, identifying all subjects' impact on classifier performance. Thirdly, examining students' performance progression during their studies and relating with subject-wise improvement or degradation. Fourthly, discovering consistent patterns of students' performance based on previous internal examination performance trends. The classification result reveals the correlation between internal examination and final academic performance. In addition, it resembles the predictor subjects for academic performance. The result also highlights the consistent pattern of students' consecutive internal examinations' performance. Thereafter, college administration can take necessary supportive initiatives for poorly-performing students and encourage good-performer students to continue excelling.

Ultrasound imaging has become one of the most frequently employed modalities to detect and classify breast irregularities, which is a relatively cost-effective and important complement to mammography. To assist radiologists in locating worrisome lesions and improving the accuracy of diagnosis, a computer-aided diagnosis system is proposed which incorporates the knowledge of Generative Adversarial Network (GAN), weighted average based ensemble technique, and feature fusion based ensemble technique. After performing encoder decoder based lesion segmentation incorporating weighted ensemble architecture, a dual-staged feature fusion-based stacked ensemble meta-classifier architecture is employed for the final classification where three deep neural network branches are employed, and the generated feature maps from those branches were fused and fed to the fully connected network to achieve the final diagnosis result. The residual learning architecture and the pretrained foundation made the system faster, whereas compound scaling and ensemble architecture boosted the overall performance. The proposed methodology is evaluated on the BUSI, UDIAT, and Thammasat datasets. The Dice score reached to 0.8397, and the IoU score reached to 0.7482 in segmentation on the benign lesions of BUSI dataset whereas the classifier achieved a highest accuracy of 99.7%, F1-score of 99.7%, and AUC score of 0.999 in classification on the BUSI dataset. The results on the UDIAT and Thammasat datasets also indicate that our proposed method shows better performance than other methods. Thus, the proposed architecture can be considered for easy and automated diagnosis purposes.

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by the symptoms of inattention, hyperactivity, and impulsivity that significantly affect daily functioning. It is usually 1st diagnosed in childhood and often lasts into adulthood. Various researchers introduced different statistical tools to identify children with ADHD based on phonotype, vision, and image-based data. The main causes of children with ADHD are still unknown and its diagnostic accuracy rate remains low. There is still some scopes to establish valid biomarkers for ADHD and improve the classification accuracy, which will help for children enhance their quality of life. This study aimed to establish and determine potential biomarkers for children with ADHD and then proposed a machine learning (ML)-based ADHD detection system. A multicenter approach was employed to collect Reverse Stroop Task (RST) data, including age, behavioral, and physiological indicators from 72 children with ADHD (aged 6–13 years) and 171 typically developing (TD) children. Moreover, we also collected the signal information from each subject using functional near-infrared spectroscopy (fNIRs) and quantified the change in prefrontal cortex oxygenated hemoglobin during RST. At the same time, we also computed the mean signal for every channel during the last 20 seconds of RST. We selected 70% of the dataset as a training set (ADHD: 51 and TD: 120) and the remaining dataset was used as a testing set (ADHD: 21 and TD: 51). To determine the potential biomarkers for ADHD, we employed independent t-test (p

A type of blood malignancies known as leukemia leads to elevated quantities of abnormally formed blood cells and often originates in the bone marrow. An abrupt rise in the quantity of immature blood cells is one of the distinctive characteristics of acute leukemia, that predominantly affects both children and adults. The possibility exists to drastically lower the death rate associated with acute leukemia through early detection and diagnosis. A significant and time-consuming task for the early detection of acute leukemia is microscopic examination of blood cells. In this paper, a two-staged deep learning-based computer-aided diagnosis system regarding microscopic blood smear images is proposed to assist hematologists and improve the diagnosis accuracy of acute leukemia in which for the feature fusion-based ensemble, two deep neural network branches adopting pretrained, fine-tuned EfficientNetB7 and MobileNetV3Large architectures were employed, and feature maps generated from those branches were fused and fed to the second stage of the architecture to achieve the final result. Additional dropout layers and ReLu activation were employed in the architecture to speed up the network, and compound scaling, bottleneck, and fusion architectures enhanced the overall performance. The ALLIDB1, ALLIDB2, and ASH databases were incorporated to evaluate the performances of the proposed method. The experimental findings demonstrated that the proposed approach detected acute leukemia (ALL, AML, Healthy) with an accuracy of 99.3%, F1-score of 99.3%, and AUC score of 0.997. Whereas in detecting acute lymphocytic leukemia (ALL, Healthy) and acute myeloid leukemia (AML, Healthy), the accuracies reached 100.0% and 99.8%, respectively. Thus, we believe that clinics can adopt our proposed architecture for quick and automated diagnosis.

Hand gesture-based Sign Language Recognition (SLR) serves as a crucial communication bridge between hard of hearing and non-deaf individuals. The absence of a universal sign language (SL) leads to diverse nationalities having various cultural SLs, such as Korean, American, and Japanese sign language. Existing SLR systems perform well for their cultural SL but may struggle with other or multi-cultural sign languages (McSL). To address these challenges, this paper introduces a novel end-to-end SLR system called GmTC, designed to translate McSL into equivalent text for enhanced understanding. Here, we employed a Graph and General deep-learning network as two stream modules to extract effective features. In the first stream, produce a graph-based feature by taking advantage of the superpixel values and the graph convolutional network (GCN), aiming to extract distance-based complex relationship features among the superpixel. In the second stream, we extracted long-range and short-range dependency features using attention-based contextual information that passes through multi-stage, multi-head self-attention (MHSA), and CNN modules. Combining these features generates final features that feed into the classification module. Extensive experiments with five culture SL datasets with high-performance accuracy compared to existing state-of-the-art models in individual domains affirming superiority and generalizability.

Parkinson’s disease (PD) is a neurological disorder caused by degeneration of dopaminergic neurons in the midbrain. PD patients mainly suffer from motor symptoms, which significantly impact their daily lives. The diagnostic criteria for PD include the presence of muscle rigidity, tremor, and postural reflex disturbances. The Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) is the standard tool for evaluating PD symptoms, part III of which is dedicated to motor symptoms. That part involves a comprehensive set of specific physical examinations, and physicians assign semi quantitative scores from 0 to 4. However, this approach faces notable challenges, including the requirement for movement-disorder experts proficient in using MDS-UPDRS and the presence of substantial inter rater variability even among experts. Overcoming these challenges requires a quantitative and objective assessment method. Given that the rating of motor symptoms predominantly involves assessing kinematic characteristics, the integration of sensor-based devices and machine learning techniques holds the potential to outperform human experts in symptom evaluations. This study used the Leap Motion optical motion-capture device to quantitatively measure and analyze hand movements while 45 PD patients performed the following 3 tasks from the MDS-UPDRS part III: finger tapping (FT), hand opening and closing (OC), and forearm pronation and supination (PS). Data from these tasks were collected and processed, resulting in the extraction of 31 movement patterns for each task. Additionally, 69 statistical features were extracted from each movement pattern, yielding 2139 features for each task. We subsequently employed a random forest algorithm to select the top 15% of features based on the reduction of Gini impurity. These selected features were subsequently fed into a sequential-forward-floating-selection algorithm, combined with a support vector machine, to identify relevant feature combinations and predict the severity of the motor symptoms. The classification accuracy was 87.0% for FT, 93.2% for OC, and 92.2% for PS. One-way analysis of variance identified 13 features of the OC task that were significantly more discriminative for classifying the movement disability of PD patients ( ${p}$

Sign Language Recognition (SLR) represents a revolutionary technology aiming to establish communication between hearing impaired and non-hearing impaired communities, surpassing traditional interpreter-based approaches. Existing efforts in automatic sign recognition predominantly rely on hand skeleton joint information, steering clear of image pixels to address challenges like partial occlusion and redundant backgrounds. Many researchers have been working to develop automatic sign recognition using hand skeleton joint information instead of image pixels to overcome partial occlusion and redundant background problems. However, body motion and facial expression play an essential role in increasing the inner gesture variance in expressing sign language emotion besides hand information for large-scale sign word datasets. Recently, some researchers have been working to develop muti-gesture-based SLR recognition systems, but their performance accuracy and efficiency are unsatisfactory for real-time deployment. Addressing these limitations, we propose a novel approach, a two-stream multistage graph convolution with attention and residual connection (GCAR) designed to extract spatial-temporal contextual information. The multistage GCAR system, incorporating a channel attention module, dynamically enhances attention levels, particularly for non-connected skeleton points during specific events within spatial-temporal features. The methodology involves capturing joint skeleton points and motion, offering a comprehensive understanding of a person’s entire body movement during sign language gestures and feeding this information into two streams. In the first stream, joint key features undergo processing through sep-TCN, graph convolution, deep learning layer, and a channel attention module across multiple stages, generating intricate spatial-temporal features in sign language gestures. Simultaneously, the joint motion is processed in the second stream, mirroring the steps of the first branch. The fusion of these two features yields the final feature vector, which is then fed into the classification module. The model excels in capturing discriminative structural displacements and short-range dependencies by leveraging unified joint features projected onto a high-dimensional space. Owing to the effectiveness of these features, the proposed method achieved significant accuracies: 90.31%, 94.10%, 99.75%, and 34.41%, for the WLASL, PSL, MSL, and ASLLVD large-scale datasets, respectively, with 0.69 million parameters. The high-performance accuracy, coupled with stable computational complexity, demonstrates the superiority of the proposed model. This innovative approach is anticipated to redefine the landscape of sign language recognition, setting a new standard in the field.

Abstract Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.

Attention deficit hyperactivity disorder (ADHD) for children is one of the behavioral disorders that affect the brain’s ability to control attention, impulsivity, and hyperactivity and its prevalence has increased over time. The cure for ADHD is still unknown and only early detection can improve the quality of life for children with ADHD. At the same time, children with ADHD often suffer from various comorbidities like autism spectrum disorder (ASD), major depressive disorder (MDD), etc. Various researchers developed computational tools to detect children with ADHD depending on handwriting text. Handwriting text-based systems are depending on a specific language that causes problems for non-native speakers of that language. Moreover, very few researchers considered other comorbidities such as ASD, MDD, etc., in their studies to detect ADHD for children. In this study, handwriting patterns or drawing is assumed as an aspect to identify/detect ADHD children who have ASD using machine learning (ML)-based approaches. We collected handwriting samples from 29 Japanese children (14 ADHD with coexisting ASD children and 15 healthy children) using a pen tablet. We asked each child to draw two patterns, namely zigzag lines (ZL) and periodic lines (PL) on a pen tablet and repeated them three times. We extracted 30 statistical features from raw datasets and these features were analyzed using sequential forward floating search (SFFS) and selected the best combinations or subsets of features. Finally, these selected features were fed into seven ML-based algorithms for detecting ADHD with coexisting ASD children. These classifiers were trained with leave-one-out cross-validation and evaluated their performances using accuracy, recall, precision, f1-score, and area under the curve (AUC). The experimental results showed that the highest performance scores (accuracy: 93.10%; recall: 90.48%; precision: 95.00%; f1-score: 92.68%; and AUC: 0.930) were achieved by the RF-based classifier for the PL predict task. This study will be helpful and provide evidence of the possibility of classifying ADHD children having ASD and healthy children based on their handwriting patterns.

Sign language recognition is crucial for improving communication accessibility for the hearing impaired community and reducing dependence on human interpreters. Notably, while significant research efforts have been devoted to many prevalent languages, Korean Sign Language (KSL) remains relatively underexplored, particularly concerning dynamic signs and generalizability. The scarcity of KSL datasets has exacerbated this limitation, hindering progress. Furthermore, most KSL research predominantly relies on static image-based datasets for recognition, leading to diminished accuracy and the inability to detect dynamic sign words. Furthermore, most KSL research predominantly relies on static image-based datasets for recognition, leading to diminished accuracy and the inability to detect dynamic sign words. Additionally, existing KSL recognition systems grapple with suboptimal performance accuracy and heightened computational complexity, further emphasizing the existing research gap. To address these formidable challenges, we propose a robust dynamic KSL recognition system that combines a skeleton-based Graph Convolution network with an attention-based neural network, effectively bridging the gap. Our solution employs a two-stream deep learning network to navigate the intricacies of dynamic signs, enhancing accuracy by effectively handling non-connected joint skeleton features. In this system, the first stream meticulously processes 47 pose landmarks using the Graph Convolutional Network (GCN) to extract graph-based features. These features are meticulously refined through a channel attention module and a general CNN, enhancing their temporal context. Concurrently, the second stream focuses on joint motion-based features, employing a similar approach. Subsequently, these distinct features from both streams are harmoniously integrated and channelled through a classification module to achieve precise sign-word recognition. A significant contribution of our work lies in creating a novel KSL video dataset, addressing the scarcity of data in this domain. This dataset comprises comprehensive information, including skeletal data from 47 joint skeleton points and details from both hands, body, and facial expressions. Our dataset aims to fill a critical gap in KSL research and provides a solid foundation for more extensive and inclusive studies in the field. Through this innovative approach, we aim to contribute significantly to the field of KSL recognition, filling the gaps in dynamic sign recognition and bolstering the accessibility of sign language communication within the Korean hearing impaired community and beyond. Our evaluations on a benchmark KSL-77 dataset and our proprietary lab dataset resulted in recognition accuracies of 99.87% and 100%, respectively. These results highlight the superiority of our model in the KSL recognition domain, outperforming existing models in terms of accuracy and computational efficiency.

Osteoporosis, a common skeletal disorder, necessitates the identification of its risk factors to develop effective preventive measures. It is crucial to identify the underlying risk factors and their relationships with the response class attribute. Different machine learning (ML) algorithms and feature selection approaches are used to estimate the risk of osteoporosis. However, ML-based algorithms may struggle to detect risk factors as well as grading of osteoporosis due to different measurement scale of data and their probability distributional assumptions. Violation of these assumptions and results interpretation may be improper in the presence of heteroscedasticity, or unequal variance in data. In this study, we seek to overcome distribution assumption constraints and improve the interpretability of our results by using rigorous statistical approaches, ensuring a robust and trustworthy study of osteoporosis risk variables. The study dataset consists of 40 clinical, lifestyle, and genetic attributes, allowing for a comprehensive analysis of potential risk factors associated with osteoporosis. In the analysis, after confirming the normality assumption using Kolmogorov-Smirnov and Shapiro-Wilk tests, independent t-test assess the factor ALT, FBG, HDL-C, LDL-C, FNT, TL, TLT, and URIC has a substantial impact on the risk of developing osteoporosis. The Mann-Whitney U test for the non-normal FN variable likewise showed a p-value of less than 0.05, indicating that this variable has a significant effect on the likelihood of developing osteoporosis. Based on the chi-square test p-values for the categorical factors, gender, calcium, calcitriol, bisphosphonate, calcitonin, COPD, CAD, and drinking have a severe significant risk of osteoporosis. For developing the predictive Gaussian Process (GPs) model, we proposed two customized integrated GP kernels into the analysis to enhance the modeling of complex relationships within the data. The proposed GP kernel model (modified kernel 2) outperforms the other individual kernels in this experiment and has the best accuracy score of 86.64% and AUC score of 86.63% on osteoporosis data. Moreover, a simulation study is also conducted to robustify the proposed model, the results are improved by different evaluation matrices ranging in accuracy from 0.60-11.41% and AUC from 0.50-11.60%.

Attention deficit hyperactivity disorder (ADHD) for children is one of the most common neurodevelopmental disorders and its prevalence has increased globally. Children with ADHD are faced with various difficulties, including inattention, impulsivity, and hyperactivity. Therefore, it is important to use an early detection system that is simple, non-invasive, and automated. Children with ADHD also suffer from other coexisting one or more disorders, including major depressive disorder (MDD), autism spectrum disorder (ASD), etc and it creates more challenges to detect ADHD children. Very few researchers considered such kinds of these comorbidities in their studies to detect ADHD for children. In this work, we proposed a deep learning (DL)-based algorithm to identify ADHD children with coexisting ASD. Functional near-infrared spectroscopy (fNIRs) signals from thirteen ADHD children who have coexisting ASD and fifteen typically developing (TD) children were recorded during the drawing of handwriting patterns. We asked each child to draw periodic lines (PL) and zigzag lines (ZL) under the predict and trace condition and repeated them three times. Finally, a hybrid approach was designed by combining convolutional neural networks (CNN) and bidirectional long short-time memory (Bi-LSTM) to determine children with ADHD who have ASD. The experimental results showed that our proposed hybrid approach could determine ADHD children with coexisting ASD with a classification accuracy of 94.0%, a sensitivity of 89.7%, specificity of 97.8%, f1-score of 93.3%, and AUC of 0.938, respectively, for the PL predict task.

Attention deficit hyperactivity disorder (ADHD) is one of the major psychiatric and neurodevelopment disorders worldwide. Electroencephalography (EEG) signal-based approach is very important for the early detection and classification of children with ADHD. However, diagnosing children with ADHD using full EEG channels with all features may lead to computational complexity and overfitting problems. To solve these problems, machine learning (ML)-based ADHD detection was designed by identifying optimal channels and its significant features. In this work, support vector machine and t-test based, two separate approaches were devised to select optimal channels individually and then proposed a hybrid channel selection approach by combining these two channel selection methods in order to select the optimal channels. After that, LASSO logistic regression-based model was used to select the important features from the selected channels. Finally, six ML-based classifiers, like Gaussian process classification (GPC), random forest, k-nearest neighbors, multilayer perceptron, decision tree, and logistic regression were applied for the detection of children with ADHD and evaluated their performances using accuracy and area under the curve (AUC). This study utilized a total of one hundred twenty-one children, with sixty-one children with ADHD, aged 7-12 years, and had nineteen channels. Ten different channels were selected by SVM based and an independent t-test-based approach separately and six overlapping channels were identified from both channel selection methods. Then, we selected twenty-eight features from selected six channels using LASSO. Using only six channels and twenty-eight features, GP-based classifier achieved an accuracy rate of 97.53% and AUC of 0.999. This is an improvement of 3% over previously developed techniques published in the literature. This study illustrated that LASSO with GP-based system performed outstanding performance in distinguishing children with ADHD from healthy children. This proposed system will be helpful to doctors and physicians in order to detect children with ADHD at an early stage and take the necessary steps for the patients to access appropriate healthcare services, receive effective treatment, and be more conscious of maintaining their lives.

The dynamic hand skeleton data have become increasingly attractive to widely studied for the recognition of hand gestures that contain 3D coordinates of hand joints. Many researchers have been working to develop skeleton-based hand gesture recognition systems using various discriminative spatial-temporal attention features by calculating the dependencies between the joints. However, these methods may face difficulties in achieving high performance and generalizability due to their inefficient features. To overcome these challenges, we proposed a Multi-branch attention-based graph and a general deep-learning model to recognize hand gestures by extracting all possible types of skeleton-based features. We used two graph-based neural network channels in our multi-branch architectures and one general neural network channel. In graph-based neural network channels, one channel first uses the spatial attention module and then the temporal attention module to produce the spatial-temporal features. In contrast, we produced temporal-spatial features in the second channel using the reverse sequence of the first branch. The last branch extracts general deep learning-based features using a general deep neural network module. The final feature vector was constructed by concatenating the spatial-temporal, temporal-spatial, and general features and feeding them into the fully connected layer. We included position embedding and mask operation for both spatial and temporal attention modules to track the node’s sequence and reduce the system’s computational complexity. Our model achieved 94.12%, 92.00%, and 97.01% accuracy after evaluation with MSRA, DHG, and SHREC’17 benchmark datasets, respectively. The high-performance accuracy and low computational cost proved that the proposed method outperformed the existing state-of-the-art methods.

Abstract Immunoglobulin-A-nephropathy (IgAN) is a kidney disease caused by the accumulation of IgAN deposits in the kidneys, which causes inflammation and damage to the kidney tissues. Various bioinformatics analysis-based approaches are widely used to predict novel candidate genes and pathways associated with IgAN. However, there is still some scope to clearly explore the molecular mechanisms and causes of IgAN development and progression. Therefore, the present study aimed to identify key candidate genes for IgAN using machine learning (ML) and statistics-based bioinformatics models. First, differentially expressed genes (DEGs) were identified using limma, and then enrichment analysis was performed on DEGs using DAVID. Protein-protein interaction (PPI) was constructed using STRING and Cytoscape was used to determine hub genes based on connectivity and hub modules based on MCODE scores and their associated genes from DEGs. Furthermore, ML-based algorithms, namely support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and partial least square discriminant analysis (PLS-DA) were applied to identify the discriminative genes of IgAN from DEGs. Finally, the key candidate genes (FOS, JUN, EGR1, FOSB, and DUSP1) were identified as overlapping genes among the selected hub genes, hub module genes, and discriminative genes from SVM, LASSO, and PLS-DA, respectively which can be used for the diagnosis and treatment of IgAN.

Human activity recognition is one of the most difficult tasks in computer vision. Due to the lack of time information, detecting human activities from still photos is more difficult than sensor-based or video-based techniques. Recently, various deep learning based solutions are being proposed one after another, and their performance is constantly improving. In this paper, we proposed a convolutional neural architecture by ensembling transfer learning based multi-channel attention networks. Here, four CNN branches were used to make feature fusion based ensembling and in each branch, an attention module was used to extract the contextual information from the feature map produced by existing pre-trained models. Finally, the extracted feature maps from four branches were concatenated and fed to fully connected network to produce the final recognition output. We considered 3 different datasets, Stanford 40 actions, BU-101 and Willow human actions datasets to evaluate our system. Experimental analysis showed that the proposed ensembled convolutional architecture outperformed previous works by a noteworthy margin.

Gait recognition with wearable sensors is an effective approach to identifying people by recognizing their distinctive walking patterns. Deep learning-based networks have recently emerged as a promising technique in gait recognition, yielding better performance than template matching and traditional machine learning methods. However, most recent studies have focused on improving gait detection accuracy while neglecting model complexity in the deep learning domain, making them unsuitable for low-power wearable devices. Therefore, inference from these models results in latency due to calculation overhead. This study proposes an efficient network suitable for wearable devices without sacrificing prediction performance. We have modified the residual block and accumulated it in shallow convolutional neural networks with five weighted layers only for gait recognition and proved the efficacy of all the architectural components with extensive experiments over publicly available IMU-based datasets: whuGait and OU-ISIR. Our proposed model outperforms all the state-of-the-art methods regarding recognition accuracy and is more than 85 percent efficient on average in terms of model parameters and memory consumption.

Abstract Identification of post-translational modifications (PTM) is significant in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Though there are several computational tools to identify individual PTMs, only three predictors have been established to predict multiple PTMs at the same lysine residue. Furthermore, detailed analysis and assessment on dataset balancing and the significance of different feature encoding techniques for a suitable multi-PTM prediction model are still lacking. This study introduces a computational method named ’iMul-kSite’ for predicting acetylation, crotonylation, methylation, succinylation, and glutarylation, from an unrecognized peptide sample with one, multiple, or no modifications. After successfully eliminating the redundant data samples from the majority class by analyzing the hardness of the sequence-coupling information, feature representation has been optimized by adopting the combination of ANOVA F-Test and incremental feature selection approach. The proposed predictor predicts multi-label PTM sites with 92.83% accuracy using the top 100 features. It has also achieved a 93.36% aiming rate and 96.23% coverage rate, which are much better than the existing state-of-the-art predictors on the validation test. This performance indicates that ’iMul-kSite’ can be used as a supportive tool for further K-PTM study. For the convenience of the experimental scientists, ’iMul-kSite’ has been deployed as a user-friendly web-server at http://103.99.176.239/iMul-kSite .

Sign language recognition (SLR) is one of the crucial applications of the hand gesture recognition and computer vision research domain. There are many researchers who have been working to develop a hand gesture-based SLR application for English, Turkey, Arabic, and other sign languages. However, few studies have been conducted on Korean sign language classification because few KSL datasets are publicly available. In addition, the existing Korean sign language recognition work still faces challenges in being conducted efficiently because light illumination and background complexity are the major problems in this field. In the last decade, researchers successfully applied a vision-based transformer for recognizing sign language by extracting long-range dependency within the image. Moreover, there is a significant gap between the CNN and transformer in terms of the performance and efficiency of the model. In addition, we have not found a combination of CNN and transformer-based Korean sign language recognition models yet. To overcome the challenges, we proposed a convolution and transformer-based multi-branch network aiming to take advantage of the long-range dependencies computation of the transformer and local feature calculation of the CNN for sign language recognition. We extracted initial features with the grained model and then parallelly extracted features from the transformer and CNN. After concatenating the local and long-range dependencies features, a new classification module was applied for the classification. We evaluated the proposed model with a KSL benchmark dataset and our lab dataset, where our model achieved 89.00% accuracy for 77 label KSL dataset and 98.30% accuracy for the lab dataset. The higher performance proves that the proposed model can achieve a generalized property with considerably less computational cost.

Metabolomic biomarkers play a vital role in the early identification and prediction of cancer. It is possible to save numerous lives if biomarkers are used to assist medical providers in diagnosing their patients faster. Many researchers have been trying to identify the crucial biomarkers in the early diagnosis of diseases. This paper presents several steps divided into two phases for determining the most important metabolomic biomarkers in the blood for lung cancer prediction using Plasma and Serum samples. We used the Shapiro–Wilk Test, Bartlett’s Test, Levene’s Test, Student’s t-Test, and Kruskal–Wallis Test in the first phase to determine the potential biomarkers. Recursive Feature Elimination with Random Forest was used to identify the final most dominant metabolomic biomarker at the second phase. Lastly, we ended with Ridge Classifier and XGBoost Classifier to assess the consistency of our approaches. Despite the declining number of metabolites up to a greater level, our prediction accuracy was 100% and 90.91% for Plasma and Serum samples, respectively which is higher than the state-of-the-art method. Finally, we made some analysis using the most dominant metabolites that can serve as a source of inspiration for our work.

The definition of human-computer interaction (HCI) has changed in the current year because people are interested in their various ergonomic devices ways. Many researchers have been working to develop a hand gesture recognition system with a kinetic sensor-based dataset, but their performance accuracy is not satisfactory. In our work, we proposed a multistage spatial attention-based neural network for hand gesture recognition to overcome the challenges. We included three stages in the proposed model where each stage is inherited the CNN; where we first apply a feature extractor and a spatial attention module by using self-attention from the original dataset and then multiply the feature vector with the attention map to highlight effective features of the dataset. Then, we explored features concatenated with the original dataset for obtaining modality feature embedding. In the same way, we generated a feature vector and attention map in the second stage with the feature extraction architecture and self-attention technique. After multiplying the attention map and features, we produced the final feature, which feeds into the third stage, a classification module to predict the label of the correspondent hand gesture. Our model achieved 99.67%, 99.75%, and 99.46% accuracy for the senz3D, Kinematic, and NTU datasets.

The name of individuals has a specific meaning and great significance. Individuals’ names generally have substantial gender differences, and explicitly, Bengali names usually have a solid sexual identity. We can determine if a stranger is a man or a woman based on their name with remarkably suitable precision. In this research, we primarily conducted a thorough investigation into gender prediction based on a person’s name using DL-based methods. While various techniques have been explored for the English language, there has been little progress in the Bengali language. We address this gap by presenting a large-scale experiment with 2030 Bangladeshi unique names. We used both convolutional neural network (CNN)- and recurrent neural network (RNN)-based deep learning methods to infer gender from the Bangladeshi names in the Bengali language. We presented the one-dimensional CNN (Conv1D), simple long short-term memory (LSTM), bidirectional LSTM, stacked LSTM, and combined Conv1D and stacked bidirectional LSTM-based models and evaluated the performance of each scheme using our own dataset. Experimental results are analyzed on the basis of accuracy, precision, recall, F1-score, ROC AUC score, and loss performance metrics. The performance evaluative results show that Conv1D outperforms with 91.18% accuracy, which is likely to improve as the size of the training data grows.

Writer identification has become a hot research topic in the fields of pattern recognition, forensic document analysis, the criminal justice system, etc. The goal of this research is to propose an efficient approach for writer identification based on online handwritten Kanji characters. We collected 47,520 samples from 33 people who wrote 72 online handwritten-based Kanji characters 20 times. We extracted features from the handwriting data and proposed a support vector machine (SVM)-based classifier for writer identification. We also conducted experiments to see how the accuracy changes with feature selection and parameter tuning. Both text-dependent and text-independent writer identification were studied in this work. In the case of text-dependent writer identification, we obtained the accuracy of each Kanji character separately. We then studied the text-independent case by considering some of the top discriminative characters from the text-dependent case. Finally, another text-dependent experiment was performed by taking two, three, and four Kanji characters instead of using only one character. The experimental results illustrated that SVM provided the highest identification accuracy of 99.0% for the text-independent case and 99.6% for text-dependent writer identification. We hope that this study will be helpful for writer identification using online handwritten Kanji characters.

Fitness is important in people’s lives. Good fitness habits can improve cardiopulmonary capacity, increase concentration, prevent obesity, and effectively reduce the risk of death. Home fitness does not require large equipment but uses dumbbells, yoga mats, and horizontal bars to complete fitness exercises and can effectively avoid contact with people, so it is deeply loved by people. People who work out at home use social media to obtain fitness knowledge, but learning ability is limited. Incomplete fitness is likely to lead to injury, and a cheap, timely, and accurate fitness detection system can reduce the risk of fitness injuries and can effectively improve people’s fitness awareness. In the past, many studies have engaged in the detection of fitness movements, among which the detection of fitness movements based on wearable devices, body nodes, and image deep learning has achieved better performance. However, a wearable device cannot detect a variety of fitness movements, may hinder the exercise of the fitness user, and has a high cost. Both body-node-based and image-deep-learning-based methods have lower costs, but each has some drawbacks. Therefore, this paper used a method based on deep transfer learning to establish a fitness database. After that, a deep neural network was trained to detect the type and completeness of fitness movements. We used Yolov4 and Mediapipe to instantly detect fitness movements and stored the 1D fitness signal of movement to build a database. Finally, MLP was used to classify the 1D signal waveform of fitness. In the performance of the classification of fitness movement types, the mAP was 99.71%, accuracy was 98.56%, precision was 97.9%, recall was 98.56%, and the F1-score was 98.23%, which is quite a high performance. In the performance of fitness movement completeness classification, accuracy was 92.84%, precision was 92.85, recall was 92.84%, and the F1-score was 92.83%. The average FPS in detection was 17.5. Experimental results show that our method achieves higher accuracy compared to other methods.

The classification of different age groups, such as adult and child, based on handwriting is very important due to its various applications in many different fields. In forensics, handwriting classification helps investigators focus on a certain category of writers. This paper aimed to propose a machine-learning (ML)-based approach for automatically classifying people as adults or children based on their handwritten data. This study utilized two types of handwritten databases: handwritten text and handwritten pattern, which were collected using a pen tablet. The handwritten text database had 57 subjects (adult: 26 vs. child: 31). Each subject (adult or child) wrote the same 30 words using Japanese hiragana characters. The handwritten pattern database had 81 subjects (adult: 42 and child: 39). Each subject (adult or child) drew four different lines as zigzag lines (trace condition and predict condition), and periodic lines (trace condition and predict condition) and repeated these line tasks three times. Handwriting classification of adult and child is performed in three steps: (i) feature extraction; (ii) feature selection; and (iii) classification. We extracted 30 features from both handwritten text and handwritten pattern datasets. The most efficient features were selected using sequential forward floating selection (SFFS) method and the optimal parameters were selected. Then two ML-based approaches, namely, support vector machine (SVM) and random forest (RF) were applied to classify adult and child. Our findings showed that RF produced up to 93.5% accuracy for handwritten text and 89.8% accuracy for handwritten pattern databases. We hope that this study will provide the evidence of the possibility of classifying adult and child based on handwriting text and handwriting pattern data.

Attention deficit hyperactivity disorder (ADHD) is one of childhood’s most frequent neurobehavioral disorders. The purpose of this study is to: (i) extract the most prominent risk factors for children with ADHD; and (ii) propose a machine learning (ML)-based approach to classify children as either having ADHD or healthy. We extracted the data of 45,779 children aged 3–17 years from the 2018–2019 National Survey of Children’s Health (NSCH, 2018–2019). About 5218 (11.4%) of children were ADHD, and the rest of the children were healthy. Since the class label is highly imbalanced, we adopted a combination of oversampling and undersampling approaches to make a balanced class label. We adopted logistic regression (LR) to extract the significant factors for children with ADHD based on p-values (

The act of writing letters or words in free space with body movements is known as air-writing. Air-writing recognition is a special case of gesture recognition in which gestures correspond to characters and digits written in the air. Air-writing, unlike general gestures, does not require the memorization of predefined special gesture patterns. Rather, it is sensitive to the subject and language of interest. Traditional air-writing requires an extra device containing sensor(s), while the wide adoption of smart-bands eliminates the requirement of the extra device. Therefore, air-writing recognition systems are becoming more flexible day by day. However, the variability of signal duration is a key problem in developing an air-writing recognition model. Inconsistent signal duration is obvious due to the nature of the writing and data-recording process. To make the signals consistent in length, researchers attempted various strategies including padding and truncating, but these procedures result in significant data loss. Interpolation is a statistical technique that can be employed for time-series signals to ensure minimum data loss. In this paper, we extensively investigated different interpolation techniques on seven publicly available air-writing datasets and developed a method to recognize air-written characters using a 2D-CNN model. In both user-dependent and user-independent principles, our method outperformed all the state-of-the-art methods by a clear margin for all datasets.

Sign language is designed to assist the deaf and hard of hearing community to convey messages and connect with society. Sign language recognition has been an important domain of research for a long time. Previously, sensor-based approaches have obtained higher accuracy than vision-based approaches. Due to the cost-effectiveness of vision-based approaches, researchers have been conducted here also despite the accuracy drop. The purpose of this research is to recognize American sign characters using hand images obtained from a web camera. In this work, the media-pipe hands algorithm was used for estimating hand joints from RGB images of hands obtained from a web camera and two types of features were generated from the estimated coordinates of the joints obtained for classification: one is the distances between the joint points and the other one is the angles between vectors and 3D axes. The classifiers utilized to classify the characters were support vector machine (SVM) and light gradient boosting machine (GBM). Three character datasets were used for recognition: the ASL Alphabet dataset, the Massey dataset, and the finger spelling A dataset. The results obtained were 99.39% for the Massey dataset, 87.60% for the ASL Alphabet dataset, and 98.45% for Finger Spelling A dataset. The proposed design for automatic American sign language recognition is cost-effective, computationally inexpensive, does not require any special sensors or devices, and has outperformed previous studies.

As it is the seventh most-spoken language and fifth most-spoken native language in the world, the domain of Bengali handwritten character recognition has fascinated researchers for decades. Although other popular languages i.e., English, Chinese, Hindi, Spanish, etc. have received many contributions in the area of handwritten character recognition, Bengali has not received many noteworthy contributions in this domain because of the complex curvatures and similar writing fashions of Bengali characters. Previously, studies were conducted by using different approaches based on traditional learning, and deep learning. In this research, we proposed a low-cost novel convolutional neural network architecture for the recognition of Bengali characters with only 2.24 to 2.43 million parameters based on the number of output classes. We considered 8 different formations of CMATERdb datasets based on previous studies for the training phase. With experimental analysis, we showed that our proposed system outperformed previous works by a noteworthy margin for all 8 datasets. Moreover, we tested our trained models on other available Bengali characters datasets such as Ekush, BanglaLekha, and NumtaDB datasets. Our proposed architecture achieved 96–99% overall accuracies for these datasets as well. We believe our contributions will be beneficial for developing an automated high-performance recognition tool for Bengali handwritten characters.

Post Translational Modification (PTM) is defined as the alteration of protein sequence upon interaction with different macromolecules after the translation process. Glutarylation is considered one of the most important PTMs, which is associated with a wide range of cellular functioning, including metabolism, translation, and specified separate subcellular localizations. During the past few years, a wide range of computational approaches has been proposed to predict Glutarylation sites. However, despite all the efforts that have been made so far, the prediction performance of the Glutarylation sites has remained limited. One of the main challenges to tackle this problem is to extract features with significant discriminatory information. To address this issue, we propose a new machine learning method called BiPepGlut using the concept of a bi-peptide-based evolutionary method for feature extraction. To build this model, we also use the Extra-Trees (ET) classifier for the classification purpose, which, to the best of our knowledge, has never been used for this task. Our results demonstrate BiPepGlut is able to significantly outperform previously proposed models to tackle this problem. BiPepGlut achieves 92.0%, 84.8%, 95.6%, 0.82, and 0.88 in accuracy, sensitivity, specificity, Matthew’s Correlation Coefficient, and F1-score, respectively. BiPepGlut is implemented as a publicly available online predictor.